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1
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Farinella R, Felici A, Peduzzi G, Testoni SGG, Costello E, Aretini P, Blazquez-Encinas R, Oz E, Pastore A, Tacelli M, Otlu B, Campa D, Gentiluomo M. From classical approaches to artificial intelligence, old and new tools for PDAC risk stratification and prediction. Semin Cancer Biol 2025; 112:71-92. [PMID: 40147701 DOI: 10.1016/j.semcancer.2025.03.004] [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: 11/28/2024] [Revised: 03/08/2025] [Accepted: 03/19/2025] [Indexed: 03/29/2025]
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is recognized as one of the most lethal malignancies, characterized by late-stage diagnosis and limited therapeutic options. Risk stratification has traditionally been performed using epidemiological studies and genetic analyses, through which key risk factors, including smoking, diabetes, chronic pancreatitis, and inherited predispositions, have been identified. However, the multifactorial nature of PDAC has often been insufficiently addressed by these methods, leading to limited precision in individualized risk assessments. Advances in artificial intelligence (AI) have been proposed as a transformative approach, allowing the integration of diverse datasets-spanning genetic, clinical, lifestyle, and imaging data into dynamic models capable of uncovering novel interactions and risk profiles. In this review, the evolution of PDAC risk stratification is explored, with classical epidemiological frameworks compared to AI-driven methodologies. Genetic insights, including genome-wide association studies and polygenic risk scores, are discussed, alongside AI models such as machine learning, radiomics, and deep learning. Strengths and limitations of these approaches are evaluated, with challenges in clinical translation, such as data scarcity, model interpretability, and external validation, addressed. Finally, future directions are proposed for combining classical and AI-driven methodologies to develop scalable, personalized predictive tools for PDAC, with the goal of improving early detection and patient outcomes.
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Affiliation(s)
| | | | | | - Sabrina Gloria Giulia Testoni
- Division of Gastroenterology and Gastrointestinal Endoscopy, IRCCS Policlinico San Donato, Vita-Salute San Raffaele University, Milan, Italy
| | - Eithne Costello
- Liverpool Experimental Cancer Medicine Centre, University of Liverpool, Liverpool, United Kingdom
| | - Paolo Aretini
- Fondazione Pisana per la Scienza, San Giuliano Terme, Italy
| | - Ricardo Blazquez-Encinas
- Department of Cell Biology, Physiology and Immunology, University of Cordoba / Maimonides Biomedical Research Institute of Cordoba (IMIBIC), Cordoba, Spain
| | - Elif Oz
- Department of Biostatistics and Bioinformatics, Institute of Health Sciences, Acibadem Mehmet Ali Aydinlar University, Istanbul, Turkey
| | - Aldo Pastore
- Fondazione Pisana per la Scienza, San Giuliano Terme, Italy
| | - Matteo Tacelli
- Pancreas Translational & Clinical Research Center, Pancreato-Biliary Endoscopy and Endosonography Division, San Raffaele Scientific Institute IRCCS, Milan, Italy
| | - Burçak Otlu
- Department of Health Informatics, Graduate School of Informatics, Middle East Technical University, Ankara, Turkey
| | - Daniele Campa
- Department of Biology, University of Pisa, Pisa, Italy
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2
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Wang L, Shi Y, Qureshi TA, Xie Y, Gaddam S, Azab L, Wu C, He Y, Deng Z, Javed S, Diwan G, Vendrami CL, Rodriguez A, Specht K, Jeon CY, Chaudhry H, Buxbaum JL, Pisegna JR, Yaghmai V, Goessling W, Hernandez-Barco YG, Miller FH, Tirkes T, Pandol SJ, Li D. Predicting Pancreatic Ductal Adenocarcinoma Occurrence Up to 10 Years in Advance Using Features of the Main Pancreatic Duct in Pre-Diagnostic CT Scans. Cancers (Basel) 2025; 17:1886. [PMID: 40507365 PMCID: PMC12153928 DOI: 10.3390/cancers17111886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2025] [Revised: 05/12/2025] [Accepted: 06/03/2025] [Indexed: 06/16/2025] Open
Abstract
Background/Objectives: Pancreatic ductal adenocarcinoma (PDAC) prediction in high-risk individuals is essential for early detection and improved outcome. While prior studies have utilized pancreatic radiomics for PDAC prediction, the added value of main pancreatic duct (MPD) features remains unclear. This study aims to assess the additional value of features of the main pancreatic duct (MPD) for predicting PDAC occurrence across different timeframes in advance. Methods: In total, 321 contrast-enhanced CT scans of the MPD and pancreas carried out across control, pre-diagnostic, and diagnostic cohorts were segmented, and radiomics were extracted. A support vector machine (SVM) classifier was used to classify the control and pre-diagnostic cohorts, with model performance assessed using area under the receiver operating characteristic (ROC) curves (AUCs) Results: The MPD diameter and volume significantly increased from the control to the pre-diagnostic and diagnostic CT scans (p < 0.05). The addition of features of the MPD to the pancreas improved the PDAC prediction AUC from 0.83 to 0.96 for subjects 6 months to 3 years in advance, from 0.81 to 0.94 for 3-6 years in advance, and 0.75 to 0.84 for 6-10 years in advance of diagnosis. Additionally, integrating MPD radiomics with diameter and volume significantly improved the AUC from 0.81 to 0.88 for subjects 6 months to 3 years in advance. Conclusions: Radiomic features from abdominal CT scans allow PDAC prediction up to 10 years in advance. Integrating MPD features, including diameter and volume, significantly improves PDAC prediction compared to using radiomics of the pancreas alone.
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Affiliation(s)
- Lixia Wang
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; (L.W.); (Y.S.); (T.A.Q.); (Y.X.); (L.A.); (C.W.); (Y.H.); (Z.D.); (S.J.); (G.D.)
| | - Yu Shi
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; (L.W.); (Y.S.); (T.A.Q.); (Y.X.); (L.A.); (C.W.); (Y.H.); (Z.D.); (S.J.); (G.D.)
- Department of Bioengineering, University of California, Los Angeles, CA 90048, USA
| | - Touseef Ahmad Qureshi
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; (L.W.); (Y.S.); (T.A.Q.); (Y.X.); (L.A.); (C.W.); (Y.H.); (Z.D.); (S.J.); (G.D.)
| | - Yibin Xie
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; (L.W.); (Y.S.); (T.A.Q.); (Y.X.); (L.A.); (C.W.); (Y.H.); (Z.D.); (S.J.); (G.D.)
| | - Srinivas Gaddam
- Karsh Division of Gastroenterology and Hepatology, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA;
| | - Linda Azab
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; (L.W.); (Y.S.); (T.A.Q.); (Y.X.); (L.A.); (C.W.); (Y.H.); (Z.D.); (S.J.); (G.D.)
| | - Chaowei Wu
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; (L.W.); (Y.S.); (T.A.Q.); (Y.X.); (L.A.); (C.W.); (Y.H.); (Z.D.); (S.J.); (G.D.)
- Department of Bioengineering, University of California, Los Angeles, CA 90048, USA
| | - Yimeng He
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; (L.W.); (Y.S.); (T.A.Q.); (Y.X.); (L.A.); (C.W.); (Y.H.); (Z.D.); (S.J.); (G.D.)
| | - Zengtian Deng
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; (L.W.); (Y.S.); (T.A.Q.); (Y.X.); (L.A.); (C.W.); (Y.H.); (Z.D.); (S.J.); (G.D.)
- Department of Bioengineering, University of California, Los Angeles, CA 90048, USA
| | - Sehrish Javed
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; (L.W.); (Y.S.); (T.A.Q.); (Y.X.); (L.A.); (C.W.); (Y.H.); (Z.D.); (S.J.); (G.D.)
| | - Garima Diwan
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; (L.W.); (Y.S.); (T.A.Q.); (Y.X.); (L.A.); (C.W.); (Y.H.); (Z.D.); (S.J.); (G.D.)
| | - Camila Lopes Vendrami
- Department of Radiology, Northwestern Memorial Hospital, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA; (C.L.V.); (F.H.M.)
| | - Alex Rodriguez
- Division of Gastroenterology, Department of Medicine, Keck School of Medicine of the University of Southern California, Los Angeles, CA 90033, USA; (A.R.); (J.L.B.)
| | - Katherine Specht
- Division of Gastroenterology, Massachusetts General Hospital, Boston, MA 02114, USA; (K.S.); (W.G.); (Y.G.H.-B.)
| | - Christie Y. Jeon
- Department of Biomedical Science, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA;
| | - Humaira Chaudhry
- Department of Radiology, Rutgers New Jersey Medical School, Newark, NJ 07103, USA;
| | - James L. Buxbaum
- Division of Gastroenterology, Department of Medicine, Keck School of Medicine of the University of Southern California, Los Angeles, CA 90033, USA; (A.R.); (J.L.B.)
| | - Joseph R. Pisegna
- Division of Gastroenterology, Hepatology and Parenteral Nutrition, Department of Medicine VA Greater Los Angeles Healthcare System, Los Angeles, CA 90073, USA;
| | - Vahid Yaghmai
- Department of Radiological Sciences, University of California, Irvine, CA 92868, USA;
| | - Wolfram Goessling
- Division of Gastroenterology, Massachusetts General Hospital, Boston, MA 02114, USA; (K.S.); (W.G.); (Y.G.H.-B.)
| | - Yasmin G. Hernandez-Barco
- Division of Gastroenterology, Massachusetts General Hospital, Boston, MA 02114, USA; (K.S.); (W.G.); (Y.G.H.-B.)
| | - Frank H. Miller
- Department of Radiology, Northwestern Memorial Hospital, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA; (C.L.V.); (F.H.M.)
| | - Temel Tirkes
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA;
| | - Stephen J. Pandol
- Division of Gastroenterology and Hepatology, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA;
| | - Debiao Li
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; (L.W.); (Y.S.); (T.A.Q.); (Y.X.); (L.A.); (C.W.); (Y.H.); (Z.D.); (S.J.); (G.D.)
