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Pandey SK, Rathore YK, Ojha MK, Janghel RR, Sinha A, Kumar A. BCCHI-HCNN: Breast Cancer Classification from Histopathological Images Using Hybrid Deep CNN Models. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2025; 38:1690-1703. [PMID: 39402357 DOI: 10.1007/s10278-024-01297-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Revised: 09/30/2024] [Accepted: 10/04/2024] [Indexed: 05/22/2025]
Abstract
Breast cancer is the most common cancer in women globally, imposing a significant burden on global public health due to high death rates. Data from the World Health Organization show an alarming annual incidence of nearly 2.3 million new cases, drawing the attention of patients, healthcare professionals, and governments alike. Through the examination of histopathological pictures, this study aims to revolutionize the early and precise identification of breast cancer by utilizing the capabilities of a deep convolutional neural network (CNN)-based model. The model's performance is improved by including numerous classifiers, including support vector machine (SVM), decision tree, and K-nearest neighbors (KNN), using transfer learning techniques. The studies include evaluating two separate feature vectors, one with and one without principal component analysis (PCA). Extensive comparisons are made to measure the model's performance against current deep learning models, including critical metrics such as false positive rate, true positive rate, accuracy, precision, and recall. The data show that the SVM algorithm with PCA features achieves excellent speed and accuracy, with an amazing accuracy of 99.5%. Furthermore, although being somewhat slower than SVM, the decision tree model has the greatest accuracy of 99.4% without PCA. This study suggests a viable strategy for improving early breast cancer diagnosis, opening the path for more effective healthcare treatments and better patient outcomes.
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Affiliation(s)
- Saroj Kumar Pandey
- Department of Computer Engineering & Applications, GLA University, Mathura, India.
| | - Yogesh Kumar Rathore
- Department of Computer Science & Engineering, Shri Shankaracharya Institute of Professional Management and Technology, Raipur, India
| | | | - Rekh Ram Janghel
- Department of Information Technology, National Institute of Technology, Raipur, India
| | - Anurag Sinha
- ICFAI Tech School, Computer Science Department, ICFAI University, Ranchi, Jharkhand, India
| | - Ankit Kumar
- Department of Information Technology, GGV, Bilaspur, CG, India
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2
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Huang YNK, Kleespies MW, Shiang-Yao L. Perspectives on Artificial Intelligence in Precision and Genomic Medicine Among Biology Students in Taiwan and Germany. BIOCHEMISTRY AND MOLECULAR BIOLOGY EDUCATION : A BIMONTHLY PUBLICATION OF THE INTERNATIONAL UNION OF BIOCHEMISTRY AND MOLECULAR BIOLOGY 2025. [PMID: 40344350 DOI: 10.1002/bmb.21905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2024] [Revised: 03/20/2025] [Accepted: 04/24/2025] [Indexed: 05/11/2025]
Abstract
Artificial intelligence (AI) in biomedicine has gained significant attention, and its fusion with biology offers exciting possibilities. Understanding students' perspectives on AI is crucial for developing appropriate lessons. This study surveyed biology undergraduates and postgraduates in Taiwan (n = 71) and Germany (n = 51) to explore their perspectives on AI in precision medicine and life sciences and its integration into their education. Exploratory Factor Analysis identified dimensions such as perception of benefits, risks, ethics, acceptance, and willingness to learn AI. The findings revealed that about 70% of students were aware of AI discussions in the field, but 35% admitted lacking basic knowledge of the technologies. Notably, there was a positive correlation between perceiving benefits and the willingness to learn AI in both countries. Interestingly, Taiwanese students expressed more concerns about AI risks than German students but showed greater acceptance and willingness to learn AI. Additionally, a negative correlation between risk perception and willingness to learn AI was found among German students but not among Taiwanese students. This difference may relate to variations in AI education between the countries. Given the high willingness to incorporate AI into biology curricula, the field of biology should lead in educating students about these technologies.
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Affiliation(s)
- Yi-Ning Kelly Huang
- Graduate Institute of Science Education National Taiwan Normal University, Taipei, Taiwan
| | | | - Liu Shiang-Yao
- Graduate Institute of Science Education National Taiwan Normal University, Taipei, Taiwan
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3
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Li W, Liu X. Anxiety about artificial intelligence from patient and doctor-physician. PATIENT EDUCATION AND COUNSELING 2025; 133:108619. [PMID: 39721348 DOI: 10.1016/j.pec.2024.108619] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2024] [Revised: 12/09/2024] [Accepted: 12/16/2024] [Indexed: 12/28/2024]
Abstract
OBJECTIVE This paper investigates the anxiety surrounding the integration of artificial intelligence (AI) in doctor-patient interactions, analyzing the perspectives of both patients and healthcare providers to identify key concerns and potential solutions. METHODS The study employs a comprehensive literature review, examining existing research on AI in healthcare, and synthesizes findings from various surveys and studies that explore the attitudes of patients and doctors towards AI applications in medical settings. RESULTS The analysis reveals that patient anxiety encompasses algorithm aversion, robophobia, lack of humanistic care, challenges in human-machine interaction, and concerns about AI's universal applicability. Doctors' anxieties stem from fears of replacement, legal liabilities, emotional impacts of work environment changes, and technological apprehension. The paper highlights the need for patient participation, humanistic care, improved interaction methods, educational training, and policy guidelines to foster public understanding and trust in AI. CONCLUSION The paper concludes that addressing AI anxiety in doctor-patient relationships is crucial for successfully integrating AI in healthcare. It emphasizes the importance of respecting patient autonomy, addressing the lack of humanistic care, and improving patient-AI interaction to enhance the patient experience and reduce medical errors. PRACTICE IMPLICATIONS The study suggests that future research should focus on understanding the needs and concerns of patients and doctors, strengthening medical humanities education, and establishing policies to guide the ethical use of AI in medicine. It also recommends public education to enhance understanding and trust in AI to improve medical services and ensure professional development and stable work environment for doctors.
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Affiliation(s)
- Wenyu Li
- School of Marxism, Capital Normal University, Beijing, China.
| | - Xueen Liu
- Beijing Hepingli Hospital, Beijing, China
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4
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Wei B, Zhang X, Shao Y, Sun X, Chen L. Comparison of the accuracy of GPT-4 and resident physicians in differentiating benign and malignant thyroid nodules. Front Artif Intell 2025; 8:1512438. [PMID: 40110176 PMCID: PMC11919898 DOI: 10.3389/frai.2025.1512438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2024] [Accepted: 02/10/2025] [Indexed: 03/22/2025] Open
Abstract
Objective To assess the diagnostic performance of the GPT-4 model in comparison to resident physicians in distinguishing between benign and malignant thyroid nodules using ultrasound images. Methods This study analyzed 1,145 ultrasound images, including 632 malignant and 513 benign nodules. Both the GPT-4 model and two resident physicians independently classified the nodules using ultrasound images. The diagnostic accuracy of the resident physicians was determined by calculating the average of the individual accuracy rates of the two physicians and this was compared with the performance of the GPT-4 model. Results The GPT-4 model correctly identified 367 out of 632 malignant nodules (58.07%) and 343 out of 513 benign nodules (66.86%). Resident physicians identified 467 malignant (73.89%) and 383 benign nodules (74.66%). There was a statistically significant difference in the classification of malignant nodules (p < 0.001) and benign nodules (p = 0.048) between the GPT-4 model and residents. GPT-4 performed better for larger nodules (>1 cm) at 65.38%, compared to 53.77% for smaller nodules (≤1 cm, p = 0.004). The AUC for GPT-4 was 0.67, while residents achieved 0.75. Conclusion The GPT-4 model shows potential in classifying thyroid nodules, but its diagnostic accuracy remains significantly lower than that of resident physicians, particularly for smaller malignant nodules.
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Affiliation(s)
- Boxiong Wei
- Department of Ultrasound, Peking University First Hospital, Beijing, China
| | - Xiumei Zhang
- Department of Ultrasound, Peking University First Hospital, Beijing, China
| | - Yuhong Shao
- Department of Ultrasound, Peking University First Hospital, Beijing, China
| | - Xiuming Sun
- Department of Ultrasound, Peking University First Hospital, Beijing, China
| | - Luzeng Chen
- Department of Ultrasound, Peking University First Hospital, Beijing, China
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García-Barberán V, Gómez Del Pulgar ME, Guamán HM, Benito-Martin A. The times they are AI-changing: AI-powered advances in the application of extracellular vesicles to liquid biopsy in breast cancer. EXTRACELLULAR VESICLES AND CIRCULATING NUCLEIC ACIDS 2025; 6:128-140. [PMID: 40206803 PMCID: PMC11977355 DOI: 10.20517/evcna.2024.51] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/18/2024] [Revised: 01/03/2025] [Accepted: 01/25/2025] [Indexed: 04/11/2025]
Abstract
Artificial intelligence (AI) is revolutionizing scientific research by facilitating a paradigm shift in data analysis and discovery. This transformation is characterized by a fundamental change in scientific methods and concepts due to AI's ability to process vast datasets with unprecedented speed and accuracy. In breast cancer research, AI aids in early detection, prognosis, and personalized treatment strategies. Liquid biopsy, a noninvasive tool for detecting circulating tumor traits, could ideally benefit from AI's analytical capabilities, enhancing the detection of minimal residual disease and improving treatment monitoring. Extracellular vesicles (EVs), which are key elements in cell communication and cancer progression, could be analyzed with AI to identify disease-specific biomarkers. AI combined with EV analysis promises an enhancement in diagnosis precision, aiding in early detection and treatment monitoring. Studies show that AI can differentiate cancer types and predict drug efficacy, exemplifying its potential in personalized medicine. Overall, the integration of AI in biomedical research and clinical practice promises significant changes and advancements in diagnostics, personalized medicine-based approaches, and our understanding of complex diseases like cancer.
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Affiliation(s)
- Vanesa García-Barberán
- Molecular Oncology Laboratory, Medical Oncology Department, Hospital Clínico Universitario San Carlos, Instituto de Investigación Sanitaria San Carlos (IdISSC), Madrid 28040, Spain
| | - María Elena Gómez Del Pulgar
- Molecular Oncology Laboratory, Medical Oncology Department, Hospital Clínico Universitario San Carlos, Instituto de Investigación Sanitaria San Carlos (IdISSC), Madrid 28040, Spain
| | - Heidy M. Guamán
- Molecular Oncology Laboratory, Medical Oncology Department, Hospital Clínico Universitario San Carlos, Instituto de Investigación Sanitaria San Carlos (IdISSC), Madrid 28040, Spain
| | - Alberto Benito-Martin
- Molecular Oncology Laboratory, Medical Oncology Department, Hospital Clínico Universitario San Carlos, Instituto de Investigación Sanitaria San Carlos (IdISSC), Madrid 28040, Spain
- Facultad de Medicina, Universidad Alfonso X el Sabio, Madrid 28691, Spain
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Wahab A, Suhail M, Eggers T, Shehzad K, Akakuru OU, Ahmad Z, Sun Z, Iqbal MZ, Kong X. Innovative perspectives on metal free contrast agents for MRI: Enhancing imaging efficacy, and AI-driven future diagnostics. Acta Biomater 2025; 193:83-106. [PMID: 39793747 DOI: 10.1016/j.actbio.2025.01.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2024] [Revised: 12/13/2024] [Accepted: 01/07/2025] [Indexed: 01/13/2025]
Abstract
The U.S. Food and Drug Administration (FDA) has issued a boxed warning and mandated additional safety measures for all gadolinium-based contrast agents (GBCAs) used in clinical magnetic resonance imaging (MRI) due to their prolonged retention in the body and associated adverse health effects. This review explores recent advancements in CAs for MRI, highlighting four innovative probes: ORCAs, CEST CAs, 19F CAs, and HP 13C MRI. ORCAs offer a metal-free alternative that enhances imaging through nitroxides. CEST MRI facilitates the direct detection of specific molecules via proton exchange, aiding in disease diagnosis and metabolic assessment. 19F MRI CAs identify subtle biological changes, enabling earlier detection and tailored treatment approaches. HP 13C MRI improves visualization of metabolic processes, demonstrating potential in cancer diagnosis and monitoring. Finally, this review concludes by addressing the challenges facing the field and outlining future research directions, with a particular focus on leveraging artificial intelligence to enhance diagnostic capabilities and optimize both the performance and safety profiles of these innovative CAs. STATEMENT OF SIGNIFICANCE: The review addresses the urgent need for safer MRI contrast agents in light of FDA warnings about GBCAs. It highlights the key factors influencing the stability and functionality of metal-free CAs and recent advancements in designing ORCAs, CEST CAs, 19F CAs, and HP 13C probes and functionalization that enhance MRI contrast. It also explores the potential of these agents for multimodal imaging and targeted diagnostics while outlining future research directions and the integration of artificial intelligence to optimize their clinical application and safety. This contribution is pivotal for driving innovation in MRI technology and improving patient outcomes in disease detection and monitoring.
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Affiliation(s)
- Abdul Wahab
- Institute of Smart Biomedical Materials, School of Materials Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, PR China
| | - Muhammad Suhail
- Institute of Smart Biomedical Materials, School of Materials Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, PR China
| | - Tatiana Eggers
- Department of Physics, University of South Florida, Tampa, FL 33620, USA
| | - Khurram Shehzad
- Institute of Physics, Silesian University of Technology, Konarskiego 22B, Gliwice 44-100, Poland
| | - Ozioma Udochukwu Akakuru
- Department of Chemical and Petroleum Engineering, Schulich School of Engineering, University of Calgary, Alberta, Canada
| | - Zahoor Ahmad
- Institute of Smart Biomedical Materials, School of Materials Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, PR China
| | - Zhichao Sun
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou 310006, China
| | - M Zubair Iqbal
- Institute of Smart Biomedical Materials, School of Materials Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, PR China.
| | - Xiangdong Kong
- Institute of Smart Biomedical Materials, School of Materials Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, PR China.
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Uwimana A, Gnecco G, Riccaboni M. Artificial intelligence for breast cancer detection and its health technology assessment: A scoping review. Comput Biol Med 2025; 184:109391. [PMID: 39579663 DOI: 10.1016/j.compbiomed.2024.109391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2024] [Revised: 10/01/2024] [Accepted: 11/07/2024] [Indexed: 11/25/2024]
Abstract
BACKGROUND Recent healthcare advancements highlight the potential of Artificial Intelligence (AI) - and especially, among its subfields, Machine Learning (ML) - in enhancing Breast Cancer (BC) clinical care, leading to improved patient outcomes and increased radiologists' efficiency. While medical imaging techniques have significantly contributed to BC detection and diagnosis, their synergy with AI algorithms has consistently demonstrated superior diagnostic accuracy, reduced False Positives (FPs), and enabled personalized treatment strategies. Despite the burgeoning enthusiasm for leveraging AI for early and effective BC clinical care, its widespread integration into clinical practice is yet to be realized, and the evaluation of AI-based health technologies in terms of health and economic outcomes remains an ongoing endeavor. OBJECTIVES This scoping review aims to investigate AI (and especially ML) applications that have been implemented and evaluated across diverse clinical tasks or decisions in breast imaging and to explore the current state of evidence concerning the assessment of AI-based technologies for BC clinical care within the context of Health Technology Assessment (HTA). METHODS We conducted a systematic literature search following the Preferred Reporting Items for Systematic review and Meta-Analysis Protocols (PRISMA-P) checklist in PubMed and Scopus to identify relevant studies on AI (and particularly ML) applications in BC detection and diagnosis. We limited our search to studies published from January 2015 to October 2023. The Minimum Information about CLinical Artificial Intelligence Modeling (MI-CLAIM) checklist was used to assess the quality of AI algorithms development, evaluation, and reporting quality in the reviewed articles. The HTA Core Model® was also used to analyze the comprehensiveness, robustness, and reliability of the reported results and evidence in AI-systems' evaluations to ensure rigorous assessment of AI systems' utility and cost-effectiveness in clinical practice. RESULTS Of the 1652 initially identified articles, 104 were deemed eligible for inclusion in the review. Most studies examined the clinical effectiveness of AI-based systems (78.84%, n= 82), with one study focusing on safety in clinical settings, and 13.46% (n=14) focusing on patients' benefits. Of the studies, 31.73% (n=33) were ethically approved to be carried out in clinical practice, whereas 25% (n=26) evaluated AI systems legally approved for clinical use. Notably, none of the studies addressed the organizational implications of AI systems in clinical practice. Of the 104 studies, only two of them focused on cost-effectiveness analysis, and were analyzed separately. The average percentage scores for the first 102 AI-based studies' quality assessment based on the MI-CLAIM checklist criteria were 84.12%, 83.92%, 83.98%, 74.51%, and 14.7% for study design, data and optimization, model performance, model examination, and reproducibility, respectively. Notably, 20.59% (n=21) of these studies relied on large-scale representative real-world breast screening datasets, with only 10.78% (n =11) studies demonstrating the robustness and generalizability of the evaluated AI systems. CONCLUSION In bridging the gap between cutting-edge developments and seamless integration of AI systems into clinical workflows, persistent challenges encompass data quality and availability, ethical and legal considerations, robustness and trustworthiness, scalability, and alignment with existing radiologists' workflow. These hurdles impede the synthesis of comprehensive, robust, and reliable evidence to substantiate these systems' clinical utility, relevance, and cost-effectiveness in real-world clinical workflows. Consequently, evaluating AI-based health technologies through established HTA methodologies becomes complicated. We also highlight potential significant influences on AI systems' effectiveness of various factors, such as operational dynamics, organizational structure, the application context of AI systems, and practices in breast screening or examination reading of AI support tools in radiology. Furthermore, we emphasize substantial reciprocal influences on decision-making processes between AI systems and radiologists. Thus, we advocate for an adapted assessment framework specifically designed to address these potential influences on AI systems' effectiveness, mainly addressing system-level transformative implications for AI systems rather than focusing solely on technical performance and task-level evaluations.