- Department of Bioengineering, University of California, Los Angeles, CA 90048, USA
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Mukherjee S, Antony A, Patnam NG, Trivedi KH, Karbhari A, Nagaraj M, Murlidhar M, Goenka AH. Pancreas segmentation using AI developed on the largest CT dataset with multi-institutional validation and implications for early cancer detection. Sci Rep 2025; 15:17096. [PMID: 40379726 PMCID: PMC12084540 DOI: 10.1038/s41598-025-01802-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2025] [Accepted: 05/08/2025] [Indexed: 05/19/2025] Open
Abstract
Accurate and fully automated pancreas segmentation is critical for advancing imaging biomarkers in early pancreatic cancer detection and for biomarker discovery in endocrine and exocrine pancreatic diseases. We developed and evaluated a deep learning (DL)-based convolutional neural network (CNN) for automated pancreas segmentation using the largest single-institution dataset to date (n = 3031 CTs). Ground truth segmentations were performed by radiologists, which were used to train a 3D nnU-Net model through five-fold cross-validation, generating an ensemble of top-performing models. To assess generalizability, the model was externally validated on the multi-institutional AbdomenCT-1K dataset (n = 585), for which volumetric segmentations were newly generated by expert radiologists and will be made publicly available. In the test subset (n = 452), the CNN achieved a mean Dice Similarity Coefficient (DSC) of 0.94 (SD 0.05), demonstrating high spatial concordance with radiologist-annotated volumes (Concordance Correlation Coefficient [CCC]: 0.95). On the AbdomenCT-1K dataset, the model achieved a DSC of 0.96 (SD 0.04) and a CCC of 0.98, confirming its robustness across diverse imaging conditions. The proposed DL model establishes new performance benchmarks for fully automated pancreas segmentation, offering a scalable and generalizable solution for large-scale imaging biomarker research and clinical translation.
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Affiliation(s)
- Sovanlal Mukherjee
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA
| | - Ajith Antony
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA
| | - Nandakumar G Patnam
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA
| | - Kamaxi H Trivedi
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA
| | - Aashna Karbhari
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA
| | - Madhu Nagaraj
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA
| | - Murlidhar Murlidhar
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA
| | - Ajit H Goenka
- Professor of Radiology, Consultant, Divisions of Abdominal and Nuclear Radiology, Co-Chair, Nuclear Radiology Research Operations, Chair, Enterprise PET/MR Research, Education and Executive Committee, Program Co-Leader, Risk Assessment, Early Detection and Interception (REDI), Mayo Clinic Comprehensive Cancer Center (MCCCC), 200 First St SW, Charlton 1, Rochester, MN, 55905, USA.
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Ramai D, Collins B, Ofosu A, Mohan BP, Jagannath S, Tabibian JH, Girotra M, Barakat MT. Deep Learning Methods in the Imaging of Hepatic and Pancreaticobiliary Diseases. J Clin Gastroenterol 2025; 59:405-411. [PMID: 40193287 DOI: 10.1097/mcg.0000000000002125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/09/2025]
Abstract
Reports indicate a growing role for artificial intelligence (AI) in the evaluation of pancreaticobiliary and hepatic conditions. A key focus is differentiating between benign and malignant lesions, which is crucial for treatment decisions. AI improves diagnostic accuracy through high sensitivity and specificity, while CNN algorithms enhance image analysis and reduce variability. These advancements aim to match the accuracy of pathologists in cancer detection. In addition, AI aids in identifying diagnostic markers, as early detection is essential. This article reviews the applications of machine learning and deep learning in the diagnosis of hepatic and pancreaticobiliary diseases. Although the use of AI in these specialized areas of gastroenterology is primarily confined to experimental trials, current models demonstrate significant potential for enhancing the detection, evaluation, and treatment planning of hepatic and pancreaticobiliary conditions.
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Affiliation(s)
- Daryl Ramai
- Division of Gastroenterology and Hepatology, University of Utah, Salt Lake City, UT
| | - Brendan Collins
- Division of Digestive Diseases, University of Cincinnati, Cincinnati, OH
| | - Andrew Ofosu
- Division of Digestive Diseases, University of Cincinnati, Cincinnati, OH
| | - Babu P Mohan
- Division of Gastroenterology and Hepatology, University of Utah, Salt Lake City, UT
| | - Soumya Jagannath
- Department of Gastroenterology, All India Institute of Medical Sciences, New Delhi, India
| | - James H Tabibian
- Division of Gastroenterology, Olive View-UCLA Medical Center, Sylmar
- David Geffen School of Medicine at UCLA, Los Angeles
| | - Mohit Girotra
- Digestive Health Institute, Swedish Medical Center, Seattle, WA
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Renjifo-Correa ME, Fanni SC, Bustamante-Cristancho LA, Cuibari ME, Aghakhanyan G, Faggioni L, Neri E, Cioni D. Diagnostic Accuracy of Radiomics in the Early Detection of Pancreatic Cancer: A Systematic Review and Qualitative Assessment Using the Methodological Radiomics Score (METRICS). Cancers (Basel) 2025; 17:803. [PMID: 40075651 PMCID: PMC11898638 DOI: 10.3390/cancers17050803] [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: 12/24/2024] [Revised: 02/14/2025] [Accepted: 02/24/2025] [Indexed: 03/14/2025] Open
Abstract
BACKGROUND/OBJECTIVES Pancreatic ductal adenocarcinoma (PDAC) is an aggressive and lethal malignancy with increasing incidence and low survival rate, primarily due to the late detection of the disease. Radiomics has demonstrated its utility in recognizing patterns and anomalies not perceptible to the human eye. This systematic literature review aims to assess the application of radiomics in the analysis of pancreatic parenchyma images to identify early indicators predictive of PDAC. METHODS A systematic search of original research papers was performed on three databases: PubMed, Embase, and Scopus. Two reviewers applied the inclusion and exclusion criteria, and one expert solved conflicts for selecting the articles. After extraction and analysis of the data, there was a quality assessment of these articles using the Methodological Radiomics Score (METRICS) tool. The METRICS assessment was carried out by two raters, and conflicts were solved by a third reviewer. RESULTS Ten articles for analysis were retrieved. CT scan was the diagnostic imaging used in all the articles. All the studies were retrospective and published between 2019 and 2024. The main objective of the articles was to generate radiomics-based machine learning models able to differentiate pancreatic tumors from healthy tissue. The reported diagnostic performance of the model chosen yielded very high results, with a diagnostic accuracy between 86.5% and 99.2%. Texture and shape features were the most frequently implemented. The METRICS scoring assessment demonstrated that three articles obtained a moderate quality, five a good quality, and, finally, two articles yielded excellent quality. The lack of external validation and available model, code, and data were the major limitations according to the qualitative assessment. CONCLUSIONS There is high heterogeneity in the research question regarding radiomics and pancreatic cancer. The principal limitations of the studies were mainly due to the nature of the trials and the considerable heterogeneity of the radiomic features reported. Nonetheless, the work in this field is promising, and further studies are still required to adopt radiomics in the early detection of PDAC.
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Affiliation(s)
- María Estefanía Renjifo-Correa
- Radiology Department, Magnetic Resonance Service, Clínica de Occidente, Calle 18 Norte No. 5N 34, Cali 760045, Colombia;
| | - Salvatore Claudio Fanni
- Department of Translational Research, Academic Radiology, University of Pisa, Via Paradisa 2, 56124 Pisa, Italy; (M.E.C.); (G.A.); (L.F.); (E.N.); (D.C.)
| | | | - Maria Emanuela Cuibari
- Department of Translational Research, Academic Radiology, University of Pisa, Via Paradisa 2, 56124 Pisa, Italy; (M.E.C.); (G.A.); (L.F.); (E.N.); (D.C.)
| | - Gayane Aghakhanyan
- Department of Translational Research, Academic Radiology, University of Pisa, Via Paradisa 2, 56124 Pisa, Italy; (M.E.C.); (G.A.); (L.F.); (E.N.); (D.C.)
| | - Lorenzo Faggioni
- Department of Translational Research, Academic Radiology, University of Pisa, Via Paradisa 2, 56124 Pisa, Italy; (M.E.C.); (G.A.); (L.F.); (E.N.); (D.C.)
| | - Emanuele Neri
- Department of Translational Research, Academic Radiology, University of Pisa, Via Paradisa 2, 56124 Pisa, Italy; (M.E.C.); (G.A.); (L.F.); (E.N.); (D.C.)
| | - Dania Cioni
- Department of Translational Research, Academic Radiology, University of Pisa, Via Paradisa 2, 56124 Pisa, Italy; (M.E.C.); (G.A.); (L.F.); (E.N.); (D.C.)
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6
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Antony A, Mukherjee S, Bi Y, Collisson EA, Nagaraj M, Murlidhar M, Wallace MB, Goenka AH. AI-Driven insights in pancreatic cancer imaging: from pre-diagnostic detection to prognostication. Abdom Radiol (NY) 2024:10.1007/s00261-024-04775-x. [PMID: 39738571 DOI: 10.1007/s00261-024-04775-x] [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: 11/23/2024] [Revised: 12/15/2024] [Accepted: 12/16/2024] [Indexed: 01/02/2025]
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is the third leading cause of cancer-related deaths in the United States, largely due to its poor five-year survival rate and frequent late-stage diagnosis. A significant barrier to early detection even in high-risk cohorts is that the pancreas often appears morphologically normal during the pre-diagnostic phase. Yet, the disease can progress rapidly from subclinical stages to widespread metastasis, undermining the effectiveness of screening. Recently, artificial intelligence (AI) applied to cross-sectional imaging has shown significant potential in identifying subtle, early-stage changes in pancreatic tissue that are often imperceptible to the human eye. Moreover, AI-driven imaging also aids in the discovery of prognostic and predictive biomarkers, essential for personalized treatment planning. This article uniquely integrates a critical discussion on AI's role in detecting visually occult PDAC on pre-diagnostic imaging, addresses challenges of model generalizability, and emphasizes solutions like standardized datasets and clinical workflows. By focusing on both technical advancements and practical implementation, this article provides a forward-thinking conceptual framework that bridges current gaps in AI-driven PDAC research.
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Affiliation(s)
- Ajith Antony
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | | | - Yan Bi
- Department of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL, USA
| | - Eric A Collisson
- Department of Medical Oncology, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Madhu Nagaraj
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | | | - Michael B Wallace
- Department of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL, USA
| | - Ajit H Goenka
- Department of Radiology, Mayo Clinic, Rochester, MN, USA.
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7
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Gu P, Mendonca O, Carter D, Dube S, Wang P, Huang X, Li D, Moore JH, McGovern DPB. AI-luminating Artificial Intelligence in Inflammatory Bowel Diseases: A Narrative Review on the Role of AI in Endoscopy, Histology, and Imaging for IBD. Inflamm Bowel Dis 2024; 30:2467-2485. [PMID: 38452040 DOI: 10.1093/ibd/izae030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Indexed: 03/09/2024]
Abstract
Endoscopy, histology, and cross-sectional imaging serve as fundamental pillars in the detection, monitoring, and prognostication of inflammatory bowel disease (IBD). However, interpretation of these studies often relies on subjective human judgment, which can lead to delays, intra- and interobserver variability, and potential diagnostic discrepancies. With the rising incidence of IBD globally coupled with the exponential digitization of these data, there is a growing demand for innovative approaches to streamline diagnosis and elevate clinical decision-making. In this context, artificial intelligence (AI) technologies emerge as a timely solution to address the evolving challenges in IBD. Early studies using deep learning and radiomics approaches for endoscopy, histology, and imaging in IBD have demonstrated promising results for using AI to detect, diagnose, characterize, phenotype, and prognosticate IBD. Nonetheless, the available literature has inherent limitations and knowledge gaps that need to be addressed before AI can transition into a mainstream clinical tool for IBD. To better understand the potential value of integrating AI in IBD, we review the available literature to summarize our current understanding and identify gaps in knowledge to inform future investigations.
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Affiliation(s)
- Phillip Gu
- F. Widjaja Inflammatory Bowel Disease Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | | | - Dan Carter
- Department of Gastroenterology, Sheba Medical Center, Tel Aviv, Israel
| | - Shishir Dube
- F. Widjaja Inflammatory Bowel Disease Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Paul Wang
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Xiuzhen Huang
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Debiao Li
- Biomedical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Jason H Moore
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Dermot P B McGovern
- F. Widjaja Inflammatory Bowel Disease Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
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8
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Rai HM, Yoo J, Razaque A. Comparative analysis of machine learning and deep learning models for improved cancer detection: A comprehensive review of recent advancements in diagnostic techniques. EXPERT SYSTEMS WITH APPLICATIONS 2024; 255:124838. [DOI: 10.1016/j.eswa.2024.124838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/11/2024]
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9
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Ahmed TM, Kawamoto S, Lopez-Ramirez F, Yasrab M, Hruban RH, Fishman EK, Chu LC. Early detection of pancreatic cancer in the era of precision medicine. Abdom Radiol (NY) 2024; 49:3559-3573. [PMID: 38761272 DOI: 10.1007/s00261-024-04358-w] [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: 03/31/2024] [Revised: 04/23/2024] [Accepted: 04/23/2024] [Indexed: 05/20/2024]
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is the third leading cause of cancer-related mortality and it is often diagnosed at advanced stages due to non-specific clinical presentation. Disease detection at localized disease stage followed by surgical resection remains the only potentially curative treatment. In this era of precision medicine, a multifaceted approach to early detection of PDAC includes targeted screening in high-risk populations, serum biomarkers and "liquid biopsies", and artificial intelligence augmented tumor detection from radiologic examinations. In this review, we will review these emerging techniques in the early detection of PDAC.
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Affiliation(s)
- Taha M Ahmed
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Satomi Kawamoto
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Felipe Lopez-Ramirez
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Mohammad Yasrab
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Ralph H Hruban
- Department of Pathology, Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Elliot K Fishman
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Linda C Chu
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Hospital, Baltimore, MD, USA.
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10
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Ahmed TM, Lopez-Ramirez F, Fishman EK, Chu L. Artificial Intelligence Applications in Pancreatic Cancer Imaging. ADVANCES IN CLINICAL RADIOLOGY 2024; 6:41-54. [DOI: 10.1016/j.yacr.2024.04.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2025]
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Mukund A, Afridi MA, Karolak A, Park MA, Permuth JB, Rasool G. Pancreatic Ductal Adenocarcinoma (PDAC): A Review of Recent Advancements Enabled by Artificial Intelligence. Cancers (Basel) 2024; 16:2240. [PMID: 38927945 PMCID: PMC11201559 DOI: 10.3390/cancers16122240] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2024] [Revised: 06/03/2024] [Accepted: 06/12/2024] [Indexed: 06/28/2024] Open
Abstract
Pancreatic Ductal Adenocarcinoma (PDAC) remains one of the most formidable challenges in oncology, characterized by its late detection and poor prognosis. Artificial intelligence (AI) and machine learning (ML) are emerging as pivotal tools in revolutionizing PDAC care across various dimensions. Consequently, many studies have focused on using AI to improve the standard of PDAC care. This review article attempts to consolidate the literature from the past five years to identify high-impact, novel, and meaningful studies focusing on their transformative potential in PDAC management. Our analysis spans a broad spectrum of applications, including but not limited to patient risk stratification, early detection, and prediction of treatment outcomes, thereby highlighting AI's potential role in enhancing the quality and precision of PDAC care. By categorizing the literature into discrete sections reflective of a patient's journey from screening and diagnosis through treatment and survivorship, this review offers a comprehensive examination of AI-driven methodologies in addressing the multifaceted challenges of PDAC. Each study is summarized by explaining the dataset, ML model, evaluation metrics, and impact the study has on improving PDAC-related outcomes. We also discuss prevailing obstacles and limitations inherent in the application of AI within the PDAC context, offering insightful perspectives on potential future directions and innovations.
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Affiliation(s)
- Ashwin Mukund
- Department of Machine Learning, Moffitt Cancer Center and Research Institute, 12902 USF Magnolia Drive, Tampa, FL 33612, USA; (A.M.); (A.K.)
| | - Muhammad Ali Afridi
- School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan;
| | - Aleksandra Karolak
- Department of Machine Learning, Moffitt Cancer Center and Research Institute, 12902 USF Magnolia Drive, Tampa, FL 33612, USA; (A.M.); (A.K.)
| | - Margaret A. Park
- Departments of Cancer Epidemiology and Gastrointestinal Oncology, Moffitt Cancer Center and Research Institute, 12902 USF Magnolia Drive, Tampa, FL 33612, USA; (M.A.P.); (J.B.P.)
| | - Jennifer B. Permuth
- Departments of Cancer Epidemiology and Gastrointestinal Oncology, Moffitt Cancer Center and Research Institute, 12902 USF Magnolia Drive, Tampa, FL 33612, USA; (M.A.P.); (J.B.P.)
| | - Ghulam Rasool
- Department of Machine Learning, Moffitt Cancer Center and Research Institute, 12902 USF Magnolia Drive, Tampa, FL 33612, USA; (A.M.); (A.K.)
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Tenchov R, Sapra AK, Sasso J, Ralhan K, Tummala A, Azoulay N, Zhou QA. Biomarkers for Early Cancer Detection: A Landscape View of Recent Advancements, Spotlighting Pancreatic and Liver Cancers. ACS Pharmacol Transl Sci 2024; 7:586-613. [PMID: 38481702 PMCID: PMC10928905 DOI: 10.1021/acsptsci.3c00346] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Revised: 01/06/2024] [Accepted: 01/23/2024] [Indexed: 01/04/2025]
Abstract
Cancer is one of the leading causes of death worldwide. Early cancer detection is critical because it can significantly improve treatment outcomes, thus saving lives, reducing suffering, and lessening psychological and economic burdens. Cancer biomarkers provide varied information about cancer, from early detection of malignancy to decisions on treatment and subsequent monitoring. A large variety of molecular, histologic, radiographic, or physiological entities or features are among the common types of cancer biomarkers. Sizeable recent methodological progress and insights have promoted significant developments in the field of early cancer detection biomarkers. Here we provide an overview of recent advances in the knowledge related to biomolecules and cellular entities used for early cancer detection. We examine data from the CAS Content Collection, the largest human-curated collection of published scientific information, as well as from the biomarker datasets at Excelra, and analyze the publication landscape of recent research. We also discuss the evolution of key concepts and cancer biomarkers development pipelines, with a particular focus on pancreatic and liver cancers, which are known to be remarkably difficult to detect early and to have particularly high morbidity and mortality. The objective of the paper is to provide a broad overview of the evolving landscape of current knowledge on cancer biomarkers and to outline challenges and evaluate growth opportunities, in order to further efforts in solving the problems that remain. The merit of this review stems from the extensive, wide-ranging coverage of the most up-to-date scientific information, allowing unique, unmatched breadth of landscape analysis and in-depth insights.
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Affiliation(s)
- Rumiana Tenchov
- CAS,
a division of the American Chemical Society, Columbus, Ohio 43210, United States
| | - Aparna K. Sapra
- Excelra
Knowledge Solutions Pvt. Ltd., Hyderabad-500039, India
| | - Janet Sasso
- CAS,
a division of the American Chemical Society, Columbus, Ohio 43210, United States
| | | | - Anusha Tummala
- Excelra
Knowledge Solutions Pvt. Ltd., Hyderabad-500039, India
| | - Norman Azoulay
- Excelra
Knowledge Solutions Pvt. Ltd., Hyderabad-500039, India
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Mukherjee S, Korfiatis P, Patnam NG, Trivedi KH, Karbhari A, Suman G, Fletcher JG, Goenka AH. Assessing the robustness of a machine-learning model for early detection of pancreatic adenocarcinoma (PDA): evaluating resilience to variations in image acquisition and radiomics workflow using image perturbation methods. Abdom Radiol (NY) 2024; 49:964-974. [PMID: 38175255 DOI: 10.1007/s00261-023-04127-1] [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: 06/15/2023] [Revised: 11/08/2023] [Accepted: 11/12/2023] [Indexed: 01/05/2024]
Abstract
PURPOSE To evaluate robustness of a radiomics-based support vector machine (SVM) model for detection of visually occult PDA on pre-diagnostic CTs by simulating common variations in image acquisition and radiomics workflow using image perturbation methods. METHODS Eighteen algorithmically generated-perturbations, which simulated variations in image noise levels (σ, 2σ, 3σ, 5σ), image rotation [both CT image and the corresponding pancreas segmentation mask by 45° and 90° in axial plane], voxel resampling (isotropic and anisotropic), gray-level discretization [bin width (BW) 32 and 64)], and pancreas segmentation (sequential erosions by 3, 4, 6, and 8 pixels and dilations by 3, 4, and 6 pixels from the boundary), were introduced to the original (unperturbed) test subset (n = 128; 45 pre-diagnostic CTs, 83 control CTs with normal pancreas). Radiomic features were extracted from pancreas masks of these additional test subsets, and the model's performance was compared vis-a-vis the unperturbed test subset. RESULTS The model correctly classified 43 out of 45 pre-diagnostic CTs and 75 out of 83 control CTs in the unperturbed test subset, achieving 92.2% accuracy and 0.98 AUC. Model's performance was unaffected by a three-fold increase in noise level except for sensitivity declining to 80% at 3σ (p = 0.02). Performance remained comparable vis-a-vis the unperturbed test subset despite variations in image rotation (p = 0.99), voxel resampling (p = 0.25-0.31), change in gray-level BW to 32 (p = 0.31-0.99), and erosions/dilations up to 4 pixels from the pancreas boundary (p = 0.12-0.34). CONCLUSION The model's high performance for detection of visually occult PDA was robust within a broad range of clinically relevant variations in image acquisition and radiomics workflow.