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Affiliation(s)
| | | | - Massimo Riccaboni
- IMT School for Advanced Studies, Lucca, Italy; IUSS University School for Advanced Studies, Pavia, Italy.
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8
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Tripathi S, Sharma Y, Kumar D. Unraveling the Mysteries of Alzheimer's Disease Using Artificial Intelligence. Rev Recent Clin Trials 2025; 20:124-141. [PMID: 39563218 DOI: 10.2174/0115748871330861241030143321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2024] [Revised: 09/13/2024] [Accepted: 09/23/2024] [Indexed: 11/21/2024]
Abstract
Alzheimer's disease (AD) is a multidimensional, complex condition that affects individuals all over the world. Despite decades of experimental and clinical research that has revealed various processes, many concerns concerning the origin of Alzheimer's disease remain unresolved. Despite the notion that there isn't a complete set of jigsaw pieces, the growing number of public data-sharing initiatives that collect biological, clinical, and lifestyle data from those suffering from Alzheimer's disease has resulted in virtually endless volumes of knowledge about the disorder, far beyond what humans can comprehend. Furthermore, combining Big Data from multi- -omics research gives a chance to investigate the pathophysiological processes underlying the whole biological spectrum of Alzheimer's disease. To improve knowledge on the subject of Alzheimer's disease, Artificial Intelligence (AI) offers a wide variety of approaches for evaluating complex and significant data. The introduction of next-generation sequencing and microarray technologies has resulted in significant growth in genetic data research. When it comes to assessing such complex projects, AI technology beats conventional statistical techniques of data processing. This review focuses on current research and potential challenges for AI in Alzheimer's disease research. This article, in particular, examines how AI may assist healthcare practitioners with patient stratification, estimating an individual's chance of AD conversion, and diagnosing AD using computer-aided diagnostic methodologies. Ultimately, scientists want to develop individualized, efficient medicines.
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Affiliation(s)
- Siddhant Tripathi
- Poona College of Pharmacy, Bharati Vidyapeeth (Deemed to be) University, Pune, Maharashtra, 411038, India
| | - Yashika Sharma
- Poona College of Pharmacy, Bharati Vidyapeeth (Deemed to be) University, Pune, Maharashtra, 411038, India
| | - Dileep Kumar
- Department of Pharm Chemistry, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India
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Pesapane F, Hauglid MK, Fumagalli M, Petersson L, Parkar AP, Cassano E, Horgan D. The translation of in-house imaging AI research into a medical device ensuring ethical and regulatory integrity. Eur J Radiol 2025; 182:111852. [PMID: 39612599 DOI: 10.1016/j.ejrad.2024.111852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2024] [Revised: 11/15/2024] [Accepted: 11/25/2024] [Indexed: 12/01/2024]
Abstract
This manuscript delineates the pathway from in-house research on Artificial Intelligence (AI) to the development of a medical device, addressing critical phases including conceptualization, development, validation, and regulatory compliance. Key stages in the transformation process involve identifying clinical needs, data management, model training, and rigorous validation to ensure AI models are both robust and clinically relevant. Continuous post-deployment surveillance is essential to maintain performance and adapt to changes in clinical practice. The regulatory landscape is complex, encompassing stringent certification processes under the EU Medical Device Regulation (MDR) and the upcoming EU AI Act, which imposes additional compliance requirements aimed at mitigating AI-specific risks. Ethical considerations such as, emphasizing transparency, patient privacy, and equitable access to AI technologies, are paramount. The manuscript underscores the importance of interdisciplinary collaboration, between healthcare institutions and industry partners, and navigation of commercialization and market entry of AI devices. This overview provides a strategic framework for radiologists and healthcare leaders to effectively integrate AI into clinical practice, while adhering to regulatory and ethical standards, ultimately enhancing patient care and operational efficiency.
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Affiliation(s)
- Filippo Pesapane
- Breast Imaging Division, Radiology Department, IEO European Institute of Oncology IRCCS, Milan, Italy.
| | | | - Marzia Fumagalli
- Technology Transfer Office, IEO European Institute of Oncology IRCCS, Milan, Italy.
| | - Lena Petersson
- School of Health and Welfare, Halmstad University, Sweden.
| | - Anagha P Parkar
- Department of Radiology, Haraldsplass Deaconess Hospital, Bergen Norway; Department of Clinical Medicine, Faculty of Medicine and Dentistry, University of Bergen, Bergen, Norway.
| | - Enrico Cassano
- Breast Imaging Division, Radiology Department, IEO European Institute of Oncology IRCCS, Milan, Italy.
| | - Denis Horgan
- European Alliance for Personalised Medicine, Brussels, Belgium.
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10
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Zhao Z, Zhang W, Chen X, Song F, Gunasegaram J, Huang W, Shi D, He M, Liu N. Slit Lamp Report Generation and Question Answering: Development and Validation of a Multimodal Transformer Model with Large Language Model Integration. J Med Internet Res 2024; 26:e54047. [PMID: 39753218 PMCID: PMC11729784 DOI: 10.2196/54047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 02/24/2024] [Accepted: 09/05/2024] [Indexed: 01/18/2025] Open
Abstract
BACKGROUND Large language models have shown remarkable efficacy in various medical research and clinical applications. However, their skills in medical image recognition and subsequent report generation or question answering (QA) remain limited. OBJECTIVE We aim to finetune a multimodal, transformer-based model for generating medical reports from slit lamp images and develop a QA system using Llama2. We term this entire process slit lamp-GPT. METHODS Our research used a dataset of 25,051 slit lamp images from 3409 participants, paired with their corresponding physician-created medical reports. We used these data, split into training, validation, and test sets, to finetune the Bootstrapping Language-Image Pre-training framework toward report generation. The generated text reports and human-posed questions were then input into Llama2 for subsequent QA. We evaluated performance using qualitative metrics (including BLEU [bilingual evaluation understudy], CIDEr [consensus-based image description evaluation], ROUGE-L [Recall-Oriented Understudy for Gisting Evaluation-Longest Common Subsequence], SPICE [Semantic Propositional Image Caption Evaluation], accuracy, sensitivity, specificity, precision, and F1-score) and the subjective assessments of two experienced ophthalmologists on a 1-3 scale (1 referring to high quality). RESULTS We identified 50 conditions related to diseases or postoperative complications through keyword matching in initial reports. The refined slit lamp-GPT model demonstrated BLEU scores (1-4) of 0.67, 0.66, 0.65, and 0.65, respectively, with a CIDEr score of 3.24, a ROUGE (Recall-Oriented Understudy for Gisting Evaluation) score of 0.61, and a Semantic Propositional Image Caption Evaluation score of 0.37. The most frequently identified conditions were cataracts (22.95%), age-related cataracts (22.03%), and conjunctival concretion (13.13%). Disease classification metrics demonstrated an overall accuracy of 0.82 and an F1-score of 0.64, with high accuracies (≥0.9) observed for intraocular lens, conjunctivitis, and chronic conjunctivitis, and high F1-scores (≥0.9) observed for cataract and age-related cataract. For both report generation and QA components, the two evaluating ophthalmologists reached substantial agreement, with κ scores between 0.71 and 0.84. In assessing 100 generated reports, they awarded scores of 1.36 for both completeness and correctness; 64% (64/100) were considered "entirely good," and 93% (93/100) were "acceptable." In the evaluation of 300 generated answers to questions, the scores were 1.33 for completeness, 1.14 for correctness, and 1.15 for possible harm, with 66.3% (199/300) rated as "entirely good" and 91.3% (274/300) as "acceptable." CONCLUSIONS This study introduces the slit lamp-GPT model for report generation and subsequent QA, highlighting the potential of large language models to assist ophthalmologists and patients.
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Affiliation(s)
- Ziwei Zhao
- School of Optometry, The Hong Kong Polytechnic University, Hong Kong, China
| | - Weiyi Zhang
- School of Optometry, The Hong Kong Polytechnic University, Hong Kong, China
| | - Xiaolan Chen
- School of Optometry, The Hong Kong Polytechnic University, Hong Kong, China
| | - Fan Song
- School of Optometry, The Hong Kong Polytechnic University, Hong Kong, China
| | | | - Wenyong Huang
- Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Danli Shi
- School of Optometry, The Hong Kong Polytechnic University, Hong Kong, China
- Research Centre for SHARP Vision, The Hong Kong Polytechnic University, Hong Kong, China
| | - Mingguang He
- School of Optometry, The Hong Kong Polytechnic University, Hong Kong, China
- Research Centre for SHARP Vision, The Hong Kong Polytechnic University, Hong Kong, China
- Centre for Eye and Vision Research (CEVR), Hong Kong, China
| | - Na Liu
- Guangzhou Cadre and Talent Health Management Center, Guangzhou, China
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11
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Xu HF, Wang H, Liu Y, Wang XY, Guo XL, Liu HW, Kang RH, Chen Q, Liu SZ, Guo LW, Zheng LY, Qiao YL, Zhang SK. Baseline Performance of Ultrasound-Based Strategies in Breast Cancer Screening Among Chinese Women. Acad Radiol 2024; 31:4772-4779. [PMID: 39174359 DOI: 10.1016/j.acra.2024.07.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2024] [Revised: 06/26/2024] [Accepted: 07/16/2024] [Indexed: 08/24/2024]
Abstract
RATIONALE AND OBJECTIVE There is a notable absence of robust evidence on the efficacy of ultrasound-based breast cancer screening strategies, particularly in populations with a high prevalence of dense breasts. Our study addresses this gap by evaluating the effectiveness of such strategies in Chinese women, thereby enriching the evidence base for identifying the most efficacious screening approaches for women with dense breast tissue. METHODS Conducted from October 2018 to August 2022 in Central China, this prospective cohort study enrolled 8996 women aged 35-64 years, divided into two age groups (35-44 and 45-64 years). Participants were screened for breast cancer using hand-held ultrasound (HHUS) and automated breast ultrasound system (ABUS), with the older age group also receiving full-field digital mammography (FFDM). The Breast Imaging Reporting and Data System (BI-RADS) was employed for image interpretation, with abnormal results indicated by BI-RADS 4/5, necessitating a biopsy; BI-RADS 3 required follow-up within 6-12 months by primary screening strategies; and BI-RADS 1/2 were classified as negative. RESULTS Among the screened women, 29 cases of breast cancer were identified, with 4 (1.3‰) in the 35-44 years age group and 25 (4.2‰) in the 45-64 years age group. In the younger age group, HHUS and ABUS performed equally well, with no significant difference in their AUC values (0.8678 vs. 0.8679, P > 0.05). For the older age group, ABUS as a standalone strategy (AUC 0.9935) and both supplemental screening methods (HHUS with FFDM, AUC 0.9920; ABUS with FFDM, AUC 0.9928) outperformed FFDM alone (AUC 0.8983, P < 0.05). However, there was no significant difference between HHUS alone and FFDM alone (AUC 0.9529 vs. 0.8983, P > 0.05). CONCLUSION The findings indicate that both HHUS and ABUS exhibit strong performance as independent breast cancer screening strategies, with ABUS demonstrating superior potential. However, the integration of FFDM with these ultrasound techniques did not confer a substantial improvement in the overall effectiveness of the screening process.
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Affiliation(s)
- Hui-Fang Xu
- Department of Cancer Epidemiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, Zhengzhou, China (H.F.X., H.W., Y.L., X.Y.W., X.L.G., H.W.L., R.H.K., Q.C., S.Z.L., L.W.G., L.Y.Z., Y.L.Q., S.K.Z.)
| | - Hong Wang
- Department of Cancer Epidemiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, Zhengzhou, China (H.F.X., H.W., Y.L., X.Y.W., X.L.G., H.W.L., R.H.K., Q.C., S.Z.L., L.W.G., L.Y.Z., Y.L.Q., S.K.Z.)
| | - Yin Liu
- Department of Cancer Epidemiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, Zhengzhou, China (H.F.X., H.W., Y.L., X.Y.W., X.L.G., H.W.L., R.H.K., Q.C., S.Z.L., L.W.G., L.Y.Z., Y.L.Q., S.K.Z.)
| | - Xiao-Yang Wang
- Department of Cancer Epidemiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, Zhengzhou, China (H.F.X., H.W., Y.L., X.Y.W., X.L.G., H.W.L., R.H.K., Q.C., S.Z.L., L.W.G., L.Y.Z., Y.L.Q., S.K.Z.)
| | - Xiao-Li Guo
- Department of Cancer Epidemiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, Zhengzhou, China (H.F.X., H.W., Y.L., X.Y.W., X.L.G., H.W.L., R.H.K., Q.C., S.Z.L., L.W.G., L.Y.Z., Y.L.Q., S.K.Z.)
| | - Hong-Wei Liu
- Department of Cancer Epidemiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, Zhengzhou, China (H.F.X., H.W., Y.L., X.Y.W., X.L.G., H.W.L., R.H.K., Q.C., S.Z.L., L.W.G., L.Y.Z., Y.L.Q., S.K.Z.)
| | - Rui-Hua Kang
- Department of Cancer Epidemiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, Zhengzhou, China (H.F.X., H.W., Y.L., X.Y.W., X.L.G., H.W.L., R.H.K., Q.C., S.Z.L., L.W.G., L.Y.Z., Y.L.Q., S.K.Z.)
| | - Qiong Chen
- Department of Cancer Epidemiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, Zhengzhou, China (H.F.X., H.W., Y.L., X.Y.W., X.L.G., H.W.L., R.H.K., Q.C., S.Z.L., L.W.G., L.Y.Z., Y.L.Q., S.K.Z.)
| | - Shu-Zheng Liu
- Department of Cancer Epidemiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, Zhengzhou, China (H.F.X., H.W., Y.L., X.Y.W., X.L.G., H.W.L., R.H.K., Q.C., S.Z.L., L.W.G., L.Y.Z., Y.L.Q., S.K.Z.)
| | - Lan-Wei Guo
- Department of Cancer Epidemiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, Zhengzhou, China (H.F.X., H.W., Y.L., X.Y.W., X.L.G., H.W.L., R.H.K., Q.C., S.Z.L., L.W.G., L.Y.Z., Y.L.Q., S.K.Z.)
| | - Li-Yang Zheng
- Department of Cancer Epidemiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, Zhengzhou, China (H.F.X., H.W., Y.L., X.Y.W., X.L.G., H.W.L., R.H.K., Q.C., S.Z.L., L.W.G., L.Y.Z., Y.L.Q., S.K.Z.)
| | - You-Lin Qiao
- Department of Cancer Epidemiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, Zhengzhou, China (H.F.X., H.W., Y.L., X.Y.W., X.L.G., H.W.L., R.H.K., Q.C., S.Z.L., L.W.G., L.Y.Z., Y.L.Q., S.K.Z.); Center for Global Health, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China (Y.L.Q.)
| | - Shao-Kai Zhang
- Department of Cancer Epidemiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, Zhengzhou, China (H.F.X., H.W., Y.L., X.Y.W., X.L.G., H.W.L., R.H.K., Q.C., S.Z.L., L.W.G., L.Y.Z., Y.L.Q., S.K.Z.).