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Affiliation(s)
- Sovanlal Mukherjee
- Divisions of Abdominal and Nuclear Imaging, Nuclear Radiology Fellowship, Nuclear Radiology Research Operations, Enterprise PET/MR Research and Development, Department of Radiology, Mayo Clinic, 200 First St SW, Charlton 1, Rochester, MN, 55905, USA
| | - Panagiotis Korfiatis
- Divisions of Abdominal and Nuclear Imaging, Nuclear Radiology Fellowship, Nuclear Radiology Research Operations, Enterprise PET/MR Research and Development, Department of Radiology, Mayo Clinic, 200 First St SW, Charlton 1, Rochester, MN, 55905, USA
| | - Nandakumar G Patnam
- Divisions of Abdominal and Nuclear Imaging, Nuclear Radiology Fellowship, Nuclear Radiology Research Operations, Enterprise PET/MR Research and Development, Department of Radiology, Mayo Clinic, 200 First St SW, Charlton 1, Rochester, MN, 55905, USA
| | - Kamaxi H Trivedi
- Divisions of Abdominal and Nuclear Imaging, Nuclear Radiology Fellowship, Nuclear Radiology Research Operations, Enterprise PET/MR Research and Development, Department of Radiology, Mayo Clinic, 200 First St SW, Charlton 1, Rochester, MN, 55905, USA
| | - Aashna Karbhari
- Divisions of Abdominal and Nuclear Imaging, Nuclear Radiology Fellowship, Nuclear Radiology Research Operations, Enterprise PET/MR Research and Development, Department of Radiology, Mayo Clinic, 200 First St SW, Charlton 1, Rochester, MN, 55905, USA
| | - Garima Suman
- Divisions of Abdominal and Nuclear Imaging, Nuclear Radiology Fellowship, Nuclear Radiology Research Operations, Enterprise PET/MR Research and Development, Department of Radiology, Mayo Clinic, 200 First St SW, Charlton 1, Rochester, MN, 55905, USA
| | - Joel G Fletcher
- Divisions of Abdominal and Nuclear Imaging, Nuclear Radiology Fellowship, Nuclear Radiology Research Operations, Enterprise PET/MR Research and Development, Department of Radiology, Mayo Clinic, 200 First St SW, Charlton 1, Rochester, MN, 55905, USA
| | - Ajit H Goenka
- Divisions of Abdominal and Nuclear Imaging, Nuclear Radiology Fellowship, Nuclear Radiology Research Operations, Enterprise PET/MR Research and Development, Department of Radiology, Mayo Clinic, 200 First St SW, Charlton 1, Rochester, MN, 55905, USA.
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Scherübl H. [Early detection of sporadic pancreatic cancer]. ZEITSCHRIFT FUR GASTROENTEROLOGIE 2024; 62:412-419. [PMID: 37827502 DOI: 10.1055/a-2114-9847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/14/2023]
Abstract
The incidence of pancreatic cancer is rising. At present, pancreatic cancer is the third most common cancer-causing death in Germany, but it is expected to become the second in 2030 and finally the leading cause of cancer death in 2050. Pancreatic ductal adenocarcinoma (PC) is generally diagnosed at advanced stages, and 5-year-survival has remained poor. Early detection of sporadic PC at stage IA, however, can yield a 5-year-survival rate of about 80%. Early detection initiatives aim at identifying persons at high risk. People with new-onset diabetes at age 50 or older have attracted much interest. Novel strategies regarding how to detect sporadic PC at an early stage are being discussed.
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Affiliation(s)
- Hans Scherübl
- Klinik für Innere Medizin; Gastroenterol., GI Onkol. u. Infektiol., Vivantes Klinikum Am Urban, Berlin, Germany
- Akademisches Lehrkrankenhaus der Charité, Berlin, Germany
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15
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Rai HM, Yoo J, Atif Moqurrab S, Dashkevych S. Advancements in traditional machine learning techniques for detection and diagnosis of fatal cancer types: Comprehensive review of biomedical imaging datasets. MEASUREMENT 2024; 225:114059. [DOI: 10.1016/j.measurement.2023.114059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/11/2024]
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Tripathi S, Tabari A, Mansur A, Dabbara H, Bridge CP, Daye D. From Machine Learning to Patient Outcomes: A Comprehensive Review of AI in Pancreatic Cancer. Diagnostics (Basel) 2024; 14:174. [PMID: 38248051 PMCID: PMC10814554 DOI: 10.3390/diagnostics14020174] [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: 09/19/2023] [Revised: 12/28/2023] [Accepted: 12/29/2023] [Indexed: 01/23/2024] Open
Abstract
Pancreatic cancer is a highly aggressive and difficult-to-detect cancer with a poor prognosis. Late diagnosis is common due to a lack of early symptoms, specific markers, and the challenging location of the pancreas. Imaging technologies have improved diagnosis, but there is still room for improvement in standardizing guidelines. Biopsies and histopathological analysis are challenging due to tumor heterogeneity. Artificial Intelligence (AI) revolutionizes healthcare by improving diagnosis, treatment, and patient care. AI algorithms can analyze medical images with precision, aiding in early disease detection. AI also plays a role in personalized medicine by analyzing patient data to tailor treatment plans. It streamlines administrative tasks, such as medical coding and documentation, and provides patient assistance through AI chatbots. However, challenges include data privacy, security, and ethical considerations. This review article focuses on the potential of AI in transforming pancreatic cancer care, offering improved diagnostics, personalized treatments, and operational efficiency, leading to better patient outcomes.
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Affiliation(s)
- Satvik Tripathi
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA; (S.T.); (A.T.); (A.M.); (C.P.B.)
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA 02129, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Azadeh Tabari
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA; (S.T.); (A.T.); (A.M.); (C.P.B.)
- Harvard Medical School, Boston, MA 02115, USA
| | - Arian Mansur
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA; (S.T.); (A.T.); (A.M.); (C.P.B.)
- Harvard Medical School, Boston, MA 02115, USA
| | - Harika Dabbara
- Boston University Chobanian & Avedisian School of Medicine, Boston, MA 02118, USA;
| | - Christopher P. Bridge
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA; (S.T.); (A.T.); (A.M.); (C.P.B.)
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA 02129, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Dania Daye
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA; (S.T.); (A.T.); (A.M.); (C.P.B.)
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA 02129, USA
- Harvard Medical School, Boston, MA 02115, USA
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Qureshi TA, Chen X, Xie Y, Murakami K, Sakatani T, Kita Y, Kobayashi T, Miyake M, Knott SRV, Li D, Rosser CJ, Furuya H. MRI/RNA-Seq-Based Radiogenomics and Artificial Intelligence for More Accurate Staging of Muscle-Invasive Bladder Cancer. Int J Mol Sci 2023; 25:88. [PMID: 38203254 PMCID: PMC10778815 DOI: 10.3390/ijms25010088] [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: 11/06/2023] [Revised: 12/08/2023] [Accepted: 12/14/2023] [Indexed: 01/12/2024] Open
Abstract
Accurate staging of bladder cancer assists in identifying optimal treatment (e.g., transurethral resection vs. radical cystectomy vs. bladder preservation). However, currently, about one-third of patients are over-staged and one-third are under-staged. There is a pressing need for a more accurate staging modality to evaluate patients with bladder cancer to assist clinical decision-making. We hypothesize that MRI/RNA-seq-based radiogenomics and artificial intelligence can more accurately stage bladder cancer. A total of 40 magnetic resonance imaging (MRI) and matched formalin-fixed paraffin-embedded (FFPE) tissues were available for analysis. Twenty-eight (28) MRI and their matched FFPE tissues were available for training analysis, and 12 matched MRI and FFPE tissues were used for validation. FFPE samples were subjected to bulk RNA-seq, followed by bioinformatics analysis. In the radiomics, several hundred image-based features from bladder tumors in MRI were extracted and analyzed. Overall, the model obtained mean sensitivity, specificity, and accuracy of 94%, 88%, and 92%, respectively, in differentiating intra- vs. extra-bladder cancer. The proposed model demonstrated improvement in the three matrices by 17%, 33%, and 25% and 17%, 16%, and 17% as compared to the genetic- and radiomic-based models alone, respectively. The radiogenomics of bladder cancer provides insight into discriminative features capable of more accurately staging bladder cancer. Additional studies are underway.
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Affiliation(s)
- Touseef Ahmad Qureshi
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; (T.A.Q.); (Y.X.); (D.L.)
- Department of Biomedical Science, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; (X.C.); (S.R.V.K.)
| | - Xingyu Chen
- Department of Biomedical Science, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; (X.C.); (S.R.V.K.)
- Department of Urology, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA;
| | - Yibin Xie
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; (T.A.Q.); (Y.X.); (D.L.)
| | - Kaoru Murakami
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; (K.M.); (T.S.)
- Department of Urology, Kyoto University, Kyoto 606-8507, Japan; (Y.K.); (T.K.)
| | - Toru Sakatani
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; (K.M.); (T.S.)
| | - Yuki Kita
- Department of Urology, Kyoto University, Kyoto 606-8507, Japan; (Y.K.); (T.K.)
| | - Takashi Kobayashi
- Department of Urology, Kyoto University, Kyoto 606-8507, Japan; (Y.K.); (T.K.)
| | - Makito Miyake
- Department of Urology, Nara Medical University, Kashihara 634-8522, Japan;
| | - Simon R. V. Knott
- Department of Biomedical Science, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; (X.C.); (S.R.V.K.)
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; (K.M.); (T.S.)
| | - Debiao Li
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; (T.A.Q.); (Y.X.); (D.L.)
| | - Charles J. Rosser
- Department of Urology, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA;
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; (K.M.); (T.S.)
| | - Hideki Furuya
- Department of Biomedical Science, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; (X.C.); (S.R.V.K.)
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; (K.M.); (T.S.)
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Rawlani P, Ghosh NK, Kumar A. Role of artificial intelligence in the characterization of indeterminate pancreatic head mass and its usefulness in preoperative diagnosis. Artif Intell Gastroenterol 2023; 4:48-63. [DOI: 10.35712/aig.v4.i3.48] [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: 07/27/2023] [Revised: 09/11/2023] [Accepted: 10/08/2023] [Indexed: 12/07/2023] Open
Abstract
Artificial intelligence (AI) has been used in various fields of day-to-day life and its role in medicine is immense. Understanding of oncology has been improved with the introduction of AI which helps in diagnosis, treatment planning, management, prognosis, and follow-up. It also helps to identify high-risk groups who can be subjected to timely screening for early detection of malignant conditions. It is more important in pancreatic cancer as it is one of the major causes of cancer-related deaths worldwide and there are no specific early features (clinical and radiological) for diagnosis. With improvement in imaging modalities (computed tomography, magnetic resonance imaging, endoscopic ultrasound), most often clinicians were being challenged with lesions that were difficult to diagnose with human competence. AI has been used in various other branches of medicine to differentiate such indeterminate lesions including the thyroid gland, breast, lungs, liver, adrenal gland, kidney, etc. In the case of pancreatic cancer, the role of AI has been explored and is still ongoing. This review article will focus on how AI can be used to diagnose pancreatic cancer early or differentiate it from benign pancreatic lesions, therefore, management can be planned at an earlier stage.
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Affiliation(s)
- Palash Rawlani
- Department of Surgical Gastroenterology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow 226014, Uttar Pradesh, India
| | - Nalini Kanta Ghosh
- Department of Surgical Gastroenterology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow 226014, Uttar Pradesh, India
| | - Ashok Kumar
- Department of Surgical Gastroenterology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow 226014, Uttar Pradesh, India
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Korfiatis P, Suman G, Patnam NG, Trivedi KH, Karbhari A, Mukherjee S, Cook C, Klug JR, Patra A, Khasawneh H, Rajamohan N, Fletcher JG, Truty MJ, Majumder S, Bolan CW, Sandrasegaran K, Chari ST, Goenka AH. Automated Artificial Intelligence Model Trained on a Large Data Set Can Detect Pancreas Cancer on Diagnostic Computed Tomography Scans As Well As Visually Occult Preinvasive Cancer on Prediagnostic Computed Tomography Scans. Gastroenterology 2023; 165:1533-1546.e4. [PMID: 37657758 PMCID: PMC10843414 DOI: 10.1053/j.gastro.2023.08.034] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 08/13/2023] [Accepted: 08/17/2023] [Indexed: 09/03/2023]
Abstract
BACKGROUND & AIMS The aims of our case-control study were (1) to develop an automated 3-dimensional (3D) Convolutional Neural Network (CNN) for detection of pancreatic ductal adenocarcinoma (PDA) on diagnostic computed tomography scans (CTs), (2) evaluate its generalizability on multi-institutional public data sets, (3) its utility as a potential screening tool using a simulated cohort with high pretest probability, and (4) its ability to detect visually occult preinvasive cancer on prediagnostic CTs. METHODS A 3D-CNN classification system was trained using algorithmically generated bounding boxes and pancreatic masks on a curated data set of 696 portal phase diagnostic CTs with PDA and 1080 control images with a nonneoplastic pancreas. The model was evaluated on (1) an intramural hold-out test subset (409 CTs with PDA, 829 controls); (2) a simulated cohort with a case-control distribution that matched the risk of PDA in glycemically defined new-onset diabetes, and Enriching New-Onset Diabetes for Pancreatic Cancer score ≥3; (3) multi-institutional public data sets (194 CTs with PDA, 80 controls), and (4) a cohort of 100 prediagnostic CTs (i.e., CTs incidentally acquired 3-36 months before clinical diagnosis of PDA) without a focal mass, and 134 controls. RESULTS Of the CTs in the intramural test subset, 798 (64%) were from other hospitals. The model correctly classified 360 CTs (88%) with PDA and 783 control CTs (94%), with a mean accuracy 0.92 (95% CI, 0.91-0.94), area under the receiver operating characteristic (AUROC) curve of 0.97 (95% CI, 0.96-0.98), sensitivity of 0.88 (95% CI, 0.85-0.91), and specificity of 0.95 (95% CI, 0.93-0.96). Activation areas on heat maps overlapped with the tumor in 350 of 360 CTs (97%). Performance was high across tumor stages (sensitivity of 0.80, 0.87, 0.95, and 1.0 on T1 through T4 stages, respectively), comparable for hypodense vs isodense tumors (sensitivity: 0.90 vs 0.82), different age, sex, CT slice thicknesses, and vendors (all P > .05), and generalizable on both the simulated cohort (accuracy, 0.95 [95% 0.94-0.95]; AUROC curve, 0.97 [95% CI, 0.94-0.99]) and public data sets (accuracy, 0.86 [95% CI, 0.82-0.90]; AUROC curve, 0.90 [95% CI, 0.86-0.95]). Despite being exclusively trained on diagnostic CTs with larger tumors, the model could detect occult PDA on prediagnostic CTs (accuracy, 0.84 [95% CI, 0.79-0.88]; AUROC curve, 0.91 [95% CI, 0.86-0.94]; sensitivity, 0.75 [95% CI, 0.67-0.84]; and specificity, 0.90 [95% CI, 0.85-0.95]) at a median 475 days (range, 93-1082 days) before clinical diagnosis. CONCLUSIONS This automated artificial intelligence model trained on a large and diverse data set shows high accuracy and generalizable performance for detection of PDA on diagnostic CTs as well as for visually occult PDA on prediagnostic CTs. Prospective validation with blood-based biomarkers is warranted to assess the potential for early detection of sporadic PDA in high-risk individuals.
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Affiliation(s)
| | - Garima Suman
- Department of Radiology, Mayo Clinic, Rochester, Minnesota
| | | | | | | | | | - Cole Cook
- Division of Medical Imaging Technology Services, Mayo Clinic, Rochester, Minnesota
| | - Jason R Klug
- Division of Medical Imaging Technology Services, Mayo Clinic, Rochester, Minnesota
| | - Anurima Patra
- Department of Radiology, Tata Medical Center, Kolkata, India
| | - Hala Khasawneh
- Department of Radiology, Mayo Clinic, Rochester, Minnesota
| | | | | | - Mark J Truty
- Department of Surgery, Mayo Clinic, Rochester, Minnesota
| | - Shounak Majumder
- Department of Gastroenterology, Mayo Clinic, Rochester, Minnesota
| | | | | | - Suresh T Chari
- Department of Gastroenterology, Mayo Clinic, Rochester, Minnesota
| | - Ajit H Goenka
- Department of Radiology, Mayo Clinic, Rochester, Minnesota.
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Yao L, Zhang Z, Keles E, Yazici C, Tirkes T, Bagci U. A review of deep learning and radiomics approaches for pancreatic cancer diagnosis from medical imaging. Curr Opin Gastroenterol 2023; 39:436-447. [PMID: 37523001 PMCID: PMC10403281 DOI: 10.1097/mog.0000000000000966] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/01/2023]
Abstract
PURPOSE OF REVIEW Early and accurate diagnosis of pancreatic cancer is crucial for improving patient outcomes, and artificial intelligence (AI) algorithms have the potential to play a vital role in computer-aided diagnosis of pancreatic cancer. In this review, we aim to provide the latest and relevant advances in AI, specifically deep learning (DL) and radiomics approaches, for pancreatic cancer diagnosis using cross-sectional imaging examinations such as computed tomography (CT) and magnetic resonance imaging (MRI). RECENT FINDINGS This review highlights the recent developments in DL techniques applied to medical imaging, including convolutional neural networks (CNNs), transformer-based models, and novel deep learning architectures that focus on multitype pancreatic lesions, multiorgan and multitumor segmentation, as well as incorporating auxiliary information. We also discuss advancements in radiomics, such as improved imaging feature extraction, optimized machine learning classifiers and integration with clinical data. Furthermore, we explore implementing AI-based clinical decision support systems for pancreatic cancer diagnosis using medical imaging in practical settings. SUMMARY Deep learning and radiomics with medical imaging have demonstrated strong potential to improve diagnostic accuracy of pancreatic cancer, facilitate personalized treatment planning, and identify prognostic and predictive biomarkers. However, challenges remain in translating research findings into clinical practice. More studies are required focusing on refining these methods, addressing significant limitations, and developing integrative approaches for data analysis to further advance the field of pancreatic cancer diagnosis.
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Affiliation(s)
- Lanhong Yao
- Machine & Hybrid Intelligence Lab, Department of Radiology, Northwestern University
| | - Zheyuan Zhang
- Machine & Hybrid Intelligence Lab, Department of Radiology, Northwestern University
| | - Elif Keles
- Machine & Hybrid Intelligence Lab, Department of Radiology, Northwestern University
| | - Cemal Yazici
- Division of Gastroentrrology and Hepatology, University of Illinois Chicago, Chicago, Illinois
| | - Temel Tirkes
- Department of Radiology & Imaging Sciences, Medicine and Urology, Indiana University School of Medicine, Indianapolis, Indianapolis, USA
| | - Ulas Bagci
- Machine & Hybrid Intelligence Lab, Department of Radiology, Northwestern University
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Ahmed TM, Kawamoto S, Hruban RH, Fishman EK, Soyer P, Chu LC. A primer on artificial intelligence in pancreatic imaging. Diagn Interv Imaging 2023; 104:435-447. [PMID: 36967355 DOI: 10.1016/j.diii.2023.03.002] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 03/06/2023] [Indexed: 06/18/2023]
Abstract
Artificial Intelligence (AI) is set to transform medical imaging by leveraging the vast data contained in medical images. Deep learning and radiomics are the two main AI methods currently being applied within radiology. Deep learning uses a layered set of self-correcting algorithms to develop a mathematical model that best fits the data. Radiomics converts imaging data into mineable features such as signal intensity, shape, texture, and higher-order features. Both methods have the potential to improve disease detection, characterization, and prognostication. This article reviews the current status of artificial intelligence in pancreatic imaging and critically appraises the quality of existing evidence using the radiomics quality score.
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Affiliation(s)
- Taha M Ahmed
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Hospital, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Satomi Kawamoto
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Hospital, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Ralph H Hruban
- Sol Goldman Pancreatic Research Center, Department of Pathology, Johns Hopkins Hospital, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Elliot K Fishman
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Hospital, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Philippe Soyer
- Université Paris Cité, Faculté de Médecine, Department of Radiology, Hôpital Cochin-APHP, 75014, 75006, Paris, France, 7501475006
| | - Linda C Chu
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Hospital, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA.
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22
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Yamada R, Tsuboi J, Murashima Y, Tanaka T, Nose K, Nakagawa H. Advances in the Early Diagnosis of Pancreatic Ductal Adenocarcinoma and Premalignant Pancreatic Lesions. Biomedicines 2023; 11:1687. [PMID: 37371782 DOI: 10.3390/biomedicines11061687] [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: 04/10/2023] [Revised: 05/23/2023] [Accepted: 06/09/2023] [Indexed: 06/29/2023] Open
Abstract
Pancreatic cancer is one of the most lethal human malignancies, in part because it is often diagnosed at late stages when surgery and systemic therapies are either unfeasible or ineffective. Therefore, diagnosing pancreatic cancer in earlier stages is important for effective treatment. However, because the signs and symptoms may be nonspecific and not apparent until the disease is at a late stage, the timely diagnoses of pancreatic cancer can be difficult to achieve. Recent studies have shown that selective screening and increased usage of biomarkers could improve the early diagnosis of pancreatic cancer. In this review, we discuss recent advancements in the early detection of pancreatic ductal carcinoma and precancerous lesions. These include innovations in imaging modalities, the diagnostic utility of various biomarkers, biopsy techniques, and population-based surveillance approaches. Additionally, we discuss how machine learning methods are being applied to develop integrated methods of identifying individuals at high risk of developing pancreatic disease. In the future, the overall survival of pancreatic cancer patients could be improved by the development and adoption of these new methods and techniques.