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Gullo RL, Brunekreef J, Marcus E, Han LK, Eskreis-Winkler S, Thakur SB, Mann R, Lipman KG, Teuwen J, Pinker K. AI Applications to Breast MRI: Today and Tomorrow. J Magn Reson Imaging 2024; 60:2290-2308. [PMID: 38581127 PMCID: PMC11452568 DOI: 10.1002/jmri.29358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 03/07/2024] [Accepted: 03/09/2024] [Indexed: 04/08/2024] Open
Abstract
In breast imaging, there is an unrelenting increase in the demand for breast imaging services, partly explained by continuous expanding imaging indications in breast diagnosis and treatment. As the human workforce providing these services is not growing at the same rate, the implementation of artificial intelligence (AI) in breast imaging has gained significant momentum to maximize workflow efficiency and increase productivity while concurrently improving diagnostic accuracy and patient outcomes. Thus far, the implementation of AI in breast imaging is at the most advanced stage with mammography and digital breast tomosynthesis techniques, followed by ultrasound, whereas the implementation of AI in breast magnetic resonance imaging (MRI) is not moving along as rapidly due to the complexity of MRI examinations and fewer available dataset. Nevertheless, there is persisting interest in AI-enhanced breast MRI applications, even as the use of and indications of breast MRI continue to expand. This review presents an overview of the basic concepts of AI imaging analysis and subsequently reviews the use cases for AI-enhanced MRI interpretation, that is, breast MRI triaging and lesion detection, lesion classification, prediction of treatment response, risk assessment, and image quality. Finally, it provides an outlook on the barriers and facilitators for the adoption of AI in breast MRI. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 6.
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Affiliation(s)
- Roberto Lo Gullo
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Joren Brunekreef
- AI for Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Eric Marcus
- AI for Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Lynn K Han
- Weill Cornell Medical College, New York-Presbyterian Hospital, New York, NY, USA
| | - Sarah Eskreis-Winkler
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Sunitha B Thakur
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Ritse Mann
- AI for Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Kevin Groot Lipman
- AI for Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Jonas Teuwen
- AI for Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Katja Pinker
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
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Uchikov P, Khalid U, Dedaj-Salad GH, Ghale D, Rajadurai H, Kraeva M, Kraev K, Hristov B, Doykov M, Mitova V, Bozhkova M, Markov S, Stanchev P. Artificial Intelligence in Breast Cancer Diagnosis and Treatment: Advances in Imaging, Pathology, and Personalized Care. Life (Basel) 2024; 14:1451. [PMID: 39598249 PMCID: PMC11595975 DOI: 10.3390/life14111451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2024] [Revised: 11/04/2024] [Accepted: 11/06/2024] [Indexed: 11/29/2024] Open
Abstract
Breast cancer is the most prevalent cancer worldwide, affecting both low- and middle-income countries, with a growing number of cases. In 2024, about 310,720 women in the U.S. are projected to receive an invasive breast cancer diagnosis, alongside 56,500 cases of ductal carcinoma in situ (DCIS). Breast cancer occurs in every country of the world in women at any age after puberty but with increasing rates in later life. About 65% of women with the BRCA1 and 45% with the BRCA2 gene variants develop breast cancer by age 70. While these genes account for 5% of breast cancers, their prevalence is higher in certain populations. Advances in early detection, personalised medicine, and AI-driven diagnostics are improving outcomes by enabling a more precise analysis, reducing recurrence, and minimising treatment side effects. Our paper aims to explore the vast applications of artificial intelligence within the diagnosis and treatment of breast cancer and how these advancements can contribute to elevating patient care as well as discussing the potential drawbacks of such integrations into modern medicine. We structured our paper as a non-systematic review and utilised Google Scholar and PubMed databases to review literature regarding the incorporation of AI in the diagnosis and treatment of non-palpable breast masses. AI is revolutionising breast cancer management by enhancing imaging, pathology, and personalised treatment. In imaging, AI can improve the detection of cancer in mammography, MRIs, and ultrasounds, rivalling expert radiologists in accuracy. In pathology, AI enhances biomarker detection, improving HER2 and Ki67 assessments. Personalised medicine benefits from AI's predictive power, aiding risk stratification and treatment response. AI also shows promise in triple-negative breast cancer management, offering better prognosis and subtype classification. However, challenges include data variability, ethical concerns, and real-world validation. Despite limitations, AI integration offers significant potential in improving breast cancer diagnosis, prognosis, and treatment outcomes.
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Affiliation(s)
- Petar Uchikov
- Department of Special Surgery, Faculty of Medicine, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria;
| | - Usman Khalid
- Faculty of Medicine, Medical University of Plovdiv, 4000 Plovdiv, Bulgaria; (U.K.); (G.H.D.-S.); (D.G.); (H.R.)
| | - Granit Harris Dedaj-Salad
- Faculty of Medicine, Medical University of Plovdiv, 4000 Plovdiv, Bulgaria; (U.K.); (G.H.D.-S.); (D.G.); (H.R.)
| | - Dibya Ghale
- Faculty of Medicine, Medical University of Plovdiv, 4000 Plovdiv, Bulgaria; (U.K.); (G.H.D.-S.); (D.G.); (H.R.)
| | - Harney Rajadurai
- Faculty of Medicine, Medical University of Plovdiv, 4000 Plovdiv, Bulgaria; (U.K.); (G.H.D.-S.); (D.G.); (H.R.)
| | - Maria Kraeva
- Department of Otorhinolaryngology, Medical Faculty, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria; (M.K.); (S.M.)
| | - Krasimir Kraev
- Department of Propedeutics of Internal Diseases, Medical Faculty, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria
| | - Bozhidar Hristov
- Second Department of Internal Diseases, Section “Gastroenterology”, Medical Faculty, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria;
| | - Mladen Doykov
- Department of Urology and General Medicine, Medical Faculty, Medical University of Plovdiv, 4001 Plovdiv, Bulgaria;
| | - Vanya Mitova
- University Specialized Hospital for Active Oncology Treatment “Prof. Ivan Chernozemsky”, 1756 Sofia, Bulgaria;
| | - Maria Bozhkova
- Medical College, Medical University of Plovdiv, 4000 Plovdiv, Bulgaria;
| | - Stoyan Markov
- Department of Otorhinolaryngology, Medical Faculty, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria; (M.K.); (S.M.)
| | - Pavel Stanchev
- Clinic of Endocrinology and Metabolic Diseases, St George University Hospital, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria;
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14
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Moran CJ. Ready the treasure at our feet, MRI feature analysis in young women with breast cancer. Eur Radiol 2024; 34:7090-7091. [PMID: 38896233 DOI: 10.1007/s00330-024-10840-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2024] [Revised: 05/21/2024] [Accepted: 05/25/2024] [Indexed: 06/21/2024]
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Ding T, Shi K, Pan Z, Ding C. AI-based automated breast cancer segmentation in ultrasound imaging based on Attention Gated Multi ResU-Net. PeerJ Comput Sci 2024; 10:e2226. [PMID: 39650425 PMCID: PMC11623109 DOI: 10.7717/peerj-cs.2226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Accepted: 07/10/2024] [Indexed: 12/11/2024]
Abstract
Breast cancer is a leading cause of death among women worldwide, making early detection and diagnosis critical for effective treatment and improved patient outcomes. Ultrasound imaging is a common diagnostic tool for breast cancer, but interpreting ultrasound images can be challenging due to the complexity of breast tissue and the variability of image quality. This study proposed an Attention Gated Multi ResU-Net model for medical image segmentation tasks, that has shown promising results for breast cancer ultrasound image segmentation. The model's multi-scale feature extraction and attention-gating mechanism enable it to accurately identify and segment areas of abnormality in the breast tissue, such as masses, cysts, and calcifications. The model's quantitative test showed an adequate degree of agreement with expert manual annotations, demonstrating its potential for improving early identification and diagnosis of breast cancer. The model's multi-scale feature extraction and attention-gating mechanism enable it to accurately identify and segment areas of abnormality in the breast tissue, such as masses, cysts, and calcifications, achieving a Dice coefficient of 0.93, sensitivity of 93%, and specificity of 99%. These results underscore the model's high precision and reliability in medical image analysis.
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Affiliation(s)
- Ting Ding
- School of Earth Science, East China University of Technology, Nanhang, JiangXi, China
- Urumqi Comprehensive Survey Center on Natural Resources, Urumq, XinJiang, China
| | - Kaimai Shi
- School of Physics, Georgia Institution of Technology, Atlanta, GA, USA
| | - Zhaoyan Pan
- School of Energy Power Engineering, Xian Jiaotong University, Xian, China
| | - Cheng Ding
- Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, United States of America
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Patra A, Biswas P, Behera SK, Barpanda NK, Sethy PK, Nanthaamornphong A. Transformative insights: Image-based breast cancer detection and severity assessment through advanced AI techniques. JOURNAL OF INTELLIGENT SYSTEMS 2024; 33. [DOI: 10.1515/jisys-2024-0172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2025] Open
Abstract
Abstract
In the realm of image-based breast cancer detection and severity assessment, this study delves into the revolutionary potential of sophisticated artificial intelligence (AI) techniques. By investigating image processing, machine learning (ML), and deep learning (DL), the research illuminates their combined impact on transforming breast cancer diagnosis. This integration offers insights into early identification and precise characterization of cancers. With a foundation in 125 research articles, this article presents a comprehensive overview of the current state of image-based breast cancer detection. Synthesizing the transformative role of AI, including image processing, ML, and DL, the review explores how these technologies collectively reshape the landscape of breast cancer diagnosis and severity assessment. An essential aspect highlighted is the synergy between advanced image processing methods and ML algorithms. This combination facilitates the automated examination of medical images, which is crucial for detecting minute anomalies indicative of breast cancer. The utilization of complex neural networks for feature extraction and pattern recognition in DL models further enhances diagnostic precision. Beyond diagnostic improvements, the abstract underscores the substantial influence of AI-driven methods on breast cancer treatment. The integration of AI not only increases diagnostic precision but also opens avenues for individualized treatment planning, marking a paradigm shift toward personalized medicine in breast cancer care. However, challenges persist, with issues related to data quality and interpretability requiring continued research efforts. Looking forward, the abstract envisions future directions for breast cancer identification and diagnosis, emphasizing the adoption of explainable AI techniques and global collaboration for data sharing. These initiatives promise to propel the field into a new era characterized by enhanced efficiency and precision in breast cancer care.
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Affiliation(s)
- Ankita Patra
- Department of Electronics, Sambalpur University , Burla , Odisha, 768019 , India
| | - Preesat Biswas
- Department of Electronics and Telecommunication Engineering, GEC Jagdalpur , C.G., 494001 , India
| | - Santi Kumari Behera
- Department of Computer Science and Engineering, VSSUT , Burla , Odisha, 768018 , India
| | | | - Prabira Kumar Sethy
- Department of Electronics, Sambalpur University , Burla , Odisha, 768019 , India
| | - Aziz Nanthaamornphong
- College of Computing, Prince of Songkla University, Phuket Campus , Phuket 83120 , Thailand
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Joshi RC, Srivastava P, Mishra R, Burget R, Dutta MK. Biomarker profiling and integrating heterogeneous models for enhanced multi-grade breast cancer prognostication. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 255:108349. [PMID: 39096573 DOI: 10.1016/j.cmpb.2024.108349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Revised: 07/01/2024] [Accepted: 07/22/2024] [Indexed: 08/05/2024]
Abstract
BACKGROUND Breast cancer remains a leading cause of female mortality worldwide, exacerbated by limited awareness, inadequate screening resources, and treatment options. Accurate and early diagnosis is crucial for improving survival rates and effective treatment. OBJECTIVES This study aims to develop an innovative artificial intelligence (AI) based model for predicting breast cancer and its various histopathological grades by integrating multiple biomarkers and subject age, thereby enhancing diagnostic accuracy and prognostication. METHODS A novel ensemble-based machine learning (ML) framework has been introduced that integrates three distinct biomarkers-beta-human chorionic gonadotropin (β-hCG), Programmed Cell Death Ligand 1 (PD-L1), and alpha-fetoprotein (AFP)-alongside subject age. Hyperparameter optimization was performed using the Particle Swarm Optimization (PSO) algorithm, and minority oversampling techniques were employed to mitigate overfitting. The model's performance was validated through rigorous five-fold cross-validation. RESULTS The proposed model demonstrated superior performance, achieving a 97.93% accuracy and a 98.06% F1-score on meticulously labeled test data across diverse age groups. Comparative analysis showed that the model outperforms state-of-the-art approaches, highlighting its robustness and generalizability. CONCLUSION By providing a comprehensive analysis of multiple biomarkers and effectively predicting tumor grades, this study offers a significant advancement in breast cancer screening, particularly in regions with limited medical resources. The proposed framework has the potential to reduce breast cancer mortality rates and improve early intervention and personalized treatment strategies.
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Affiliation(s)
- Rakesh Chandra Joshi
- Amity Centre for Artificial Intelligence, Amity University, Noida, Uttar Pradesh, India; Centre for Advanced Studies, Dr. A.P.J. Abdul Kalam Technical University, Lucknow, Uttar Pradesh, India
| | - Pallavi Srivastava
- Department of Biotechnology, Noida Institute of Engineering & Technology, Greater Noida, Uttar Pradesh, India
| | - Rashmi Mishra
- Department of Biotechnology, Noida Institute of Engineering & Technology, Greater Noida, Uttar Pradesh, India
| | - Radim Burget
- Department of Telecommunications, Faculty of Electrical Engineering and Communication, Brno University of Technology, Brno, Czech Republic
| | - Malay Kishore Dutta
- Amity Centre for Artificial Intelligence, Amity University, Noida, Uttar Pradesh, India.
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Vanni G, Pellicciaro M, Materazzo M, Berretta M, Meucci R, Perretta T, Portarena I, Pistolese CA, Buonomo OC. Radiological and pathological predictors of post-operative upstaging of breast ductal carcinoma in situ (DCIS) to invasive ductal carcinoma and lymph-nodes metastasis; a potential algorithm for node surgical de-escalation. Surg Oncol 2024; 56:102128. [PMID: 39241490 DOI: 10.1016/j.suronc.2024.102128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 07/12/2024] [Accepted: 08/29/2024] [Indexed: 09/09/2024]
Abstract
BACKGROUND/AIM Ductal carcinoma in situ is considered a local disease with no metastatic potential, thus sentinel lymph node biopsy (SLNB) may be deemed an overtreatment. SLNB should be reserved for patients with invasive cancer, even though the risk of upstaging rises to 25 %. We aimed to identify clinicopathological predictors of post-operative upstaging in invasive carcinoma. METHODS We retrospectively analyzed patients with a pre-operative diagnosis of DCIS subjected to breast surgery between January 2017 to December 2021, and evaluated at the Breast Unit of PTV (Policlinico Tor Vergata, Rome). RESULTS Out of 267 patients diagnosed with DCIS, 33(12.4 %) received a diagnosis upstaging and 9(3.37 %) patients presented with sentinel lymph node (SLN) metastasis. In multivariate analysis, grade 3 tumor (OR 1.9; 95 % CI 1.2-5.6), dense nodule at mammography (OR 1.3; 95 % CI 1.1-2.6) and presence of a solid nodule at ultrasonography (OR 1.5; 95 % CI 1.2-2.6) were independent upstaging predictors. Differently, the independent predictors for SLNB metastasis were: upstaging (OR 2.1.; 95 % CI 1.2-4.6; p = 0.0079) and age between 40 and 60yrs (OR 1.4; 95 % CI 1.4-2.7; p = 0.027). All 9 patients with SLN metastasis received a diagnosis upstaging and were aged between 40 and 60 years old. CONCLUSION We identified pre-operative independent predictors of upstaging to invasive ductal carcinoma. The combined use of different predictors in an algorithm for surgical treatments of DCIS could reduce the numbers of unnecessary SLNB.
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MESH Headings
- Humans
- Female
- Breast Neoplasms/pathology
- Breast Neoplasms/surgery
- Retrospective Studies
- Middle Aged
- Carcinoma, Intraductal, Noninfiltrating/surgery
- Carcinoma, Intraductal, Noninfiltrating/pathology
- Carcinoma, Intraductal, Noninfiltrating/secondary
- Lymphatic Metastasis
- Carcinoma, Ductal, Breast/surgery
- Carcinoma, Ductal, Breast/pathology
- Carcinoma, Ductal, Breast/secondary
- Algorithms
- Adult
- Aged
- Sentinel Lymph Node Biopsy/methods
- Prognosis
- Follow-Up Studies
- Mammography
- Mastectomy
- Neoplasm Staging
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Affiliation(s)
- Gianluca Vanni
- Breast Unit Policlinico Tor Vergata, Department of Surgical Science, Tor Vergata University, Viale Oxford 81, 00133, Rome, (RM), Italy
| | - Marco Pellicciaro
- Breast Unit Policlinico Tor Vergata, Department of Surgical Science, Tor Vergata University, Viale Oxford 81, 00133, Rome, (RM), Italy; Applied Medical-Surgical Sciences, Department of Surgical Science, Tor Vergata University, Rome, RM, Italy.
| | - Marco Materazzo
- Breast Unit Policlinico Tor Vergata, Department of Surgical Science, Tor Vergata University, Viale Oxford 81, 00133, Rome, (RM), Italy; Applied Medical-Surgical Sciences, Department of Surgical Science, Tor Vergata University, Rome, RM, Italy
| | - Massimiliano Berretta
- Department of Clinical and Experimental Medicine, University of Messina, 98100, Messina, (ME), Italy
| | - Rosaria Meucci
- Department of Diagnostic Imaging and Interventional Radiology, Molecular Imaging and Radiotherapy, Tor Vergata University, Viale Oxford 81, 00133, Rome, (RM), Italy
| | - Tommaso Perretta
- Department of Diagnostic Imaging and Interventional Radiology, Molecular Imaging and Radiotherapy, Tor Vergata University, Viale Oxford 81, 00133, Rome, (RM), Italy
| | - Ilaria Portarena
- Department of Oncology, Tor Vergata University, Viale Oxford 81, 00133, Rome, (RM), Italy
| | - Chiara Adriana Pistolese
- Department of Diagnostic Imaging and Interventional Radiology, Molecular Imaging and Radiotherapy, Tor Vergata University, Viale Oxford 81, 00133, Rome, (RM), Italy
| | - Oreste Claudio Buonomo
- Breast Unit Policlinico Tor Vergata, Department of Surgical Science, Tor Vergata University, Viale Oxford 81, 00133, Rome, (RM), Italy
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Bolano-Díaz C, Verdú-Díaz J, Díaz-Manera J. MRI for the diagnosis of limb girdle muscular dystrophies. Curr Opin Neurol 2024; 37:536-548. [PMID: 39132784 DOI: 10.1097/wco.0000000000001305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
PURPOSE OF REVIEW In the last 30 years, there have many publications describing the pattern of muscle involvement of different neuromuscular diseases leading to an increase in the information available for diagnosis. A high degree of expertise is needed to remember all the patterns described. Some attempts to use artificial intelligence or analysing muscle MRIs have been developed. We review the main patterns of involvement in limb girdle muscular dystrophies (LGMDs) and summarize the strategies for using artificial intelligence tools in this field. RECENT FINDINGS The most frequent LGMDs have a widely described pattern of muscle involvement; however, for those rarer diseases, there is still not too much information available. patients. Most of the articles still include only pelvic and lower limbs muscles, which provide an incomplete picture of the diseases. AI tools have efficiently demonstrated to predict diagnosis of a limited number of disease with high accuracy. SUMMARY Muscle MRI continues being a useful tool supporting the diagnosis of patients with LGMD and other neuromuscular diseases. However, the huge variety of patterns described makes their use in clinics a complicated task. Artificial intelligence tools are helping in that regard and there are already some accessible machine learning algorithms that can be used by the global medical community.
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Affiliation(s)
- Carla Bolano-Díaz
- The John Walton Muscular Dystrophy Research Centre, Newcastle University and Newcastle Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
| | - José Verdú-Díaz
- The John Walton Muscular Dystrophy Research Centre, Newcastle University and Newcastle Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
| | - Jordi Díaz-Manera
- The John Walton Muscular Dystrophy Research Centre, Newcastle University and Newcastle Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
- Neuromuscular Diseases Laboratory, Insitut de Recerca de l'Hospital de la Santa Creu i Sant Pau
- Centro de Investigación Biomédica en Red en Enfermedades Raras (CIBERER), Barcelona, Spain
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20
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Desolda G, Dimauro G, Esposito A, Lanzilotti R, Matera M, Zancanaro M. A Human-AI interaction paradigm and its application to rhinocytology. Artif Intell Med 2024; 155:102933. [PMID: 39094227 DOI: 10.1016/j.artmed.2024.102933] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 07/17/2024] [Accepted: 07/19/2024] [Indexed: 08/04/2024]
Abstract
This article explores Human-Centered Artificial Intelligence (HCAI) in medical cytology, with a focus on enhancing the interaction with AI. It presents a Human-AI interaction paradigm that emphasizes explainability and user control of AI systems. It is an iterative negotiation process based on three interaction strategies aimed to (i) elaborate the system outcomes through iterative steps (Iterative Exploration), (ii) explain the AI system's behavior or decisions (Clarification), and (iii) allow non-expert users to trigger simple retraining of the AI model (Reconfiguration). This interaction paradigm is exploited in the redesign of an existing AI-based tool for microscopic analysis of the nasal mucosa. The resulting tool is tested with rhinocytologists. The article discusses the analysis of the results of the conducted evaluation and outlines lessons learned that are relevant for AI in medicine.
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Affiliation(s)
- Giuseppe Desolda
- Department of Computer Science, University of Bari Aldo Moro, Via E. Orabona 4, Bari, 70125, Italy.
| | - Giovanni Dimauro
- Department of Computer Science, University of Bari Aldo Moro, Via E. Orabona 4, Bari, 70125, Italy.
| | - Andrea Esposito
- Department of Computer Science, University of Bari Aldo Moro, Via E. Orabona 4, Bari, 70125, Italy.
| | - Rosa Lanzilotti
- Department of Computer Science, University of Bari Aldo Moro, Via E. Orabona 4, Bari, 70125, Italy.
| | - Maristella Matera
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Via Ponzio 34/5, Milan, 20133, Italy.
| | - Massimo Zancanaro
- Department of Psychology and Cognitive Science, University of Trento, Corso Bettini 31, Rovereto, 38068, Italy; Fondazione Bruno Kessler, Povo, Trento, 38123, Italy.
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21
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Jing X, Wielema M, Monroy-Gonzalez AG, Stams TRG, Mahesh SVK, Oudkerk M, Sijens PE, Dorrius MD, van Ooijen PMA. Automated Breast Density Assessment in MRI Using Deep Learning and Radiomics: Strategies for Reducing Inter-Observer Variability. J Magn Reson Imaging 2024; 60:80-91. [PMID: 37846440 DOI: 10.1002/jmri.29058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 09/18/2023] [Accepted: 09/19/2023] [Indexed: 10/18/2023] Open
Abstract
BACKGROUND Accurate breast density evaluation allows for more precise risk estimation but suffers from high inter-observer variability. PURPOSE To evaluate the feasibility of reducing inter-observer variability of breast density assessment through artificial intelligence (AI) assisted interpretation. STUDY TYPE Retrospective. POPULATION Six hundred and twenty-one patients without breast prosthesis or reconstructions were randomly divided into training (N = 377), validation (N = 98), and independent test (N = 146) datasets. FIELD STRENGTH/SEQUENCE 1.5 T and 3.0 T; T1-weighted spectral attenuated inversion recovery. ASSESSMENT Five radiologists independently assessed each scan in the independent test set to establish the inter-observer variability baseline and to reach a reference standard. Deep learning and three radiomics models were developed for three classification tasks: (i) four Breast Imaging-Reporting and Data System (BI-RADS) breast composition categories (A-D), (ii) dense (categories C, D) vs. non-dense (categories A, B), and (iii) extremely dense (category D) vs. moderately dense (categories A-C). The models were tested against the reference standard on the independent test set. AI-assisted interpretation was performed by majority voting between the models and each radiologist's assessment. STATISTICAL TESTS Inter-observer variability was assessed using linear-weighted kappa (κ) statistics. Kappa statistics, accuracy, and area under the receiver operating characteristic curve (AUC) were used to assess models against reference standard. RESULTS In the independent test set, five readers showed an overall substantial agreement on tasks (i) and (ii), but moderate agreement for task (iii). The best-performing model showed substantial agreement with reference standard for tasks (i) and (ii), but moderate agreement for task (iii). With the assistance of the AI models, almost perfect inter-observer variability was obtained for tasks (i) (mean κ = 0.86), (ii) (mean κ = 0.94), and (iii) (mean κ = 0.94). DATA CONCLUSION Deep learning and radiomics models have the potential to help reduce inter-observer variability of breast density assessment. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Xueping Jing
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Machine Learning Lab, Data Science Center in Health (DASH), University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Mirjam Wielema
- Department of Radiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Andrea G Monroy-Gonzalez
- Department of Radiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Thom R G Stams
- Department of Radiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Shekar V K Mahesh
- Department of Radiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Matthijs Oudkerk
- Faculty of Medical Sciences, University of Groningen, Groningen, The Netherlands
- Institute of Diagnostic Accuracy Research B.V., Groningen, The Netherlands
| | - Paul E Sijens
- Department of Radiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Monique D Dorrius
- Department of Radiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Peter M A van Ooijen
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Machine Learning Lab, Data Science Center in Health (DASH), University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
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22
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Maleki Varnosfaderani S, Forouzanfar M. The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century. Bioengineering (Basel) 2024; 11:337. [PMID: 38671759 PMCID: PMC11047988 DOI: 10.3390/bioengineering11040337] [Citation(s) in RCA: 83] [Impact Index Per Article: 83.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 03/25/2024] [Accepted: 03/26/2024] [Indexed: 04/28/2024] Open
Abstract
As healthcare systems around the world face challenges such as escalating costs, limited access, and growing demand for personalized care, artificial intelligence (AI) is emerging as a key force for transformation. This review is motivated by the urgent need to harness AI's potential to mitigate these issues and aims to critically assess AI's integration in different healthcare domains. We explore how AI empowers clinical decision-making, optimizes hospital operation and management, refines medical image analysis, and revolutionizes patient care and monitoring through AI-powered wearables. Through several case studies, we review how AI has transformed specific healthcare domains and discuss the remaining challenges and possible solutions. Additionally, we will discuss methodologies for assessing AI healthcare solutions, ethical challenges of AI deployment, and the importance of data privacy and bias mitigation for responsible technology use. By presenting a critical assessment of AI's transformative potential, this review equips researchers with a deeper understanding of AI's current and future impact on healthcare. It encourages an interdisciplinary dialogue between researchers, clinicians, and technologists to navigate the complexities of AI implementation, fostering the development of AI-driven solutions that prioritize ethical standards, equity, and a patient-centered approach.
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Affiliation(s)
| | - Mohamad Forouzanfar
- Département de Génie des Systèmes, École de Technologie Supérieure (ÉTS), Université du Québec, Montréal, QC H3C 1K3, Canada
- Centre de Recherche de L’institut Universitaire de Gériatrie de Montréal (CRIUGM), Montréal, QC H3W 1W5, Canada
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23
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Al Mohammad B, Aldaradkeh A, Gharaibeh M, Reed W. Assessing radiologists' and radiographers' perceptions on artificial intelligence integration: opportunities and challenges. Br J Radiol 2024; 97:763-769. [PMID: 38273675 PMCID: PMC11027289 DOI: 10.1093/bjr/tqae022] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 09/30/2023] [Accepted: 01/21/2024] [Indexed: 01/27/2024] Open
Abstract
OBJECTIVES The objective of this study was to evaluate radiologists' and radiographers' opinions and perspectives on artificial intelligence (AI) and its integration into the radiology department. Additionally, we investigated the most common challenges and barriers that radiologists and radiographers face when learning about AI. METHODS A nationwide, online descriptive cross-sectional survey was distributed to radiologists and radiographers working in hospitals and medical centres from May 29, 2023 to July 30, 2023. The questionnaire examined the participants' opinions, feelings, and predictions regarding AI and its applications in the radiology department. Descriptive statistics were used to report the participants' demographics and responses. Five-points Likert-scale data were reported using divergent stacked bar graphs to highlight any central tendencies. RESULTS Responses were collected from 258 participants, revealing a positive attitude towards implementing AI. Both radiologists and radiographers predicted breast imaging would be the subspecialty most impacted by the AI revolution. MRI, mammography, and CT were identified as the primary modalities with significant importance in the field of AI application. The major barrier encountered by radiologists and radiographers when learning about AI was the lack of mentorship, guidance, and support from experts. CONCLUSION Participants demonstrated a positive attitude towards learning about AI and implementing it in the radiology practice. However, radiologists and radiographers encounter several barriers when learning about AI, such as the absence of experienced professionals support and direction. ADVANCES IN KNOWLEDGE Radiologists and radiographers reported several barriers to AI learning, with the most significant being the lack of mentorship and guidance from experts, followed by the lack of funding and investment in new technologies.
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Affiliation(s)
- Badera Al Mohammad
- Department of Allied Medical Sciences, Faculty of Applied Medical Sciences, Jordan University of Science and Technology, Irbid 22110, Jordan
| | - Afnan Aldaradkeh
- Department of Allied Medical Sciences, Faculty of Applied Medical Sciences, Jordan University of Science and Technology, Irbid 22110, Jordan
| | - Monther Gharaibeh
- Department of Special Surgery, Faculty of Medicine, The Hashemite University, Zarqa 13133, Jordan
| | - Warren Reed
- Discipline of Medical Imaging Science, Faculty of Medicine and Health, University of Sydney 2006, Sydney, NSW, Australia
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24
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Lo Gullo R, Marcus E, Huayanay J, Eskreis-Winkler S, Thakur S, Teuwen J, Pinker K. Artificial Intelligence-Enhanced Breast MRI: Applications in Breast Cancer Primary Treatment Response Assessment and Prediction. Invest Radiol 2024; 59:230-242. [PMID: 37493391 PMCID: PMC10818006 DOI: 10.1097/rli.0000000000001010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/27/2023]
Abstract
ABSTRACT Primary systemic therapy (PST) is the treatment of choice in patients with locally advanced breast cancer and is nowadays also often used in patients with early-stage breast cancer. Although imaging remains pivotal to assess response to PST accurately, the use of imaging to predict response to PST has the potential to not only better prognostication but also allow the de-escalation or omission of potentially toxic treatment with undesirable adverse effects, the accelerated implementation of new targeted therapies, and the mitigation of surgical delays in selected patients. In response to the limited ability of radiologists to predict response to PST via qualitative, subjective assessments of tumors on magnetic resonance imaging (MRI), artificial intelligence-enhanced MRI with classical machine learning, and in more recent times, deep learning, have been used with promising results to predict response, both before the start of PST and in the early stages of treatment. This review provides an overview of the current applications of artificial intelligence to MRI in assessing and predicting response to PST, and discusses the challenges and limitations of their clinical implementation.
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Affiliation(s)
- Roberto Lo Gullo
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66 Street, New York, NY 10065, USA
| | - Eric Marcus
- AI for Oncology, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, the Netherlands
| | - Jorge Huayanay
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66 Street, New York, NY 10065, USA
- Department of Radiology, National Institute of Neoplastic Diseases, Lima, Peru
| | - Sarah Eskreis-Winkler
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66 Street, New York, NY 10065, USA
| | - Sunitha Thakur
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Jonas Teuwen
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66 Street, New York, NY 10065, USA
- Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA Nijmegen, the Netherlands
- AI for Oncology, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, the Netherlands
| | - Katja Pinker
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66 Street, New York, NY 10065, USA
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Bhalla K, Xiao Q, Luna JM, Podany E, Ahmad T, Ademuyiwa FO, Davis A, Bennett DL, Gastounioti A. Radiologic imaging biomarkers in triple-negative breast cancer: a literature review about the role of artificial intelligence and the way forward. BJR ARTIFICIAL INTELLIGENCE 2024; 1:ubae016. [PMID: 40201726 PMCID: PMC11974408 DOI: 10.1093/bjrai/ubae016] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/27/2024] [Revised: 09/27/2024] [Accepted: 11/10/2024] [Indexed: 04/10/2025]
Abstract
Breast cancer is one of the most common and deadly cancers in women. Triple-negative breast cancer (TNBC) accounts for approximately 10%-15% of breast cancer diagnoses and is an aggressive molecular breast cancer subtype associated with important challenges in its diagnosis, treatment, and prognostication. This poses an urgent need for developing more effective and personalized imaging biomarkers for TNBC. Towards this direction, artificial intelligence (AI) for radiologic imaging holds a prominent role, leveraging unique advantages of radiologic breast images, being used routinely for TNBC diagnosis, staging, and treatment planning, and offering high-resolution whole-tumour visualization, combined with the immense potential of AI to elucidate anatomical and functional properties of tumours that may not be easily perceived by the human eye. In this review, we synthesize the current state-of-the-art radiologic imaging applications of AI in assisting TNBC diagnosis, treatment, and prognostication. Our goal is to provide a comprehensive overview of radiomic and deep learning-based AI developments and their impact on advancing TNBC management over the last decade (2013-2024). For completeness of the review, we start with a brief introduction of AI, radiomics, and deep learning. Next, we focus on clinically relevant AI-based diagnostic, predictive, and prognostic models for radiologic breast images evaluated in TNBC. We conclude with opportunities and future directions for AI towards advancing diagnosis, treatment response predictions, and prognostic evaluations for TNBC.