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Affiliation(s)
- Reiko Yamada
- Department of Gastroenterology and Hepatology, School of Medicine, Mie University, Tsu 514-8507, Japan
| | - Junya Tsuboi
- Department of Gastroenterology and Hepatology, School of Medicine, Mie University, Tsu 514-8507, Japan
| | - Yumi Murashima
- Department of Gastroenterology and Hepatology, School of Medicine, Mie University, Tsu 514-8507, Japan
| | - Takamitsu Tanaka
- Department of Gastroenterology and Hepatology, School of Medicine, Mie University, Tsu 514-8507, Japan
| | - Kenji Nose
- Department of Gastroenterology and Hepatology, School of Medicine, Mie University, Tsu 514-8507, Japan
| | - Hayato Nakagawa
- Department of Gastroenterology and Hepatology, School of Medicine, Mie University, Tsu 514-8507, Japan
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23
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Jiang J, Chao WL, Culp S, Krishna SG. Artificial Intelligence in the Diagnosis and Treatment of Pancreatic Cystic Lesions and Adenocarcinoma. Cancers (Basel) 2023; 15:2410. [PMID: 37173876 PMCID: PMC10177524 DOI: 10.3390/cancers15092410] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 04/20/2023] [Accepted: 04/20/2023] [Indexed: 05/15/2023] Open
Abstract
Pancreatic cancer is projected to become the second leading cause of cancer-related mortality in the United States by 2030. This is in part due to the paucity of reliable screening and diagnostic options for early detection. Amongst known pre-malignant pancreatic lesions, pancreatic intraepithelial neoplasia (PanIN) and intraductal papillary mucinous neoplasms (IPMNs) are the most prevalent. The current standard of care for the diagnosis and classification of pancreatic cystic lesions (PCLs) involves cross-sectional imaging studies and endoscopic ultrasound (EUS) and, when indicated, EUS-guided fine needle aspiration and cyst fluid analysis. However, this is suboptimal for the identification and risk stratification of PCLs, with accuracy of only 65-75% for detecting mucinous PCLs. Artificial intelligence (AI) is a promising tool that has been applied to improve accuracy in screening for solid tumors, including breast, lung, cervical, and colon cancer. More recently, it has shown promise in diagnosing pancreatic cancer by identifying high-risk populations, risk-stratifying premalignant lesions, and predicting the progression of IPMNs to adenocarcinoma. This review summarizes the available literature on artificial intelligence in the screening and prognostication of precancerous lesions in the pancreas, and streamlining the diagnosis of pancreatic cancer.
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Affiliation(s)
- Joanna Jiang
- Department of Internal Medicine, Ohio State University Wexner Medical Center, Columbus, OH 43210, USA
| | - Wei-Lun Chao
- Department of Computer Science and Engineering, The Ohio State University, Columbus, OH 43210, USA
| | - Stacey Culp
- Department of Biomedical Informatics, The Ohio State University College of Medicine, Columbus, OH 43210, USA
| | - Somashekar G. Krishna
- Division of Gastroenterology, Hepatology, and Nutrition, Department of Internal Medicine, Ohio State University Wexner Medical Ceter, Columbus, OH 43210, USA
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24
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Faur AC, Lazar DC, Ghenciu LA. Artificial intelligence as a noninvasive tool for pancreatic cancer prediction and diagnosis. World J Gastroenterol 2023; 29:1811-1823. [PMID: 37032728 PMCID: PMC10080704 DOI: 10.3748/wjg.v29.i12.1811] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 12/23/2022] [Accepted: 03/15/2023] [Indexed: 03/28/2023] Open
Abstract
Pancreatic cancer (PC) has a low incidence rate but a high mortality, with patients often in the advanced stage of the disease at the time of the first diagnosis. If detected, early neoplastic lesions are ideal for surgery, offering the best prognosis. Preneoplastic lesions of the pancreas include pancreatic intraepithelial neoplasia and mucinous cystic neoplasms, with intraductal papillary mucinous neoplasms being the most commonly diagnosed. Our study focused on predicting PC by identifying early signs using noninvasive techniques and artificial intelligence (AI). A systematic English literature search was conducted on the PubMed electronic database and other sources. We obtained a total of 97 studies on the subject of pancreatic neoplasms. The final number of articles included in our study was 44, 34 of which focused on the use of AI algorithms in the early diagnosis and prediction of pancreatic lesions. AI algorithms can facilitate diagnosis by analyzing massive amounts of data in a short period of time. Correlations can be made through AI algorithms by expanding image and electronic medical records databases, which can later be used as part of a screening program for the general population. AI-based screening models should involve a combination of biomarkers and medical and imaging data from different sources. This requires large numbers of resources, collaboration between medical practitioners, and investment in medical infrastructures.
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Affiliation(s)
- Alexandra Corina Faur
- Department of Anatomy and Embriology, “Victor Babeș” University of Medicine and Pharmacy Timișoara, Timișoara 300041, Timiș, Romania
| | - Daniela Cornelia Lazar
- Department V of Internal Medicine I, Discipline of Internal Medicine IV, University of Medicine and Pharmacy “Victor Babes” Timișoara, Timișoara 300041, Timiș, Romania
| | - Laura Andreea Ghenciu
- Department III, Discipline of Pathophysiology, “Victor Babeș” University of Medicine and Pharmacy, Timișoara 300041, Timiș, Romania
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25
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Tovar DR, Rosenthal MH, Maitra A, Koay EJ. Potential of artificial intelligence in the risk stratification for and early detection of pancreatic cancer. ARTIFICIAL INTELLIGENCE SURGERY 2023; 3:14-26. [PMID: 37124705 PMCID: PMC10141523 DOI: 10.20517/ais.2022.38] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is the third most lethal cancer in the United States, with a 5-year life expectancy of 11%. Most symptoms manifest at an advanced stage of the disease when surgery is no longer appropriate. The dire prognosis of PDAC warrants new strategies to improve the outcomes of patients, and early detection has garnered significant attention. However, early detection of PDAC is most often incidental, emphasizing the importance of developing new early detection screening strategies. Due to the low incidence of the disease in the general population, much of the focus for screening has turned to individuals at high risk of PDAC. This enriches the screening population and balances the risks associated with pancreas interventions. The cancers that are found in these high-risk individuals by MRI and/or EUS screening show favorable 73% 5-year overall survival. Even with the emphasis on screening in enriched high-risk populations, only a minority of incident cancers are detected this way. One strategy to improve early detection outcomes is to integrate artificial intelligence (AI) into biomarker discovery and risk models. This expert review summarizes recent publications that have developed AI algorithms for the applications of risk stratification of PDAC using radiomics and electronic health records. Furthermore, this review illustrates the current uses of radiomics and biomarkers in AI for early detection of PDAC. Finally, various challenges and potential solutions are highlighted regarding the use of AI in medicine for early detection purposes.
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Affiliation(s)
- Daniela R. Tovar
- Department of Gastrointestinal Radiation Oncology, The University of Texas, Anderson Cancer Center, Houston, TX 77030, USA
| | | | - Anirban Maitra
- Department of Radiology, The University of Texas, Anderson Cancer Center, Houston, TX 77030, USA
| | - Eugene J. Koay
- Department of Gastrointestinal Radiation Oncology, The University of Texas, Anderson Cancer Center, Houston, TX 77030, USA
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26
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Zhang P, Xiao Y, Sun X, Lin X, Koo S, Yaremenko AV, Qin D, Kong N, Farokhzad OC, Tao W. Cancer nanomedicine toward clinical translation: Obstacles, opportunities, and future prospects. MED 2023; 4:147-167. [PMID: 36549297 DOI: 10.1016/j.medj.2022.12.001] [Citation(s) in RCA: 88] [Impact Index Per Article: 44.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 10/03/2022] [Accepted: 12/01/2022] [Indexed: 12/24/2022]
Abstract
With the integration of nanotechnology into the medical field at large, great strides have been made in the development of nanomedicines for tackling different diseases, including cancers. To date, various cancer nanomedicines have demonstrated success in preclinical studies, improving therapeutic outcomes, prolonging survival, and/or decreasing side effects. However, the translation from bench to bedside remains challenging. While a number of nanomedicines have entered clinical trials, only a few have been approved for clinical applications. In this review, we highlight the most recent progress in cancer nanomedicine, discuss current clinical advances and challenges for the translation of cancer nanomedicines, and provide our viewpoints on accelerating clinical translation. We expect this review to benefit the future development of cancer nanotherapeutics specifically from the clinical perspective.
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Affiliation(s)
- Pengfei Zhang
- Institute of Molecular Immunology, School of Laboratory Medicine and Biotechnology, Southern Medical University, Guangzhou 510080, China
| | - Yufen Xiao
- Center for Nanomedicine, Department of Anesthesiology, Perioperative, and Pain Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Xue Sun
- Department of Neurosurgery, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen 361004, China
| | - Xiaoning Lin
- Department of Neurosurgery, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen 361004, China
| | - Seyoung Koo
- Center for Nanomedicine, Department of Anesthesiology, Perioperative, and Pain Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Alexey V Yaremenko
- Center for Nanomedicine, Department of Anesthesiology, Perioperative, and Pain Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Duotian Qin
- Center for Nanomedicine, Department of Anesthesiology, Perioperative, and Pain Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Na Kong
- Center for Nanomedicine, Department of Anesthesiology, Perioperative, and Pain Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Omid C Farokhzad
- Center for Nanomedicine, Department of Anesthesiology, Perioperative, and Pain Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA; Seer, Inc., Redwood City, CA 94065, USA
| | - Wei Tao
- Center for Nanomedicine, Department of Anesthesiology, Perioperative, and Pain Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.
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27
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Scherübl H. [Prevention of pancreatic cancer]. Dtsch Med Wochenschr 2023; 148:246-252. [PMID: 36848888 DOI: 10.1055/a-1975-2366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/01/2023]
Abstract
The incidence of pancreatic cancer is rising in Germany. At present pancreatic cancer is the third commonest cause of cancer death but is expected to become the second in 2030 and finally the leading cause of cancer death in 2050. Pancreatic ductal adenocarcinoma (PC) is generally diagnosed at far advanced stages and 5-year-survival has remained poor. Modifiable risk factors of PC are tobacco smoking, excess body weight, alcohol use, type 2-diabetes and the metabolic syndrome. Smoking cessation and -in case of obesity- intentional weight loss can reduce PC risk by as much as 50 %. Early detection of asymptomatic sporadic PC at stage IA - stage IA-PC now has a 5-year-survival rate of about 80 %- has become a realistic chance for people older than 50 years with new-onset diabetes.