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Affiliation(s)
- Kanika Bhalla
- Breast Image Computing Lab, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, United States
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, United States
| | - Qi Xiao
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, United States
| | - José Marcio Luna
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, United States
- Alvin J. Siteman Cancer Center, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, United States
| | - Emily Podany
- Division of Hematology, Department of Medicine, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, United States
- Division of Oncology, Department of Medicine, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, United States
| | - Tabassum Ahmad
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, United States
| | - Foluso O Ademuyiwa
- Alvin J. Siteman Cancer Center, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, United States
- Division of Oncology, Department of Medicine, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, United States
| | - Andrew Davis
- Alvin J. Siteman Cancer Center, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, United States
- Division of Oncology, Department of Medicine, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, United States
| | - Debbie Lee Bennett
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, United States
- Alvin J. Siteman Cancer Center, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, United States
| | - Aimilia Gastounioti
- Breast Image Computing Lab, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, United States
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, United States
- Alvin J. Siteman Cancer Center, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, United States
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26
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Zhang L, Xiao Z, Jiang W, Luo C, Ye M, Yue G, Chen Z, Ouyang S, Liu Y. Liver fibrosis MR images classification based on higher-order interaction and sample distribution rebalancing. Health Inf Sci Syst 2023; 11:51. [PMID: 37954065 PMCID: PMC10632346 DOI: 10.1007/s13755-023-00255-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 10/17/2023] [Indexed: 11/14/2023] Open
Abstract
The fractal features of liver fibrosis MR images exhibit an irregular fragmented distribution, and the diffuse feature distribution lacks interconnectivity, result- ing in incomplete feature learning and poor recognition accuracy. In this paper, we insert recursive gated convolution into the ResNet18 network to introduce spatial information interactions during the feature learning process and extend it to higher orders using recursion. Higher-order spatial information interactions enhance the correlation between features and enable the neural network to focus more on the pixel-level dependencies, enabling a global interpretation of liver MR images. Additionally, the existence of light scattering and quantum noise during the imaging process, coupled with environmental factors such as breathing artifacts caused by long time breath holding, affects the quality of the MR images. To improve the classification performance of the neural network and better cap- ture sample features, we introduce the Adaptive Rebalance loss function and incorporate the feature paradigm as a learnable adaptive attribute into the angular margin auxiliary function. Adaptive Rebalance loss function can expand the inter-class distance and narrow the intra-class difference to further enhance discriminative ability of the model. We conduct extensive experiments on liver fibrosis MR imaging involving 209 patients. The results demonstrate an average improvement of two percent in recognition accuracy compared to ResNet18. The github is at https://github.com/XZN1233/paper.git.
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Affiliation(s)
- Ling Zhang
- School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, 510006 Guangdong China
| | - Zhennan Xiao
- School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, 510006 Guangdong China
| | - Wenchao Jiang
- School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, 510006 Guangdong China
| | - Chengbin Luo
- School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, 510006 Guangdong China
| | - Ming Ye
- School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, 510006 Guangdong China
| | - Guanghui Yue
- School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, 518060 Guangdong China
| | - Zhiyuan Chen
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120 Guangdong China
| | - Shuman Ouyang
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120 Guangdong China
| | - Yupin Liu
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120 Guangdong China
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Li JW, Sheng DL, Chen JG, You C, Liu S, Xu HX, Chang C. Artificial intelligence in breast imaging: potentials and challenges. Phys Med Biol 2023; 68:23TR01. [PMID: 37722385 DOI: 10.1088/1361-6560/acfade] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Accepted: 09/18/2023] [Indexed: 09/20/2023]
Abstract
Breast cancer, which is the most common type of malignant tumor among humans, is a leading cause of death in females. Standard treatment strategies, including neoadjuvant chemotherapy, surgery, postoperative chemotherapy, targeted therapy, endocrine therapy, and radiotherapy, are tailored for individual patients. Such personalized therapies have tremendously reduced the threat of breast cancer in females. Furthermore, early imaging screening plays an important role in reducing the treatment cycle and improving breast cancer prognosis. The recent innovative revolution in artificial intelligence (AI) has aided radiologists in the early and accurate diagnosis of breast cancer. In this review, we introduce the necessity of incorporating AI into breast imaging and the applications of AI in mammography, ultrasonography, magnetic resonance imaging, and positron emission tomography/computed tomography based on published articles since 1994. Moreover, the challenges of AI in breast imaging are discussed.
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Affiliation(s)
- Jia-Wei Li
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
| | - Dan-Li Sheng
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
| | - Jian-Gang Chen
- Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication & Electronic Engineering, East China Normal University, People's Republic of China
| | - Chao You
- Department of Radiology, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, People's Republic of China
| | - Shuai Liu
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, People's Republic of China
| | - Hui-Xiong Xu
- Department of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, 200032, People's Republic of China
| | - Cai Chang
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
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Zhang JQ, Mi JJ, Wang R. Application of convolutional neural network-based endoscopic imaging in esophageal cancer or high-grade dysplasia: A systematic review and meta-analysis. World J Gastrointest Oncol 2023; 15:1998-2016. [DOI: 10.4251/wjgo.v15.i11.1998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 09/05/2023] [Accepted: 10/11/2023] [Indexed: 11/15/2023] Open
Abstract
BACKGROUND Esophageal cancer is the seventh-most common cancer type worldwide, accounting for 5% of death from malignancy. Development of novel diagnostic techniques has facilitated screening, early detection, and improved prognosis. Convolutional neural network (CNN)-based image analysis promises great potential for diagnosing and determining the prognosis of esophageal cancer, enabling even early detection of dysplasia.
AIM To conduct a meta-analysis of the diagnostic accuracy of CNN models for the diagnosis of esophageal cancer and high-grade dysplasia (HGD).
METHODS PubMed, EMBASE, Web of Science and Cochrane Library databases were searched for articles published up to November 30, 2022. We evaluated the diagnostic accuracy of using the CNN model with still image-based analysis and with video-based analysis for esophageal cancer or HGD, as well as for the invasion depth of esophageal cancer. The pooled sensitivity, pooled specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR) and area under the curve (AUC) were estimated, together with the 95% confidence intervals (CI). A bivariate method and hierarchical summary receiver operating characteristic method were used to calculate the diagnostic test accuracy of the CNN model. Meta-regression and subgroup analyses were used to identify sources of heterogeneity.
RESULTS A total of 28 studies were included in this systematic review and meta-analysis. Using still image-based analysis for the diagnosis of esophageal cancer or HGD provided a pooled sensitivity of 0.95 (95%CI: 0.92-0.97), pooled specificity of 0.92 (0.89-0.94), PLR of 11.5 (8.3-16.0), NLR of 0.06 (0.04-0.09), DOR of 205 (115-365), and AUC of 0.98 (0.96-0.99). When video-based analysis was used, a pooled sensitivity of 0.85 (0.77-0.91), pooled specificity of 0.73 (0.59-0.83), PLR of 3.1 (1.9-5.0), NLR of 0.20 (0.12-0.34), DOR of 15 (6-38) and AUC of 0.87 (0.84-0.90) were found. Prediction of invasion depth resulted in a pooled sensitivity of 0.90 (0.87-0.92), pooled specificity of 0.83 (95%CI: 0.76-0.88), PLR of 7.8 (1.9-32.0), NLR of 0.10 (0.41-0.25), DOR of 118 (11-1305), and AUC of 0.95 (0.92-0.96).
CONCLUSION CNN-based image analysis in diagnosing esophageal cancer and HGD is an excellent diagnostic method with high sensitivity and specificity that merits further investigation in large, multicenter clinical trials.
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Affiliation(s)
- Jun-Qi Zhang
- The Fifth Clinical Medical College, Shanxi Medical University, Taiyuan 030001, Shanxi Province, China
| | - Jun-Jie Mi
- Department of Gastroenterology, Shanxi Provincial People’s Hospital, Taiyuan 030012, Shanxi Province, China
| | - Rong Wang
- Department of Gastroenterology, The Fifth Hospital of Shanxi Medical University (Shanxi Provincial People’s Hospital), Taiyuan 030012, Shanxi Province, China
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Zhang J, Wu J, Zhou XS, Shi F, Shen D. Recent advancements in artificial intelligence for breast cancer: Image augmentation, segmentation, diagnosis, and prognosis approaches. Semin Cancer Biol 2023; 96:11-25. [PMID: 37704183 DOI: 10.1016/j.semcancer.2023.09.001] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 08/03/2023] [Accepted: 09/05/2023] [Indexed: 09/15/2023]
Abstract
Breast cancer is a significant global health burden, with increasing morbidity and mortality worldwide. Early screening and accurate diagnosis are crucial for improving prognosis. Radiographic imaging modalities such as digital mammography (DM), digital breast tomosynthesis (DBT), magnetic resonance imaging (MRI), ultrasound (US), and nuclear medicine techniques, are commonly used for breast cancer assessment. And histopathology (HP) serves as the gold standard for confirming malignancy. Artificial intelligence (AI) technologies show great potential for quantitative representation of medical images to effectively assist in segmentation, diagnosis, and prognosis of breast cancer. In this review, we overview the recent advancements of AI technologies for breast cancer, including 1) improving image quality by data augmentation, 2) fast detection and segmentation of breast lesions and diagnosis of malignancy, 3) biological characterization of the cancer such as staging and subtyping by AI-based classification technologies, 4) prediction of clinical outcomes such as metastasis, treatment response, and survival by integrating multi-omics data. Then, we then summarize large-scale databases available to help train robust, generalizable, and reproducible deep learning models. Furthermore, we conclude the challenges faced by AI in real-world applications, including data curating, model interpretability, and practice regulations. Besides, we expect that clinical implementation of AI will provide important guidance for the patient-tailored management.
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Affiliation(s)
- Jiadong Zhang
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
| | - Jiaojiao Wu
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Xiang Sean Zhou
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Feng Shi
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China.
| | - Dinggang Shen
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China; Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China; Shanghai Clinical Research and Trial Center, Shanghai, China.
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Cheng K, Wang J, Liu J, Zhang X, Shen Y, Su H. Public health implications of computer-aided diagnosis and treatment technologies in breast cancer care. AIMS Public Health 2023; 10:867-895. [PMID: 38187901 PMCID: PMC10764974 DOI: 10.3934/publichealth.2023057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2023] [Accepted: 10/10/2023] [Indexed: 01/09/2024] Open
Abstract
Breast cancer remains a significant public health issue, being a leading cause of cancer-related mortality among women globally. Timely diagnosis and efficient treatment are crucial for enhancing patient outcomes, reducing healthcare burdens and advancing community health. This systematic review, following the PRISMA guidelines, aims to comprehensively synthesize the recent advancements in computer-aided diagnosis and treatment for breast cancer. The study covers the latest developments in image analysis and processing, machine learning and deep learning algorithms, multimodal fusion techniques and radiation therapy planning and simulation. The results of the review suggest that machine learning, augmented and virtual reality and data mining are the three major research hotspots in breast cancer management. Moreover, this paper discusses the challenges and opportunities for future research in this field. The conclusion highlights the importance of computer-aided techniques in the management of breast cancer and summarizes the key findings of the review.
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Affiliation(s)
- Kai Cheng
- Yantai Affiliated Hospital of Binzhou Medical University, Yantai, 264100, China
| | - Jiangtao Wang
- Yantai Affiliated Hospital of Binzhou Medical University, Yantai, 264100, China
| | - Jian Liu
- Yantai Affiliated Hospital of Binzhou Medical University, Yantai, 264100, China
| | - Xiangsheng Zhang
- Yantai Affiliated Hospital of Binzhou Medical University, Yantai, 264100, China
| | - Yuanyuan Shen
- Yantai Affiliated Hospital of Binzhou Medical University, Yantai, 264100, China
| | - Hang Su
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
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Zhang H, Meng Z, Ru J, Meng Y, Wang K. Application and prospects of AI-based radiomics in ultrasound diagnosis. Vis Comput Ind Biomed Art 2023; 6:20. [PMID: 37828411 PMCID: PMC10570254 DOI: 10.1186/s42492-023-00147-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 09/20/2023] [Indexed: 10/14/2023] Open
Abstract
Artificial intelligence (AI)-based radiomics has attracted considerable research attention in the field of medical imaging, including ultrasound diagnosis. Ultrasound imaging has unique advantages such as high temporal resolution, low cost, and no radiation exposure. This renders it a preferred imaging modality for several clinical scenarios. This review includes a detailed introduction to imaging modalities, including Brightness-mode ultrasound, color Doppler flow imaging, ultrasound elastography, contrast-enhanced ultrasound, and multi-modal fusion analysis. It provides an overview of the current status and prospects of AI-based radiomics in ultrasound diagnosis, highlighting the application of AI-based radiomics to static ultrasound images, dynamic ultrasound videos, and multi-modal ultrasound fusion analysis.
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Affiliation(s)
- Haoyan Zhang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100190, China
| | - Zheling Meng
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100190, China
| | - Jinyu Ru
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100190, China
| | - Yaqing Meng
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100190, China
| | - Kun Wang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100190, China.
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Hesso I, Kayyali R, Dolton DR, Joo K, Zacharias L, Charalambous A, Lavdaniti M, Stalika E, Ajami T, Acampa W, Boban J, Nabhani-Gebara S. Cancer care at the time of the fourth industrial revolution: an insight to healthcare professionals' perspectives on cancer care and artificial intelligence. Radiat Oncol 2023; 18:167. [PMID: 37814325 PMCID: PMC10561443 DOI: 10.1186/s13014-023-02351-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 09/13/2023] [Indexed: 10/11/2023] Open
Abstract
BACKGROUND The integration of Artificial Intelligence (AI) technology in cancer care has gained unprecedented global attention over the past few decades. This has impacted the way that cancer care is practiced and delivered across settings. The purpose of this study was to explore the perspectives and experiences of healthcare professionals (HCPs) on cancer treatment and the need for AI. This study is a part of the INCISIVE European Union H2020 project's development of user requirements, which aims to fully explore the potential of AI-based cancer imaging technologies. METHODS A mixed-methods research design was employed. HCPs participating in cancer care in the UK, Greece, Italy, Spain, Cyprus, and Serbia were first surveyed anonymously online. Twenty-seven HCPs then participated in semi-structured interviews. Appropriate statistical method was adopted to report the survey results by using SPSS. The interviews were audio recorded, verbatim transcribed, and then thematically analysed supported by NVIVO. RESULTS The survey drew responses from 95 HCPs. The occurrence of diagnostic delay was reported by 56% (n = 28/50) for breast cancer, 64% (n = 27/42) for lung cancer, 76% (n = 34/45) for colorectal cancer and 42% (n = 16/38) for prostate cancer. A proportion of participants reported the occurrence of false positives in the accuracy of the current imaging techniques used: 64% (n = 32/50) reported this for breast cancer, 60% (n = 25/42) for lung cancer, 51% (n = 23/45) for colorectal cancer and 45% (n = 17/38) for prostate cancer. All participants agreed that the use of technology would enhance the care pathway for cancer patients. Despite the positive perspectives toward AI, certain limitations were also recorded. The majority (73%) of respondents (n = 69/95) reported they had never utilised technology in the care pathway which necessitates the need for education and training in the qualitative finding; compared to 27% (n = 26/95) who had and were still using it. Most, 89% of respondents (n = 85/95) said they would be opened to providing AI-based services in the future to improve medical imaging for cancer care. Interviews with HCPs revealed lack of widespread preparedness for AI in oncology, several barriers to introducing AI, and a need for education and training. Provision of AI training, increasing public awareness of AI, using evidence-based technology, and developing AI based interventions that will not replace HCPs were some of the recommendations. CONCLUSION HCPs reported favourable opinions of AI-based cancer imaging technologies and noted a number of care pathway concerns where AI can be useful. For the future design and execution of the INCISIVE project and other comparable AI-based projects, the characteristics and recommendations offered in the current research can serve as a reference.
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Affiliation(s)
- Iman Hesso
- School of Life Sciences, Pharmacy and Chemistry, Kingston University London, Penrhyn Road, Kingston Upon Thames, KT1 2EE, UK
| | - Reem Kayyali
- School of Life Sciences, Pharmacy and Chemistry, Kingston University London, Penrhyn Road, Kingston Upon Thames, KT1 2EE, UK
| | - Debbie-Rose Dolton
- School of Life Sciences, Pharmacy and Chemistry, Kingston University London, Penrhyn Road, Kingston Upon Thames, KT1 2EE, UK
| | - Kwanyoung Joo
- School of Life Sciences, Pharmacy and Chemistry, Kingston University London, Penrhyn Road, Kingston Upon Thames, KT1 2EE, UK
| | - Lithin Zacharias
- School of Life Sciences, Pharmacy and Chemistry, Kingston University London, Penrhyn Road, Kingston Upon Thames, KT1 2EE, UK
| | - Andreas Charalambous
- Cyprus University of Technology, Limassol, Cyprus
- University of Turku, Turku, Finland
| | | | - Evangelia Stalika
- International Hellenic University, Thessaloniki, Greece
- Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Tarek Ajami
- Urology Department, Hospital Clinic de Barcelona, Barcelona, Spain
| | - Wanda Acampa
- Department of Advanced Biomedical Science, University of Naples Federico II, Naples, Italy
| | - Jasmina Boban
- Department of Radiology, Faculty of Medicine, University of Novi Sad, Hajduk Veljkova 3, 21000, Novi Sad, Serbia
- Diagnostic Imaging Center, Oncology Institute of Vojvodine, Put Dr Goldmana 4, 21204, Sremska Kamenica, Serbia
| | - Shereen Nabhani-Gebara
- School of Life Sciences, Pharmacy and Chemistry, Kingston University London, Penrhyn Road, Kingston Upon Thames, KT1 2EE, UK.
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You C, Shen Y, Sun S, Zhou J, Li J, Su G, Michalopoulou E, Peng W, Gu Y, Guo W, Cao H. Artificial intelligence in breast imaging: Current situation and clinical challenges. EXPLORATION (BEIJING, CHINA) 2023; 3:20230007. [PMID: 37933287 PMCID: PMC10582610 DOI: 10.1002/exp.20230007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 04/30/2023] [Indexed: 11/08/2023]
Abstract
Breast cancer ranks among the most prevalent malignant tumours and is the primary contributor to cancer-related deaths in women. Breast imaging is essential for screening, diagnosis, and therapeutic surveillance. With the increasing demand for precision medicine, the heterogeneous nature of breast cancer makes it necessary to deeply mine and rationally utilize the tremendous amount of breast imaging information. With the rapid advancement of computer science, artificial intelligence (AI) has been noted to have great advantages in processing and mining of image information. Therefore, a growing number of scholars have started to focus on and research the utility of AI in breast imaging. Here, an overview of breast imaging databases and recent advances in AI research are provided, the challenges and problems in this field are discussed, and then constructive advice is further provided for ongoing scientific developments from the perspective of the National Natural Science Foundation of China.
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Affiliation(s)
- Chao You
- Department of RadiologyFudan University Shanghai Cancer CenterShanghaiChina
- Department of OncologyShanghai Medical CollegeFudan UniversityShanghaiChina
| | - Yiyuan Shen
- Department of RadiologyFudan University Shanghai Cancer CenterShanghaiChina
- Department of OncologyShanghai Medical CollegeFudan UniversityShanghaiChina
| | - Shiyun Sun
- Department of RadiologyFudan University Shanghai Cancer CenterShanghaiChina
- Department of OncologyShanghai Medical CollegeFudan UniversityShanghaiChina
| | - Jiayin Zhou
- Department of RadiologyFudan University Shanghai Cancer CenterShanghaiChina
- Department of OncologyShanghai Medical CollegeFudan UniversityShanghaiChina
| | - Jiawei Li
- Department of RadiologyFudan University Shanghai Cancer CenterShanghaiChina
- Department of OncologyShanghai Medical CollegeFudan UniversityShanghaiChina
| | - Guanhua Su
- Department of OncologyShanghai Medical CollegeFudan UniversityShanghaiChina
- Department of Breast SurgeryKey Laboratory of Breast Cancer in ShanghaiFudan University Shanghai Cancer CenterShanghaiChina
| | | | - Weijun Peng
- Department of RadiologyFudan University Shanghai Cancer CenterShanghaiChina
- Department of OncologyShanghai Medical CollegeFudan UniversityShanghaiChina
| | - Yajia Gu
- Department of RadiologyFudan University Shanghai Cancer CenterShanghaiChina
- Department of OncologyShanghai Medical CollegeFudan UniversityShanghaiChina
| | - Weisheng Guo
- Department of Minimally Invasive Interventional RadiologyKey Laboratory of Molecular Target and Clinical PharmacologySchool of Pharmaceutical Sciences and The Second Affiliated HospitalGuangzhou Medical UniversityGuangzhouChina
| | - Heqi Cao
- Department of Health SciencesNational Natural Science Foundation of ChinaBeijingChina
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Mahmoud A, El-Sharkawy YH. Delineation and detection of breast cancer using novel label-free fluorescence. BMC Med Imaging 2023; 23:132. [PMID: 37716994 PMCID: PMC10505331 DOI: 10.1186/s12880-023-01095-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 09/04/2023] [Indexed: 09/18/2023] Open
Abstract
BACKGROUND Accurate diagnosis of breast cancer (BC) plays a crucial role in clinical pathology analysis and ensuring precise surgical margins to prevent recurrence. METHODS Laser-induced fluorescence (LIF) technology offers high sensitivity to tissue biochemistry, making it a potential tool for noninvasive BC identification. In this study, we utilized hyperspectral (HS) imaging data of stimulated BC specimens to detect malignancies based on altered fluorescence characteristics compared to normal tissue. Initially, we employed a HS camera and broadband spectrum light to assess the absorbance of BC samples. Notably, significant absorbance differences were observed in the 440-460 nm wavelength range. Subsequently, we developed a specialized LIF system for BC detection, utilizing a low-power blue laser source at 450 nm wavelength for ten BC samples. RESULTS Our findings revealed that the fluorescence distribution of breast specimens, which carries molecular-scale structural information, serves as an effective marker for identifying breast tumors. Specifically, the emission at 561 nm exhibited the greatest variation in fluorescence signal intensity for both tumor and normal tissue, serving as an optical predictive biomarker. To enhance BC identification, we propose an advanced image classification technique that combines image segmentation using contour mapping and K-means clustering (K-mc, K = 8) for HS emission image data analysis. CONCLUSIONS This exploratory work presents a potential avenue for improving "in-vivo" disease characterization using optical technology, specifically our LIF technique combined with the advanced K-mc approach, facilitating early tumor diagnosis in BC.
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Affiliation(s)
- Alaaeldin Mahmoud
- Optoelectronics and automatic control systems department, Military Technical College, Kobry El-Kobba, Cairo, Egypt.
| | - Yasser H El-Sharkawy
- Optoelectronics and automatic control systems department, Military Technical College, Kobry El-Kobba, Cairo, Egypt
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Zhang J, Cui Z, Shi Z, Jiang Y, Zhang Z, Dai X, Yang Z, Gu Y, Zhou L, Han C, Huang X, Ke C, Li S, Xu Z, Gao F, Zhou L, Wang R, Liu J, Zhang J, Ding Z, Sun K, Li Z, Liu Z, Shen D. A robust and efficient AI assistant for breast tumor segmentation from DCE-MRI via a spatial-temporal framework. PATTERNS (NEW YORK, N.Y.) 2023; 4:100826. [PMID: 37720328 PMCID: PMC10499873 DOI: 10.1016/j.patter.2023.100826] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 04/25/2023] [Accepted: 07/21/2023] [Indexed: 09/19/2023]
Abstract
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) allows screening, follow up, and diagnosis for breast tumor with high sensitivity. Accurate tumor segmentation from DCE-MRI can provide crucial information of tumor location and shape, which significantly influences the downstream clinical decisions. In this paper, we aim to develop an artificial intelligence (AI) assistant to automatically segment breast tumors by capturing dynamic changes in multi-phase DCE-MRI with a spatial-temporal framework. The main advantages of our AI assistant include (1) robustness, i.e., our model can handle MR data with different phase numbers and imaging intervals, as demonstrated on a large-scale dataset from seven medical centers, and (2) efficiency, i.e., our AI assistant significantly reduces the time required for manual annotation by a factor of 20, while maintaining accuracy comparable to that of physicians. More importantly, as the fundamental step to build an AI-assisted breast cancer diagnosis system, our AI assistant will promote the application of AI in more clinical diagnostic practices regarding breast cancer.
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Affiliation(s)
- Jiadong Zhang
- School of Biomedical Engineering, ShanghaiTech University, Shanghai 201210, China
| | - Zhiming Cui
- School of Biomedical Engineering, ShanghaiTech University, Shanghai 201210, China
| | - Zhenwei Shi
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong 510080, China
| | - Yingjia Jiang
- Department of Radiology, The Second Xiangya Hospital, Central South University, Hunan 410011, China
| | - Zhiliang Zhang
- School of Medical Imaging, Hangzhou Medical College, Zhejiang 310059, China
| | - Xiaoting Dai
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China
| | - Zhenlu Yang
- Department of Radiology, Guizhou Provincial People’s Hospital, Guizhou 550002, China
| | - Yuning Gu
- School of Biomedical Engineering, ShanghaiTech University, Shanghai 201210, China
| | - Lei Zhou
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Chu Han
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong 510080, China
| | - Xiaomei Huang
- Department of Medical Imaging, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China
| | - Chenglu Ke
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong 510080, China
| | - Suyun Li
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong 510080, China
| | - Zeyan Xu
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong 510080, China
| | - Fei Gao
- School of Biomedical Engineering, ShanghaiTech University, Shanghai 201210, China
| | - Luping Zhou
- School of Electrical and Information Engineering, The University of Sydney, Sydney, NSW 2006, Australia
| | - Rongpin Wang
- Department of Radiology, Guizhou Provincial People’s Hospital, Guizhou 550002, China
| | - Jun Liu
- Department of Radiology, The Second Xiangya Hospital, Central South University, Hunan 410011, China
| | - Jiayin Zhang
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China
| | - Zhongxiang Ding
- Department of Radiology, Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Hangzhou 310003, China
| | - Kun Sun
- Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Zhenhui Li
- Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Kunming 650118, China
| | - Zaiyi Liu
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong 510080, China
| | - Dinggang Shen
- School of Biomedical Engineering, ShanghaiTech University, Shanghai 201210, China
- Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200230, China
- Shanghai Clinical Research and Trial Center, Shanghai 200052, China
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Fatima Qizilbash F, Sartaj A, Qamar Z, Kumar S, Imran M, Mohammed Y, Ali J, Baboota S, Ali A. Nanotechnology revolutionises breast cancer treatment: harnessing lipid-based nanocarriers to combat cancer cells. J Drug Target 2023; 31:794-816. [PMID: 37525966 DOI: 10.1080/1061186x.2023.2243403] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 07/21/2023] [Accepted: 07/26/2023] [Indexed: 08/02/2023]
Abstract
One of the most common cancers that occur in females is breast cancer. Despite the significant leaps and bounds that have been made in treatment of breast cancer, the disease remains one of the leading causes of death among women and a major public health challenge. The therapeutic efficacy of chemotherapeutics is hindered by chemoresistance and toxicity. Nano-based lipid drug delivery systems offer controlled drug release, nanometric size and site-specific targeting. Breast cancer treatment includes surgery, chemotherapy and radiotherapy. Despite this, no single method of treatment for the condition is currently effective due to cancer stem cell metastasis and chemo-resistance. Therefore, the employment of nanocarrier systems is necessary in order to target breast cancer stem cells. This article addresses breast cancer treatment options, including modern treatment procedures such as chemotherapy, etc. and some innovative therapeutic options highlighting the role of lipidic nanocarriers loaded with chemotherapeutic drugs such as nanoemulsion, solid-lipid nanoparticles, nanostructured lipid carriers and liposomes, and their investigations have demonstrated that they can limit cancer cell growth, reduce the risk of recurrence, as well as minimise post-chemotherapy metastasis. This article also explores FDA-approved lipid-based nanocarriers, commercially available formulations, and ligand-based formulations that are being considered for further research.
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Affiliation(s)
| | - Ali Sartaj
- Department of Pharmaceutics, School of Pharmaceutical Education and Research, New Delhi, India
- Lloyd School of Pharmacy, Greater Noida, India
| | - Zufika Qamar
- Department of Pharmaceutics, School of Pharmaceutical Education and Research, New Delhi, India
| | - Shobhit Kumar
- Department of Pharmaceutical Technology, Meerut Institute of Engineering and Technology (MIET), Meerut, India
| | - Mohammad Imran
- Therapeutics Research Group, Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, Australia
| | - Yousuf Mohammed
- Therapeutics Research Group, Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, Australia
- School of Pharmacy, The University of Queensland, Brisbane, Australia
| | - Javed Ali
- Department of Pharmaceutics, School of Pharmaceutical Education and Research, New Delhi, India
| | - Sanjula Baboota
- Department of Pharmaceutics, School of Pharmaceutical Education and Research, New Delhi, India
| | - Asgar Ali
- Department of Pharmaceutics, School of Pharmaceutical Education and Research, New Delhi, India
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Zhang Y, Liu YL, Nie K, Zhou J, Chen Z, Chen JH, Wang X, Kim B, Parajuli R, Mehta RS, Wang M, Su MY. Deep Learning-based Automatic Diagnosis of Breast Cancer on MRI Using Mask R-CNN for Detection Followed by ResNet50 for Classification. Acad Radiol 2023; 30 Suppl 2:S161-S171. [PMID: 36631349 PMCID: PMC10515321 DOI: 10.1016/j.acra.2022.12.038] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 12/10/2022] [Accepted: 12/23/2022] [Indexed: 01/11/2023]
Abstract
RATIONALE AND OBJECTIVES Diagnosis of breast cancer on MRI requires, first, the identification of suspicious lesions; second, the characterization to give a diagnostic impression. We implemented Mask Reginal-Convolutional Neural Network (R-CNN) to detect abnormal lesions, followed by ResNet50 to estimate the malignancy probability. MATERIALS AND METHODS Two datasets were used. The first set had 176 cases, 103 cancer, and 73 benign. The second set had 84 cases, 53 cancer, and 31 benign. For detection, the pre-contrast image and the subtraction images of left and right breasts were used as inputs, so the symmetry could be considered. The detected suspicious area was characterized by ResNet50, using three DCE parametric maps as inputs. The results obtained using slice-based analyses were combined to give a lesion-based diagnosis. RESULTS In the first dataset, 101 of 103 cancers were detected by Mask R-CNN as suspicious, and 99 of 101 were correctly classified by ResNet50 as cancer, with a sensitivity of 99/103 = 96%. 48 of 73 benign lesions and 131 normal areas were identified as suspicious. Following classification by ResNet50, only 16 benign and 16 normal areas remained as malignant. The second dataset was used for independent testing. The sensitivity was 43/53 = 81%. Of the total of 121 identified non-cancerous lesions, only 6 of 31 benign lesions and 22 normal tissues were classified as malignant. CONCLUSION ResNet50 could eliminate approximately 80% of false positives detected by Mask R-CNN. Combining Mask R-CNN and ResNet50 has the potential to develop a fully-automatic computer-aided diagnostic system for breast cancer on MRI.
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Affiliation(s)
- Yang Zhang
- Department of Radiological Sciences, University of California, Irvine, California; Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, New Jersey
| | - Yan-Lin Liu
- Department of Radiological Sciences, University of California, Irvine, California
| | - Ke Nie
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, New Jersey
| | - Jiejie Zhou
- Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Zhongwei Chen
- Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Jeon-Hor Chen
- Department of Radiological Sciences, University of California, Irvine, California; Department of Radiology, E-Da Hospital and I-Shou University, Kaohsiung, Taiwan
| | - Xiao Wang
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, New Jersey
| | - Bomi Kim
- Department of Radiological Sciences, University of California, Irvine, California; Department of Breast Radiology, Ilsan Hospital, Goyang, South Korea
| | - Ritesh Parajuli
- Department of Medicine, University of California, Irvine, United States
| | - Rita S Mehta
- Department of Medicine, University of California, Irvine, United States
| | - Meihao Wang
- Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Min-Ying Su
- Department of Radiological Sciences, University of California, Irvine, California; Department of Medical Imaging and Radiological Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan.
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Zhang M, Wang C, Cai L, Zhao J, Xu Y, Xing J, Sun J, Zhang Y. Developing a weakly supervised deep learning framework for breast cancer diagnosis with HR status based on mammography images. Comput Struct Biotechnol J 2023; 22:17-26. [PMID: 37655162 PMCID: PMC10465855 DOI: 10.1016/j.csbj.2023.08.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 07/29/2023] [Accepted: 08/15/2023] [Indexed: 09/02/2023] Open
Abstract
The status of hormone receptors (HR) at the molecular level is crucial for accurate diagnosis and effective treatment of breast cancer. Meanwhile, mammography is an effective screening method for detecting breast cancer, which significantly improve survival. However, diagnosing the molecular status of breast cancer involves a pathological biopsy, which can affect the accuracy of the diagnosis. To non-invasively diagnose the hormone receptor (HR) status of breast cancer and reduced manual annotation, we proposed a weakly supervised deep learning framework BSNet which detected breast cancer with HR status and benign tumors. BSNet was trained on 2321 multi-view mammography cases from female undergoing digital mammography for the general population at Harbin Medical University Cancer Hospital in Heilongjiang Province during the period 2017-2018 and was validated on the external cohort. The average AUCs of BSNet on the test set and the external validation set were 0.89 and 0.92, respectively. BSNet demonstrated excellent performance in non-invasive breast cancer diagnosis with HR status, using multiple mammography views without pixel annotation. Furthermore, we developed a web server (http://bsnet.edbc.org) for easy use. BSNet described high-dimensional mammography of breast cancer subtypes, which helped inform early management options.