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Affiliation(s)
- Hans Scherübl
- Klinik für Gastroenterologie, Gastrointestinale Onkologie und Infektiologie, Vivantes Klinikum am Urban, 10967 Berlin
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28
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Granata V, Fusco R, Setola SV, Galdiero R, Maggialetti N, Silvestro L, De Bellis M, Di Girolamo E, Grazzini G, Chiti G, Brunese MC, Belli A, Patrone R, Palaia R, Avallone A, Petrillo A, Izzo F. Risk Assessment and Pancreatic Cancer: Diagnostic Management and Artificial Intelligence. Cancers (Basel) 2023; 15:351. [PMID: 36672301 PMCID: PMC9857317 DOI: 10.3390/cancers15020351] [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: 11/16/2022] [Revised: 12/30/2022] [Accepted: 01/03/2023] [Indexed: 01/06/2023] Open
Abstract
Pancreatic cancer (PC) is one of the deadliest cancers, and it is responsible for a number of deaths almost equal to its incidence. The high mortality rate is correlated with several explanations; the main one is the late disease stage at which the majority of patients are diagnosed. Since surgical resection has been recognised as the only curative treatment, a PC diagnosis at the initial stage is believed the main tool to improve survival. Therefore, patient stratification according to familial and genetic risk and the creation of screening protocol by using minimally invasive diagnostic tools would be appropriate. Pancreatic cystic neoplasms (PCNs) are subsets of lesions which deserve special management to avoid overtreatment. The current PC screening programs are based on the annual employment of magnetic resonance imaging with cholangiopancreatography sequences (MR/MRCP) and/or endoscopic ultrasonography (EUS). For patients unfit for MRI, computed tomography (CT) could be proposed, although CT results in lower detection rates, compared to MRI, for small lesions. The actual major limit is the incapacity to detect and characterize the pancreatic intraepithelial neoplasia (PanIN) by EUS and MR/MRCP. The possibility of utilizing artificial intelligence models to evaluate higher-risk patients could favour the diagnosis of these entities, although more data are needed to support the real utility of these applications in the field of screening. For these motives, it would be appropriate to realize screening programs in research settings.
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Affiliation(s)
- Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 41012 Napoli, Italy
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via della Signora 2, 20122 Milan, Italy
| | - Sergio Venanzio Setola
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Roberta Galdiero
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Nicola Maggialetti
- Department of Medical Science, Neuroscience and Sensory Organs (DSMBNOS), University of Bari “Aldo Moro”, 70124 Bari, Italy
| | - Lucrezia Silvestro
- Division of Clinical Experimental Oncology Abdomen, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Mario De Bellis
- Division of Gastroenterology and Digestive Endoscopy, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Elena Di Girolamo
- Division of Gastroenterology and Digestive Endoscopy, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Giulia Grazzini
- Department of Emergency Radiology, University Hospital Careggi, Largo Brambilla 3, 50134 Florence, Italy
| | - Giuditta Chiti
- Department of Emergency Radiology, University Hospital Careggi, Largo Brambilla 3, 50134 Florence, Italy
| | - Maria Chiara Brunese
- Diagnostic Imaging Section, Department of Medical and Surgical Sciences & Neurosciences, University of Molise, 86100 Campobasso, Italy
| | - Andrea Belli
- Division of Epatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Renato Patrone
- Division of Epatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Raffaele Palaia
- Division of Epatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Antonio Avallone
- Division of Clinical Experimental Oncology Abdomen, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Antonella Petrillo
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Francesco Izzo
- Division of Epatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
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29
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Javed S, Qureshi TA, Gaddam S, Wang L, Azab L, Wachsman AM, Chen W, Asadpour V, Jeon CY, Wu B, Xie Y, Pandol SJ, Li D. Risk prediction of pancreatic cancer using AI analysis of pancreatic subregions in computed tomography images. Front Oncol 2022; 12:1007990. [PMID: 36439445 PMCID: PMC9682250 DOI: 10.3389/fonc.2022.1007990] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Accepted: 10/24/2022] [Indexed: 10/14/2023] Open
Abstract
Early detection of Pancreatic Ductal Adenocarcinoma (PDAC) is complicated as PDAC remains asymptomatic until cancer advances to late stages when treatment is mostly ineffective. Stratifying the risk of developing PDAC can improve early detection as subsequent screening of high-risk individuals through specialized surveillance systems reduces the chance of misdiagnosis at the initial stage of cancer. Risk stratification is however challenging as PDAC lacks specific predictive biomarkers. Studies reported that the pancreas undergoes local morphological changes in response to underlying biological evolution associated with PDAC development. Accurate identification of these changes can help stratify the risk of PDAC. In this retrospective study, an extensive radiomic analysis of the precancerous pancreatic subregions was performed using abdominal Computed Tomography (CT) scans. The analysis was performed using 324 pancreatic subregions identified in 108 contrast-enhanced abdominal CT scans with equal proportion from healthy control, pre-diagnostic, and diagnostic groups. In a pairwise feature analysis, several textural features were found potentially predictive of PDAC. A machine learning classifier was then trained to perform risk prediction of PDAC by automatically classifying the CT scans into healthy control (low-risk) and pre-diagnostic (high-risk) classes and specifying the subregion(s) likely to develop a tumor. The proposed model was trained on CT scans from multiple phases. Whereas using 42 CT scans from the venous phase, model validation was performed which resulted in ~89.3% classification accuracy on average, with sensitivity and specificity reaching 86% and 93%, respectively, for predicting the development of PDAC (i.e., high-risk). To our knowledge, this is the first model that unveiled microlevel precancerous changes across pancreatic subregions and quantified the risk of developing PDAC. The model demonstrated improved prediction by 3.3% in comparison to the state-of-the-art method that considers the global (whole pancreas) features for PDAC prediction.
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Affiliation(s)
- Sehrish Javed
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Touseef Ahmad Qureshi
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Srinivas Gaddam
- Gastroenterology, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Lixia Wang
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Linda Azab
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Ashley Max Wachsman
- Department of Radiology, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Wansu Chen
- Department of Research and Evaluation, Southern California Kaiser Permanente Medical Center, Los Angeles, CA, United States
| | - Vahid Asadpour
- Department of Research and Evaluation, Southern California Kaiser Permanente Medical Center, Los Angeles, CA, United States
| | - Christie Younghae Jeon
- Division of Hematology, Cedars-Sinai Medical Center, Los Angeles, CA, United States
- Division of Oncology, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Beichien Wu
- Department of Research and Evaluation, Southern California Kaiser Permanente Medical Center, Los Angeles, CA, United States
| | - Yibin Xie
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | | | - Debiao Li
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States
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30
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Rosenthal M, Schawkat K, Wolpin B. A Growing Hope for Earlier Detection of Pancreatic Cancer. Gastroenterology 2022; 163:1170-1172. [PMID: 35961377 DOI: 10.1053/j.gastro.2022.07.080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 07/26/2022] [Indexed: 12/02/2022]
Affiliation(s)
- Michael Rosenthal
- Department of Radiology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.
| | - Khoschy Schawkat
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Brian Wolpin
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
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31
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Wright DE, Mukherjee S, Patra A, Khasawneh H, Korfiatis P, Suman G, Chari ST, Kudva YC, Kline TL, Goenka AH. Radiomics-based machine learning (ML) classifier for detection of type 2 diabetes on standard-of-care abdomen CTs: a proof-of-concept study. Abdom Radiol (NY) 2022; 47:3806-3816. [PMID: 36085379 DOI: 10.1007/s00261-022-03668-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 08/26/2022] [Accepted: 08/27/2022] [Indexed: 01/18/2023]
Abstract
PURPOSE To determine if pancreas radiomics-based AI model can detect the CT imaging signature of type 2 diabetes (T2D). METHODS Total 107 radiomic features were extracted from volumetrically segmented normal pancreas in 422 T2D patients and 456 age-matched controls. Dataset was randomly split into training (300 T2D, 300 control CTs) and test subsets (122 T2D, 156 control CTs). An XGBoost model trained on 10 features selected through top-K-based selection method and optimized through threefold cross-validation on training subset was evaluated on test subset. RESULTS Model correctly classified 73 (60%) T2D patients and 96 (62%) controls yielding F1-score, sensitivity, specificity, precision, and AUC of 0.57, 0.62, 0.61, 0.55, and 0.65, respectively. Model's performance was equivalent across gender, CT slice thicknesses, and CT vendors (p values > 0.05). There was no difference between correctly classified versus misclassified patients in the mean (range) T2D duration [4.5 (0-15.4) versus 4.8 (0-15.7) years, p = 0.8], antidiabetic treatment [insulin (22% versus 18%), oral antidiabetics (10% versus 18%), both (41% versus 39%) (p > 0.05)], and treatment duration [5.4 (0-15) versus 5 (0-13) years, p = 0.4]. CONCLUSION Pancreas radiomics-based AI model can detect the imaging signature of T2D. Further refinement and validation are needed to evaluate its potential for opportunistic T2D detection on millions of CTs that are performed annually.
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Affiliation(s)
- Darryl E Wright
- Department of Radiology, Mayo Clinic, 200 First Street SW, Charlton 1, Rochester, MN, 55905, USA
| | - Sovanlal Mukherjee
- Department of Radiology, Mayo Clinic, 200 First Street SW, Charlton 1, Rochester, MN, 55905, USA
| | - Anurima Patra
- Department of Radiology, Tata Medical Center, Kolkata, 700160, India
| | - Hala Khasawneh
- Department of Radiology, Mayo Clinic, 200 First Street SW, Charlton 1, Rochester, MN, 55905, USA
| | - Panagiotis Korfiatis
- Department of Radiology, Mayo Clinic, 200 First Street SW, Charlton 1, Rochester, MN, 55905, USA
| | - Garima Suman
- Department of Radiology, Mayo Clinic, 200 First Street SW, Charlton 1, Rochester, MN, 55905, USA
| | - Suresh T Chari
- Department of Gastroenterology, Hepatology and Nutrition, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA
- Department of Gastroenterology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA
| | - Yogish C Kudva
- Department of Endocrinology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA
| | - Timothy L Kline
- Department of Radiology, Mayo Clinic, 200 First Street SW, Charlton 1, Rochester, MN, 55905, USA
| | - Ajit H Goenka
- Department of Radiology, Mayo Clinic, 200 First Street SW, Charlton 1, Rochester, MN, 55905, USA.
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Hameed BS, Krishnan UM. Artificial Intelligence-Driven Diagnosis of Pancreatic Cancer. Cancers (Basel) 2022; 14:5382. [PMID: 36358800 PMCID: PMC9657087 DOI: 10.3390/cancers14215382] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 10/28/2022] [Accepted: 10/28/2022] [Indexed: 08/01/2023] Open
Abstract
Pancreatic cancer is among the most challenging forms of cancer to treat, owing to its late diagnosis and aggressive nature that reduces the survival rate drastically. Pancreatic cancer diagnosis has been primarily based on imaging, but the current state-of-the-art imaging provides a poor prognosis, thus limiting clinicians' treatment options. The advancement of a cancer diagnosis has been enhanced through the integration of artificial intelligence and imaging modalities to make better clinical decisions. In this review, we examine how AI models can improve the diagnosis of pancreatic cancer using different imaging modalities along with a discussion on the emerging trends in an AI-driven diagnosis, based on cytopathology and serological markers. Ethical concerns regarding the use of these tools have also been discussed.