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Affiliation(s)
- Mengyan Zhang
- School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Cong Wang
- School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Li Cai
- The Fourth Department of Medical Oncology, Harbin Medical University Cancer Hospital, 150040, China
| | - Jiyun Zhao
- School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Ye Xu
- Department of Radiology, Harbin Medical University Cancer Hospital, Harbin 150081, China
| | - Jiacheng Xing
- Beidahuang Industry Group General Hospital, 150060 Harbin, China
| | - Jianghong Sun
- Department of Radiology, Harbin Medical University Cancer Hospital, Harbin 150081, China
| | - Yan Zhang
- School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
- College of pathology, Qiqihar Medical University, Qiqihar 161042, China
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39
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Adam R, Dell'Aquila K, Hodges L, Maldjian T, Duong TQ. Deep learning applications to breast cancer detection by magnetic resonance imaging: a literature review. Breast Cancer Res 2023; 25:87. [PMID: 37488621 PMCID: PMC10367400 DOI: 10.1186/s13058-023-01687-4] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 07/11/2023] [Indexed: 07/26/2023] Open
Abstract
Deep learning analysis of radiological images has the potential to improve diagnostic accuracy of breast cancer, ultimately leading to better patient outcomes. This paper systematically reviewed the current literature on deep learning detection of breast cancer based on magnetic resonance imaging (MRI). The literature search was performed from 2015 to Dec 31, 2022, using Pubmed. Other database included Semantic Scholar, ACM Digital Library, Google search, Google Scholar, and pre-print depositories (such as Research Square). Articles that were not deep learning (such as texture analysis) were excluded. PRISMA guidelines for reporting were used. We analyzed different deep learning algorithms, methods of analysis, experimental design, MRI image types, types of ground truths, sample sizes, numbers of benign and malignant lesions, and performance in the literature. We discussed lessons learned, challenges to broad deployment in clinical practice and suggested future research directions.
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Affiliation(s)
- Richard Adam
- Department of Radiology, Albert Einstein College of Medicine and the Montefiore Medical Center, 1300 Morris Park Avenue, Bronx, NY, 10461, USA
| | - Kevin Dell'Aquila
- Department of Radiology, Albert Einstein College of Medicine and the Montefiore Medical Center, 1300 Morris Park Avenue, Bronx, NY, 10461, USA
| | - Laura Hodges
- Department of Radiology, Albert Einstein College of Medicine and the Montefiore Medical Center, 1300 Morris Park Avenue, Bronx, NY, 10461, USA
| | - Takouhie Maldjian
- Department of Radiology, Albert Einstein College of Medicine and the Montefiore Medical Center, 1300 Morris Park Avenue, Bronx, NY, 10461, USA
| | - Tim Q Duong
- Department of Radiology, Albert Einstein College of Medicine and the Montefiore Medical Center, 1300 Morris Park Avenue, Bronx, NY, 10461, USA.
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Zafar A, Tanveer J, Ali MU, Lee SW. BU-DLNet: Breast Ultrasonography-Based Cancer Detection Using Deep-Learning Network Selection and Feature Optimization. Bioengineering (Basel) 2023; 10:825. [PMID: 37508852 PMCID: PMC10376009 DOI: 10.3390/bioengineering10070825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 07/04/2023] [Accepted: 07/09/2023] [Indexed: 07/30/2023] Open
Abstract
Early detection of breast lesions and distinguishing between malignant and benign lesions are critical for breast cancer (BC) prognosis. Breast ultrasonography (BU) is an important radiological imaging modality for the diagnosis of BC. This study proposes a BU image-based framework for the diagnosis of BC in women. Various pre-trained networks are used to extract the deep features of the BU images. Ten wrapper-based optimization algorithms, including the marine predator algorithm, generalized normal distribution optimization, slime mold algorithm, equilibrium optimizer (EO), manta-ray foraging optimization, atom search optimization, Harris hawks optimization, Henry gas solubility optimization, path finder algorithm, and poor and rich optimization, were employed to compute the optimal subset of deep features using a support vector machine classifier. Furthermore, a network selection algorithm was employed to determine the best pre-trained network. An online BU dataset was used to test the proposed framework. After comprehensive testing and analysis, it was found that the EO algorithm produced the highest classification rate for each pre-trained model. It produced the highest classification accuracy of 96.79%, and it was trained using only a deep feature vector with a size of 562 in the ResNet-50 model. Similarly, the Inception-ResNet-v2 had the second highest classification accuracy of 96.15% using the EO algorithm. Moreover, the results of the proposed framework are compared with those in the literature.
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Affiliation(s)
- Amad Zafar
- Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Republic of Korea
| | - Jawad Tanveer
- Department of Computer Science and Engineering, Sejong University, Seoul 05006, Republic of Korea
| | - Muhammad Umair Ali
- Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Republic of Korea
| | - Seung Won Lee
- Department of Precision Medicine, School of Medicine, Sungkyunkwan University, Suwon 16419, Republic of Korea
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41
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Fatima GN, Fatma H, Saraf SK. Vaccines in Breast Cancer: Challenges and Breakthroughs. Diagnostics (Basel) 2023; 13:2175. [PMID: 37443570 DOI: 10.3390/diagnostics13132175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 06/09/2023] [Accepted: 06/21/2023] [Indexed: 07/15/2023] Open
Abstract
Breast cancer is a problem for women's health globally. Early detection techniques come in a variety of forms ranging from local to systemic and from non-invasive to invasive. The treatment of cancer has always been challenging despite the availability of a wide range of therapeutics. This is either due to the variable behaviour and heterogeneity of the proliferating cells and/or the individual's response towards the treatment applied. However, advancements in cancer biology and scientific technology have changed the course of the cancer treatment approach. This current review briefly encompasses the diagnostics, the latest and most recent breakthrough strategies and challenges, and the limitations in fighting breast cancer, emphasising the development of breast cancer vaccines. It also includes the filed/granted patents referring to the same aspects.
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Affiliation(s)
- Gul Naz Fatima
- Division of Pharmaceutical Chemistry, Faculty of Pharmacy, Babu Banarasi Das Northern India Institute of Technology, Lucknow 226028, Uttar Pradesh, India
| | - Hera Fatma
- Division of Pharmaceutical Chemistry, Faculty of Pharmacy, Babu Banarasi Das Northern India Institute of Technology, Lucknow 226028, Uttar Pradesh, India
| | - Shailendra K Saraf
- Division of Pharmaceutical Chemistry, Faculty of Pharmacy, Babu Banarasi Das Northern India Institute of Technology, Lucknow 226028, Uttar Pradesh, India
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Zhang R, Wang P, Bian Y, Fan Y, Li J, Liu X, Shen J, Hu Y, Liao X, Wang H, Song C, Li W, Wang X, Sun M, Zhang J, Wang M, Wang S, Shen Y, Zhang X, Jia Q, Tan J, Li N, Wang S, Xu L, Wu W, Zhang W, Meng Z. Establishment and validation of an AI-aid method in the diagnosis of myocardial perfusion imaging. BMC Med Imaging 2023; 23:84. [PMID: 37328753 PMCID: PMC10273563 DOI: 10.1186/s12880-023-01037-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 05/29/2023] [Indexed: 06/18/2023] Open
Abstract
BACKGROUND This study aimed to develop and validate an AI (artificial intelligence)-aid method in myocardial perfusion imaging (MPI) to differentiate ischemia in coronary artery disease. METHODS We retrospectively selected 599 patients who had received gated-MPI protocol. Images were acquired using hybrid SPECT-CT systems. A training set was used to train and develop the neural network and a validation set was used to test the predictive ability of the neural network. We used a learning technique named "YOLO" to carry out the training process. We compared the predictive accuracy of AI with that of physician interpreters (beginner, inexperienced, and experienced interpreters). RESULTS Training performance showed that the accuracy ranged from 66.20% to 94.64%, the recall rate ranged from 76.96% to 98.76%, and the average precision ranged from 80.17% to 98.15%. In the ROC analysis of the validation set, the sensitivity range was 88.9 ~ 93.8%, the specificity range was 93.0 ~ 97.6%, and the AUC range was 94.1 ~ 96.1%. In the comparison between AI and different interpreters, AI outperformed the other interpreters (most P-value < 0.05). CONCLUSION The AI system of our study showed excellent predictive accuracy in the diagnosis of MPI protocols, and therefore might be potentially helpful to aid radiologists in clinical practice and develop more sophisticated models.
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Affiliation(s)
- Ruyi Zhang
- Department of Nuclear Medicine, Tianjin Medical University General Hospital, Anshan Road No. 154, Heping District, Tianjin, China, 300052
| | - Peng Wang
- Department of Nuclear Medicine, Tianjin Medical University General Hospital, Anshan Road No. 154, Heping District, Tianjin, China, 300052
| | - Yanzhu Bian
- Department of Nuclear Medicine, Hebei General Hospital, Shijiazhuang, China
| | - Yan Fan
- Department of Nuclear Medicine, Peking University First Hospital, Beijing, China
| | - Jianming Li
- Department of Nuclear Medicine, Teda International Cardiovascular Hospital, Tianjin, China
| | - Xuehui Liu
- Department of Nuclear Medicine, Tianjin Third Central Hospital, Tianjin, China
| | - Jie Shen
- Department of Nuclear Medicine, Tianjin First Central Hospital, Tianjin, China
| | - Yujing Hu
- Department of Nuclear Medicine, Hebei General Hospital, Shijiazhuang, China
| | - Xianghe Liao
- Department of Nuclear Medicine, Peking University First Hospital, Beijing, China
| | - He Wang
- School of Microelectronics, Tianjin University, Weijin Road No. 92, Nankai District, Tianjin, China, 300072
| | - Chengyu Song
- School of Microelectronics, Tianjin University, Weijin Road No. 92, Nankai District, Tianjin, China, 300072
| | - Wangxiao Li
- School of Microelectronics, Tianjin University, Weijin Road No. 92, Nankai District, Tianjin, China, 300072
| | - Xiaojie Wang
- Department of Nuclear Medicine, Teda International Cardiovascular Hospital, Tianjin, China
| | - Momo Sun
- Department of Nuclear Medicine, Tianjin First Central Hospital, Tianjin, China
| | - Jianping Zhang
- Department of Nuclear Medicine, Tianjin Third Central Hospital, Tianjin, China
| | - Miao Wang
- Department of Nuclear Medicine, Tianjin Medical University General Hospital, Anshan Road No. 154, Heping District, Tianjin, China, 300052
| | - Shen Wang
- Department of Nuclear Medicine, Tianjin Medical University General Hospital, Anshan Road No. 154, Heping District, Tianjin, China, 300052
| | - Yiming Shen
- Department of Nuclear Medicine, Tianjin Medical University General Hospital, Anshan Road No. 154, Heping District, Tianjin, China, 300052
| | - Xuemei Zhang
- Department of Nuclear Medicine, Tianjin Medical University General Hospital, Anshan Road No. 154, Heping District, Tianjin, China, 300052
| | - Qiang Jia
- Department of Nuclear Medicine, Tianjin Medical University General Hospital, Anshan Road No. 154, Heping District, Tianjin, China, 300052
| | - Jian Tan
- Department of Nuclear Medicine, Tianjin Medical University General Hospital, Anshan Road No. 154, Heping District, Tianjin, China, 300052
| | - Ning Li
- Department of Nuclear Medicine, Tianjin Medical University General Hospital, Anshan Road No. 154, Heping District, Tianjin, China, 300052
| | - Sen Wang
- Department of Nuclear Medicine, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Lingyun Xu
- Department of Nuclear Medicine, Tianjin Medical University General Hospital, Anshan Road No. 154, Heping District, Tianjin, China, 300052
| | - Weiming Wu
- Department of Nuclear Medicine, Tianjin Medical University General Hospital, Anshan Road No. 154, Heping District, Tianjin, China, 300052
| | - Wei Zhang
- School of Microelectronics, Tianjin University, Weijin Road No. 92, Nankai District, Tianjin, China, 300072.
| | - Zhaowei Meng
- Department of Nuclear Medicine, Tianjin Medical University General Hospital, Anshan Road No. 154, Heping District, Tianjin, China, 300052.
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Ying W. Phenomic Studies on Diseases: Potential and Challenges. PHENOMICS (CHAM, SWITZERLAND) 2023; 3:285-299. [PMID: 36714223 PMCID: PMC9867904 DOI: 10.1007/s43657-022-00089-4] [Citation(s) in RCA: 39] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Revised: 11/21/2022] [Accepted: 11/24/2022] [Indexed: 01/23/2023]
Abstract
The rapid development of such research field as multi-omics and artificial intelligence (AI) has made it possible to acquire and analyze the multi-dimensional big data of human phenomes. Increasing evidence has indicated that phenomics can provide a revolutionary strategy and approach for discovering new risk factors, diagnostic biomarkers and precision therapies of diseases, which holds profound advantages over conventional approaches for realizing precision medicine: first, the big data of patients' phenomes can provide remarkably richer information than that of the genomes; second, phenomic studies on diseases may expose the correlations among cross-scale and multi-dimensional phenomic parameters as well as the mechanisms underlying the correlations; and third, phenomics-based studies are big data-driven studies, which can significantly enhance the possibility and efficiency for generating novel discoveries. However, phenomic studies on human diseases are still in early developmental stage, which are facing multiple major challenges and tasks: first, there is significant deficiency in analytical and modeling approaches for analyzing the multi-dimensional data of human phenomes; second, it is crucial to establish universal standards for acquirement and management of phenomic data of patients; third, new methods and devices for acquirement of phenomic data of patients under clinical settings should be developed; fourth, it is of significance to establish the regulatory and ethical guidelines for phenomic studies on diseases; and fifth, it is important to develop effective international cooperation. It is expected that phenomic studies on diseases would profoundly and comprehensively enhance our capacity in prevention, diagnosis and treatment of diseases.
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Affiliation(s)
- Weihai Ying
- Med-X Research Institute and School of Biomedical Engineering, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai, 200030 China
- Collaborative Innovation Center for Genetics and Development, Shanghai, 200043 China
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Odhiambo P, Okello H, Wakaanya A, Wekesa C, Okoth P. Mutational signatures for breast cancer diagnosis using artificial intelligence. J Egypt Natl Canc Inst 2023; 35:14. [PMID: 37184779 DOI: 10.1186/s43046-023-00173-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Accepted: 04/19/2023] [Indexed: 05/16/2023] Open
Abstract
BACKGROUND Breast cancer is the most common female cancer worldwide. Its diagnosis and prognosis remain scanty, imprecise, and poorly documented. Previous studies have indicated that some genetic mutational signatures are suspected to lead to progression of various breast cancer scenarios. There is paucity of data on the role of AI tools in delineating breast cancer mutational signatures. This study sought to investigate the relationship between breast cancer genetic mutational profiles using artificial intelligence models with a view to developing an accurate prognostic prediction based on breast cancer genetic signatures. Prior research on breast cancer has been based on symptoms, origin, and tumor size. It has not been investigated whether diagnosis of breast cancer can be made utilizing AI platforms like Cytoscape, Phenolyzer, and Geneshot with potential for better prognostic power. This is the first ever attempt for a combinatorial approach to breast cancer diagnosis using different AI platforms. METHOD Artificial intelligence (AI) are mathematical algorithms that simulate human cognitive abilities and solve difficult healthcare issues such as complicated biological abnormalities like those experienced in breast cancer scenarios. The current models aimed to predict outcomes and prognosis by correlating imaging phenotypes with genetic mutations, tumor profiles, and hormone receptor status and development of imaging biomarkers that combine tumor and patient-specific features. Geneshotsav 2021, Cytoscape 3.9.1, and Phenolyzer Nature Methods, 12:841-843 (2015) tools, were used to mine breast cancer-associated mutational signatures and provided useful alternative computational tools for discerning pathways and enriched networks of genes of similarity with the overall goal of providing a systematic view of the variety of mutational processes that lead to breast cancer development. The development of novel-tailored pharmaceuticals, as well as the distribution of prospective treatment alternatives, would be aided by the collection of massive datasets and the use of such tools as diagnostic markers. RESULTS Specific DNA-maintenance defects, endogenous or environmental exposures, and cancer genomic signatures are connected. The PubMed database (Geneshot) search for the keywords yielded a total of 21,921 genes associated with breast cancer. Then, based on their propensity to result in gene mutations, the genes were screened using the Phenolyzer software. These platforms lend credence to the fact that breast cancer diagnosis using Cytoscape 3.9.1, Phenolyzer, and Geneshot 2021 reveals high profile of the following mutational signatures: BRCA1, BRCA2, TP53, CHEK2, PTEN, CDH1, BRIP1, RAD51C, CASP3, CREBBP, and SMAD3.
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Affiliation(s)
- Patrick Odhiambo
- Department of Biological Sciences, School of Natural and Applied Sciences, Masinde Muliro University of Science and Technology, P.O. Box 190, Kakamega, 50100, Kenya.
| | - Harrison Okello
- Department of Biological Sciences, School of Natural and Applied Sciences, Masinde Muliro University of Science and Technology, P.O. Box 190, Kakamega, 50100, Kenya
| | - Annette Wakaanya
- Department of Mathematics, School of Natural and Applied Sciences, Masinde Muliro University of Science and Technology, P.O. Box 190, Kakamega, 50100, Kenya
| | - Clabe Wekesa
- Department of Biological Sciences, School of Natural and Applied Sciences, Masinde Muliro University of Science and Technology, P.O. Box 190, Kakamega, 50100, Kenya
| | - Patrick Okoth
- Department of Biological Sciences, School of Natural and Applied Sciences, Masinde Muliro University of Science and Technology, P.O. Box 190, Kakamega, 50100, Kenya
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Wang W, Wang Y. Deep Learning-Based Modified YOLACT Algorithm on Magnetic Resonance Imaging Images for Screening Common and Difficult Samples of Breast Cancer. Diagnostics (Basel) 2023; 13:diagnostics13091582. [PMID: 37174975 PMCID: PMC10177566 DOI: 10.3390/diagnostics13091582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 03/27/2023] [Accepted: 04/09/2023] [Indexed: 05/15/2023] Open
Abstract
Computer-aided methods have been extensively applied for diagnosing breast lesions with magnetic resonance imaging (MRI), but fully-automatic diagnosis using deep learning is rarely documented. Deep-learning-technology-based artificial intelligence (AI) was used in this work to classify and diagnose breast cancer based on MRI images. Breast cancer MRI images from the Rider Breast MRI public dataset were converted into processable joint photographic expert group (JPG) format images. The location and shape of the lesion area were labeled using the Labelme software. A difficult-sample mining mechanism was introduced to improve the performance of the YOLACT algorithm model as a modified YOLACT algorithm model. Diagnostic efficacy was compared with the Mask R-CNN algorithm model. The deep learning framework was based on PyTorch version 1.0. Four thousand and four hundred labeled data with corresponding lesions were labeled as normal samples, and 1600 images with blurred lesion areas as difficult samples. The modified YOLACT algorithm model achieved higher accuracy and better classification performance than the YOLACT model. The detection accuracy of the modified YOLACT algorithm model with the difficult-sample-mining mechanism is improved by nearly 3% for common and difficult sample images. Compared with Mask R-CNN, it is still faster in running speed, and the difference in recognition accuracy is not obvious. The modified YOLACT algorithm had a classification accuracy of 98.5% for the common sample test set and 93.6% for difficult samples. We constructed a modified YOLACT algorithm model, which is superior to the YOLACT algorithm model in diagnosis and classification accuracy.
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Affiliation(s)
- Wei Wang
- College of Computer Science and Technology, Guizhou University, Guiyang 550001, China
- Institute for Artificial Intelligence, Guizhou University, Guiyang 550001, China
- Guizhou Provincial People's Hospital, Guiyang 550001, China
| | - Yisong Wang
- College of Computer Science and Technology, Guizhou University, Guiyang 550001, China
- Institute for Artificial Intelligence, Guizhou University, Guiyang 550001, China
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Puchnin V, Jandaliyeva A, Hurshkainen A, Solomakha G, Nikulin A, Petrova P, Lavrenteva A, Andreychenko A, Shchelokova A. Quadrature transceive wireless coil: Design concept and application for bilateral breast MRI at 1.5 T. Magn Reson Med 2023; 89:1251-1264. [PMID: 36336799 DOI: 10.1002/mrm.29507] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 09/20/2022] [Accepted: 10/09/2022] [Indexed: 11/09/2022]
Abstract
PURPOSE Development of a novel quadrature inductively driven transceive wireless coil for breast MRI at 1.5 T. METHODS A quadrature wireless coil (HHMM-coil) design has been developed as a combination of two linearly polarized coils: a pair of 'metasolenoid' coils (MM-coil) and a pair of Helmholtz-type coils (HH-coil). The MM-coil consisted of an array of split-loop resonators. The HH-coil design included two electrically connected flat spirals. All the wireless coils were coupled to a whole-body birdcage coil. The HHMM-coil was studied and compared to the linear coils in terms of transmit and SAR efficiencies via numerical simulations. A prototype of HHMM-coil was built and tested on a 1.5 T scanner in a phantom and healthy volunteer. We also proposed an extended design of the HHMM-coil and compared its performance to a dedicated breast array. RESULTS Numerical simulations of the HHMM-coil with a female voxel model have shown more than a 2.5-fold increase in transmit efficiency and a 1.7-fold enhancement of SAR efficiency compared to the linearly polarized coils. Phantom and in vivo imaging showed good agreement with the numerical simulations. Moreover, the HHMM-coil provided good image quality, visualizing all areas of interest similar to a multichannel breast array with a 32% reduction in signal-to-noise ratio. CONCLUSION The proposed quadrature HHMM-coil allows the B 1 + $$ {\mathrm{B}}_1^{+} $$ -field to be significantly better focused in the region-of-interest compared to the linearly polarized coils. Thus, the HHMM-coil provides high-quality breast imaging on a 1.5 T scanner using a whole-body birdcage coil for transmit and receive.
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Affiliation(s)
- Viktor Puchnin
- School of Physics and Engineering, ITMO University, St. Petersburg, Russia
| | | | - Anna Hurshkainen
- School of Physics and Engineering, ITMO University, St. Petersburg, Russia
| | - Georgiy Solomakha
- School of Physics and Engineering, ITMO University, St. Petersburg, Russia
| | - Anton Nikulin
- School of Physics and Engineering, ITMO University, St. Petersburg, Russia
| | - Polina Petrova
- School of Physics and Engineering, ITMO University, St. Petersburg, Russia
| | - Anna Lavrenteva
- Medical Institute named after Berezin Sergey (MIBS), St. Petersburg, Russia
| | - Anna Andreychenko
- School of Physics and Engineering, ITMO University, St. Petersburg, Russia.,Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies, Moscow Health Care Department, Moscow, Russia
| | - Alena Shchelokova
- School of Physics and Engineering, ITMO University, St. Petersburg, Russia
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Pesapane F, De Marco P, Rapino A, Lombardo E, Nicosia L, Tantrige P, Rotili A, Bozzini AC, Penco S, Dominelli V, Trentin C, Ferrari F, Farina M, Meneghetti L, Latronico A, Abbate F, Origgi D, Carrafiello G, Cassano E. How Radiomics Can Improve Breast Cancer Diagnosis and Treatment. J Clin Med 2023; 12:jcm12041372. [PMID: 36835908 PMCID: PMC9963325 DOI: 10.3390/jcm12041372] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 02/04/2023] [Accepted: 02/06/2023] [Indexed: 02/11/2023] Open
Abstract
Recent technological advances in the field of artificial intelligence hold promise in addressing medical challenges in breast cancer care, such as early diagnosis, cancer subtype determination and molecular profiling, prediction of lymph node metastases, and prognostication of treatment response and probability of recurrence. Radiomics is a quantitative approach to medical imaging, which aims to enhance the existing data available to clinicians by means of advanced mathematical analysis using artificial intelligence. Various published studies from different fields in imaging have highlighted the potential of radiomics to enhance clinical decision making. In this review, we describe the evolution of AI in breast imaging and its frontiers, focusing on handcrafted and deep learning radiomics. We present a typical workflow of a radiomics analysis and a practical "how-to" guide. Finally, we summarize the methodology and implementation of radiomics in breast cancer, based on the most recent scientific literature to help researchers and clinicians gain fundamental knowledge of this emerging technology. Alongside this, we discuss the current limitations of radiomics and challenges of integration into clinical practice with conceptual consistency, data curation, technical reproducibility, adequate accuracy, and clinical translation. The incorporation of radiomics with clinical, histopathological, and genomic information will enable physicians to move forward to a higher level of personalized management of patients with breast cancer.
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Affiliation(s)
- Filippo Pesapane
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
- Correspondence: ; Tel.: +39-02-574891
| | - Paolo De Marco
- Medical Physics Unit, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Anna Rapino
- Postgraduation School in Radiodiagnostics, University of Milan, 20122 Milan, Italy
| | - Eleonora Lombardo
- UOC of Diagnostic Imaging, Policlinico Tor Vergata University, 00133 Rome, Italy
| | - Luca Nicosia
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Priyan Tantrige
- Department of Radiology, King’s College Hospital NHS Foundation Trust, London SE5 9RS, UK
| | - Anna Rotili
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Anna Carla Bozzini
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Silvia Penco
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Valeria Dominelli
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Chiara Trentin
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Federica Ferrari
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Mariagiorgia Farina
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Lorenza Meneghetti
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Antuono Latronico
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Francesca Abbate
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Daniela Origgi
- Medical Physics Unit, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Gianpaolo Carrafiello
- Department of Radiology, IRCCS Foundation Ca’ Granda Ospedale Maggiore Policlinico, 20122 Milan, Italy
- Department of Health Sciences, University of Milan, 20122 Milan, Italy
| | - Enrico Cassano
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
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48
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AlZaabi A, AlMaskari S, AalAbdulsalam A. Are physicians and medical students ready for artificial intelligence applications in healthcare? Digit Health 2023; 9:20552076231152167. [PMID: 36762024 PMCID: PMC9903019 DOI: 10.1177/20552076231152167] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 01/03/2023] [Indexed: 01/28/2023] Open
Abstract
Background Artificial intelligence (AI) Healthcare applications are listed in the national visions of some Gulf Cooperation Council countries. A successful use of AI depends on the attitude and perception of medical experts of its applications. Objective To evaluate physicians and medical students' attitude and perception on AI applications in healthcare. Method A web-based survey was disseminated by email to physicians and medical students. Results A total of 293 (82 physicians and 211 medical students) individuals have participated (response rate is 27%). Seven participants (9%) reported knowing nothing about AI, while 208 (69%) were aware that it is an emerging field and would like to learn about it. Concerns about AI impact on physicians' employability were not prominent. Instead, the majority (n=159) agreed that new positions will be created and the job market for those who embrace AI will increase. They reported willingness to adapt AI in practice if it was incorporated in international guidelines (30.5%), published in respected scientific journals (17.1%), or included in formal training (12.2%). Almost two of the three participants agreed that dedicated courses will help them to implement AI. The most commonly reported problem of AI is its inability to provide opinions in unexpected scenarios. Half of the participants think that both the manufacturer and physicians should be legally liable for medical errors occur due to AI-based decision support tools while more than one-third (36.77%) think that physicians who make the final decision should be legally liable. Senior physicians were found to be less familiar with AI and more concerned about physicians' legal liability in case of a medical error. Conclusion Physicians and medical students showed positive attitudes and willingness to learn about AI applications in healthcare. Introducing AI learning objectives or short courses in medical curriculum would help to equip physicians with the needed skills for AI-augmented healthcare system.
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Affiliation(s)
- Adhari AlZaabi
- Human and Clinical Anatomy Department, College of Medicine and Health Science, Muscat, Sultanate of Oman
| | - Saleh AlMaskari
- Human and Clinical Anatomy Department, College of Medicine and Health Science, Muscat, Sultanate of Oman
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Artificial Intelligence (AI) in Breast Imaging: A Scientometric Umbrella Review. Diagnostics (Basel) 2022; 12:diagnostics12123111. [PMID: 36553119 PMCID: PMC9777253 DOI: 10.3390/diagnostics12123111] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 12/07/2022] [Accepted: 12/08/2022] [Indexed: 12/14/2022] Open
Abstract
Artificial intelligence (AI), a rousing advancement disrupting a wide spectrum of applications with remarkable betterment, has continued to gain momentum over the past decades. Within breast imaging, AI, especially machine learning and deep learning, honed with unlimited cross-data/case referencing, has found great utility encompassing four facets: screening and detection, diagnosis, disease monitoring, and data management as a whole. Over the years, breast cancer has been the apex of the cancer cumulative risk ranking for women across the six continents, existing in variegated forms and offering a complicated context in medical decisions. Realizing the ever-increasing demand for quality healthcare, contemporary AI has been envisioned to make great strides in clinical data management and perception, with the capability to detect indeterminate significance, predict prognostication, and correlate available data into a meaningful clinical endpoint. Here, the authors captured the review works over the past decades, focusing on AI in breast imaging, and systematized the included works into one usable document, which is termed an umbrella review. The present study aims to provide a panoramic view of how AI is poised to enhance breast imaging procedures. Evidence-based scientometric analysis was performed in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guideline, resulting in 71 included review works. This study aims to synthesize, collate, and correlate the included review works, thereby identifying the patterns, trends, quality, and types of the included works, captured by the structured search strategy. The present study is intended to serve as a "one-stop center" synthesis and provide a holistic bird's eye view to readers, ranging from newcomers to existing researchers and relevant stakeholders, on the topic of interest.
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Ming W, Li F, Zhu Y, Bai Y, Gu W, Liu Y, Sun X, Liu X, Liu H. Predicting hormone receptors and PAM50 subtypes of breast cancer from multi-scale lesion images of DCE-MRI with transfer learning technique. Comput Biol Med 2022; 150:106147. [PMID: 36201887 DOI: 10.1016/j.compbiomed.2022.106147] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 09/06/2022] [Accepted: 09/24/2022] [Indexed: 11/03/2022]
Abstract
BACKGROUND The recent development of artificial intelligence (AI) technologies coupled with medical imaging data has gained considerable attention, and offers a non-invasive approach for cancer diagnosis and prognosis. In this context, improved breast cancer (BC) molecular characteristics assessment models are foreseen to enable personalized strategies with better clinical outcomes compared to existing screening strategies. And it is a promising approach to developing models for hormone receptors (HR) and subtypes of BC patients from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data. METHODS In this institutional review board-approved study, 174 BC patients with both DCE-MRI and RNA-seq data in the local database were analyzed. Slice images from tumor lesions and multi-scale peri-tumor regions were used as model inputs, and five representative pre-trained transfer learning (TF) networks, such as Inception-v3 and Xception, were employed to establish prediction models. A comprehensive analysis was performed using five-fold cross-validation to avoid overfitting, and accuracy (ACC) and area under the receiver operating characteristic curve (AUROC) to evaluate model performance. RESULTS Xception achieved the superior results when using solely tumor regions, with highest AUROCs of 0.844 (95% CI: [0.841, 0.847]) and 0.784 (95% CI: [0.781, 0.788]) for estrogen receptor (ER) and progesterone receptor (PR), respectively, and best ACC of 0.467 (95% CI: [0.462, 0.470]) for PAM50 subtypes. A significant improvement in the model performance was observed when images of the peri-tumor region were included, with optimal results achieved using images of the tumor and the 10 mm peri-tumor regions. Xception-based TF models performed most effectively in predicting ER and PR statuses, with the AUROCs were 0.942 (95% CI: [0.940, 0.944]) and 0.920 (95% CI: [0.917, 0.922]), respectively, whereas for PAM50 subtypes, the Inception-v3-based network yielded the highest ACC as 0.742 (95% CI: [0.738, 0.746]). CONCLUSIONS Transfer learning analysis based on DCE-MRI data of tumor and peri-tumor regions was helpful to the non-invasive assessment of molecular characteristics of BC.
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Affiliation(s)
- Wenlong Ming
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, PR China
| | - Fuyu Li
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, PR China
| | - Yanhui Zhu
- Department of Breast Surgery, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, PR China
| | - Yunfei Bai
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, PR China
| | - Wanjun Gu
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, PR China; Collaborative Innovation Center of Jiangsu Province of Cancer Prevention and Treatment of Chinese Medicine, School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing, 210023, PR China
| | - Yun Liu
- Department of Information, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, PR China
| | - Xiao Sun
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, PR China
| | - Xiaoan Liu
- Department of Breast Surgery, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, PR China.
| | - Hongde Liu
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, PR China.
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