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Affiliation(s)
- Bahrudeen Shahul Hameed
- Centre for Nanotechnology & Advanced Biomaterials (CeNTAB), Shanmugha Arts, Science, Technology and Research Academy, Deemed University, Thanjavur 613401, India
- School of Chemical & Biotechnology (SCBT), Shanmugha Arts, Science, Technology and Research Academy, Deemed University, Thanjavur 613401, India
| | - Uma Maheswari Krishnan
- Centre for Nanotechnology & Advanced Biomaterials (CeNTAB), Shanmugha Arts, Science, Technology and Research Academy, Deemed University, Thanjavur 613401, India
- School of Chemical & Biotechnology (SCBT), Shanmugha Arts, Science, Technology and Research Academy, Deemed University, Thanjavur 613401, India
- School of Arts, Sciences, Humanities & Education (SASHE), Shanmugha Arts, Science, Technology and Research Academy, Deemed University, Thanjavur 613401, India
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Huang B, Huang H, Zhang S, Zhang D, Shi Q, Liu J, Guo J. Artificial intelligence in pancreatic cancer. Theranostics 2022; 12:6931-6954. [PMID: 36276650 PMCID: PMC9576619 DOI: 10.7150/thno.77949] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 09/24/2022] [Indexed: 11/30/2022] Open
Abstract
Pancreatic cancer is the deadliest disease, with a five-year overall survival rate of just 11%. The pancreatic cancer patients diagnosed with early screening have a median overall survival of nearly ten years, compared with 1.5 years for those not diagnosed with early screening. Therefore, early diagnosis and early treatment of pancreatic cancer are particularly critical. However, as a rare disease, the general screening cost of pancreatic cancer is high, the accuracy of existing tumor markers is not enough, and the efficacy of treatment methods is not exact. In terms of early diagnosis, artificial intelligence technology can quickly locate high-risk groups through medical images, pathological examination, biomarkers, and other aspects, then screening pancreatic cancer lesions early. At the same time, the artificial intelligence algorithm can also be used to predict the survival time, recurrence risk, metastasis, and therapy response which could affect the prognosis. In addition, artificial intelligence is widely used in pancreatic cancer health records, estimating medical imaging parameters, developing computer-aided diagnosis systems, etc. Advances in AI applications for pancreatic cancer will require a concerted effort among clinicians, basic scientists, statisticians, and engineers. Although it has some limitations, it will play an essential role in overcoming pancreatic cancer in the foreseeable future due to its mighty computing power.
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Affiliation(s)
- Bowen Huang
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China
- School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Haoran Huang
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China
- School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Shuting Zhang
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China
- School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Dingyue Zhang
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China
- School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Qingya Shi
- School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Jianzhou Liu
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China
| | - Junchao Guo
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China
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Manickam P, Mariappan SA, Murugesan SM, Hansda S, Kaushik A, Shinde R, Thipperudraswamy SP. Artificial Intelligence (AI) and Internet of Medical Things (IoMT) Assisted Biomedical Systems for Intelligent Healthcare. BIOSENSORS 2022; 12:bios12080562. [PMID: 35892459 PMCID: PMC9330886 DOI: 10.3390/bios12080562] [Citation(s) in RCA: 110] [Impact Index Per Article: 36.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 07/20/2022] [Accepted: 07/21/2022] [Indexed: 05/05/2023]
Abstract
Artificial intelligence (AI) is a modern approach based on computer science that develops programs and algorithms to make devices intelligent and efficient for performing tasks that usually require skilled human intelligence. AI involves various subsets, including machine learning (ML), deep learning (DL), conventional neural networks, fuzzy logic, and speech recognition, with unique capabilities and functionalities that can improve the performances of modern medical sciences. Such intelligent systems simplify human intervention in clinical diagnosis, medical imaging, and decision-making ability. In the same era, the Internet of Medical Things (IoMT) emerges as a next-generation bio-analytical tool that combines network-linked biomedical devices with a software application for advancing human health. In this review, we discuss the importance of AI in improving the capabilities of IoMT and point-of-care (POC) devices used in advanced healthcare sectors such as cardiac measurement, cancer diagnosis, and diabetes management. The role of AI in supporting advanced robotic surgeries developed for advanced biomedical applications is also discussed in this article. The position and importance of AI in improving the functionality, detection accuracy, decision-making ability of IoMT devices, and evaluation of associated risks assessment is discussed carefully and critically in this review. This review also encompasses the technological and engineering challenges and prospects for AI-based cloud-integrated personalized IoMT devices for designing efficient POC biomedical systems suitable for next-generation intelligent healthcare.
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Affiliation(s)
- Pandiaraj Manickam
- Electrodics and Electrocatalysis Division, CSIR-Central Electrochemical Research Institute (CECRI), Karaikudi, Sivagangai 630003, Tamil Nadu, India; (S.A.M.); (S.M.M.)
- Academy of Scientific & Innovative Research (AcSIR), Ghaziabad 201002, Uttar Pradesh, India; (S.H.); (S.P.T.)
- Correspondence:
| | - Siva Ananth Mariappan
- Electrodics and Electrocatalysis Division, CSIR-Central Electrochemical Research Institute (CECRI), Karaikudi, Sivagangai 630003, Tamil Nadu, India; (S.A.M.); (S.M.M.)
- Academy of Scientific & Innovative Research (AcSIR), Ghaziabad 201002, Uttar Pradesh, India; (S.H.); (S.P.T.)
| | - Sindhu Monica Murugesan
- Electrodics and Electrocatalysis Division, CSIR-Central Electrochemical Research Institute (CECRI), Karaikudi, Sivagangai 630003, Tamil Nadu, India; (S.A.M.); (S.M.M.)
| | - Shekhar Hansda
- Academy of Scientific & Innovative Research (AcSIR), Ghaziabad 201002, Uttar Pradesh, India; (S.H.); (S.P.T.)
- Corrosion and Materials Protection Division, CSIR-Central Electrochemical Research Institute (CECRI), Karaikudi, Sivagangai 630003, Tamil Nadu, India
| | - Ajeet Kaushik
- School of Engineering, University of Petroleum and Energy Studies (UPES), Dehradun 248001, Uttarakhand, India;
- NanoBioTech Laboratory, Department of Environmental Engineering, Florida Polytechnic University, Lakeland, FL 33805-8531, USA
| | - Ravikumar Shinde
- Department of Zoology, Shri Pundlik Maharaj Mahavidyalaya Nandura, Buldana 443404, Maharashtra, India;
| | - S. P. Thipperudraswamy
- Academy of Scientific & Innovative Research (AcSIR), Ghaziabad 201002, Uttar Pradesh, India; (S.H.); (S.P.T.)
- Central Instrument Facility, CSIR-Central Electrochemical Research Institute, Karaikudi, Sivagangai 630003, Tamil Nadu, India
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Segmentation of Pancreatic Subregions in Computed Tomography Images. J Imaging 2022; 8:jimaging8070195. [PMID: 35877639 PMCID: PMC9317715 DOI: 10.3390/jimaging8070195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 07/02/2022] [Accepted: 07/07/2022] [Indexed: 11/16/2022] Open
Abstract
The accurate segmentation of pancreatic subregions (head, body, and tail) in CT images provides an opportunity to examine the local morphological and textural changes in the pancreas. Quantifying such changes aids in understanding the spatial heterogeneity of the pancreas and assists in the diagnosis and treatment planning of pancreatic cancer. Manual outlining of pancreatic subregions is tedious, time-consuming, and prone to subjective inconsistency. This paper presents a multistage anatomy-guided framework for accurate and automatic 3D segmentation of pancreatic subregions in CT images. Using the delineated pancreas, two soft-label maps were estimated for subregional segmentation—one by training a fully supervised naïve Bayes model that considers the length and volumetric proportions of each subregional structure based on their anatomical arrangement, and the other by using the conventional deep learning U-Net architecture for 3D segmentation. The U-Net model then estimates the joint probability of the two maps and performs optimal segmentation of subregions. Model performance was assessed using three datasets of contrast-enhanced abdominal CT scans: one public NIH dataset of the healthy pancreas, and two datasets D1 and D2 (one for each of pre-cancerous and cancerous pancreas). The model demonstrated excellent performance during the multifold cross-validation using the NIH dataset, and external validation using D1 and D2. To the best of our knowledge, this is the first automated model for the segmentation of pancreatic subregions in CT images. A dataset consisting of reference anatomical labels for subregions in all images of the NIH dataset is also established.
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Chen X, Fu R, Shao Q, Chen Y, Ye Q, Li S, He X, Zhu J. Application of artificial intelligence to pancreatic adenocarcinoma. Front Oncol 2022; 12:960056. [PMID: 35936738 PMCID: PMC9353734 DOI: 10.3389/fonc.2022.960056] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 06/24/2022] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND AND OBJECTIVES Pancreatic cancer (PC) is one of the deadliest cancers worldwide although substantial advancement has been made in its comprehensive treatment. The development of artificial intelligence (AI) technology has allowed its clinical applications to expand remarkably in recent years. Diverse methods and algorithms are employed by AI to extrapolate new data from clinical records to aid in the treatment of PC. In this review, we will summarize AI's use in several aspects of PC diagnosis and therapy, as well as its limits and potential future research avenues. METHODS We examine the most recent research on the use of AI in PC. The articles are categorized and examined according to the medical task of their algorithm. Two search engines, PubMed and Google Scholar, were used to screen the articles. RESULTS Overall, 66 papers published in 2001 and after were selected. Of the four medical tasks (risk assessment, diagnosis, treatment, and prognosis prediction), diagnosis was the most frequently researched, and retrospective single-center studies were the most prevalent. We found that the different medical tasks and algorithms included in the reviewed studies caused the performance of their models to vary greatly. Deep learning algorithms, on the other hand, produced excellent results in all of the subdivisions studied. CONCLUSIONS AI is a promising tool for helping PC patients and may contribute to improved patient outcomes. The integration of humans and AI in clinical medicine is still in its infancy and requires the in-depth cooperation of multidisciplinary personnel.
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Affiliation(s)
- Xi Chen
- Department of General Surgery, Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, China
| | - Ruibiao Fu
- Department of General Surgery, Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, China
| | - Qian Shao
- Department of Surgical Ward 1, Ningbo Women and Children’s Hospital, Ningbo, China
| | - Yan Chen
- Department of General Surgery, Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, China
| | - Qinghuang Ye
- Department of General Surgery, Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, China
| | - Sheng Li
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Xiongxiong He
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Jinhui Zhu
- Department of General Surgery, Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, China
- *Correspondence: Jinhui Zhu,
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