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Dai J, He C, Jin L, Chen C, Wu J, Bian Y. A deep learning detection method for pancreatic cystic neoplasm based on Mamba architecture. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2025; 33:461-471. [PMID: 39973786 DOI: 10.1177/08953996251313719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/21/2025]
Abstract
OBJECTIVE Early diagnosis of pancreatic cystic neoplasm (PCN) is crucial for patient survival. This study proposes M-YOLO, a novel model combining Mamba architecture and YOLO, to enhance the detection of pancreatic cystic tumors. The model addresses the technical challenge posed by the tumors' complex morphological features in medical images. METHODS This study develops an innovative deep learning network architecture, M-YOLO (Mamba YOLOv10), which combines the advantages of Mamba and YOLOv10 and aims to improve the accuracy and efficiency of pancreatic cystic neoplasm(PCN) detection. The Mamba architecture, with its superior sequence modeling capabilities, is ideally suited for processing the rich contextual information contained in medical images. At the same time, YOLOv10's fast object detection feature ensures the system's viability for application in clinical practice. RESULTS M-YOLO has a high sensitivity of 0.98, a specificity of 0.92, a precision of 0.96, an F1 value of 0.97, an accuracy of 0.93, as well as a mean average precision (mAP) of 0.96 at 50% intersection-to-union (IoU) threshold on the dataset provided by Changhai Hospital. CONCLUSIONS M-YOLO(Mamba YOLOv10) enhances the identification performance of PCN by integrating the deep feature extraction capability of Mamba and the fast localization technique of YOLOv10.
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Affiliation(s)
- Junlong Dai
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Cong He
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Liang Jin
- Department of Radiology, Huadong Hospital, Fudan University, Shanghai, China
| | - Chengwei Chen
- Department of Radiology, First Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai, China
| | - Jie Wu
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Yun Bian
- Department of Radiology, First Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai, China
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Qadir MI, Baril JA, Yip-Schneider MT, Schonlau D, Tran TTT, Schmidt CM, Kolbinger FR. Artificial Intelligence in Pancreatic Intraductal Papillary Mucinous Neoplasm Imaging: A Systematic Review. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2025:2025.01.08.25320130. [PMID: 39830259 PMCID: PMC11741484 DOI: 10.1101/2025.01.08.25320130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 01/22/2025]
Abstract
Background Based on the Fukuoka and Kyoto international consensus guidelines, the current clinical management of intraductal papillary mucinous neoplasm (IPMN) largely depends on imaging features. While these criteria are highly sensitive in detecting high-risk IPMN, they lack specificity, resulting in surgical overtreatment. Artificial Intelligence (AI)-based medical image analysis has the potential to augment the clinical management of IPMNs by improving diagnostic accuracy. Methods Based on a systematic review of the academic literature on AI in IPMN imaging, 1041 publications were identified of which 25 published studies were included in the analysis. The studies were stratified based on prediction target, underlying data type and imaging modality, patient cohort size, and stage of clinical translation and were subsequently analyzed to identify trends and gaps in the field. Results Research on AI in IPMN imaging has been increasing in recent years. The majority of studies utilized CT imaging to train computational models. Most studies presented computational models developed on single-center datasets (n=11,44%) and included less than 250 patients (n=18,72%). Methodologically, convolutional neural network (CNN)-based algorithms were most commonly used. Thematically, most studies reported models augmenting differential diagnosis (n=9,36%) or risk stratification (n=10,40%) rather than IPMN detection (n=5,20%) or IPMN segmentation (n=2,8%). Conclusion This systematic review provides a comprehensive overview of the research landscape of AI in IPMN imaging. Computational models have potential to enhance the accurate and precise stratification of patients with IPMN. Multicenter collaboration and datasets comprising various modalities are necessary to fully utilize this potential, alongside concerted efforts towards clinical translation.
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Affiliation(s)
| | - Jackson A. Baril
- Division of Surgical Oncology, Department of Surgery, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Michele T. Yip-Schneider
- Division of Surgical Oncology, Department of Surgery, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Duane Schonlau
- Department of Radiology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Thi Thanh Thoa Tran
- Division of Surgical Oncology, Department of Surgery, Indiana University School of Medicine, Indianapolis, IN, USA
| | - C. Max Schmidt
- Division of Surgical Oncology, Department of Surgery, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Fiona R. Kolbinger
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA
- Regenstrief Center for Healthcare Engineering (RCHE), Purdue University, West Lafayette, IN, USA
- Department of Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health, Indiana University, Indianapolis, IN, USA
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Nadeem A, Ashraf R, Mahmood T, Parveen S. Automated CAD system for early detection and classification of pancreatic cancer using deep learning model. PLoS One 2025; 20:e0307900. [PMID: 39752442 PMCID: PMC11698441 DOI: 10.1371/journal.pone.0307900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Accepted: 07/10/2024] [Indexed: 01/06/2025] Open
Abstract
Accurate diagnosis of pancreatic cancer using CT scan images is critical for early detection and treatment, potentially saving numerous lives globally. Manual identification of pancreatic tumors by radiologists is challenging and time-consuming due to the complex nature of CT scan images and variations in tumor shape, size, and location of the pancreatic tumor also make it challenging to detect and classify different types of tumors. Thus, to address this challenge we proposed a four-stage framework of computer-aided diagnosis systems. In the preprocessing stage, the input image resizes into 227 × 227 dimensions then converts the RGB image into a grayscale image, and enhances the image by removing noise without blurring edges by applying anisotropic diffusion filtering. In the segmentation stage, the preprocessed grayscale image a binary image is created based on a threshold, highlighting the edges by Sobel filtering, and watershed segmentation to segment the tumor region and we also implement the U-Net method for segmentation. Then refine the geometric structure of the image using morphological operation and extracting the texture features from the image using a gray-level co-occurrence matrix computed by analyzing the spatial relationship of pixel intensities in the refined image, counting the occurrences of pixel pairs with specific intensity values and spatial relationships. The detection stage analyzes the tumor region's extracted features characteristics by labeling the connected components and selecting the region with the highest density to locate the tumor area, achieving a good accuracy of 99.64%. In the classification stage, the system classifies the detected tumor into the normal, pancreatic tumor, then into benign, pre-malignant, or malignant using a proposed reduced 11-layer AlexNet model. The classification stage attained an accuracy level of 98.72%, an AUC of 0.9979, and an overall system average processing time of 1.51 seconds, demonstrating the capability of the system to effectively and efficiently identify and classify pancreatic cancers.
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Affiliation(s)
- Abubakar Nadeem
- Department of Computer Science, National Textile University, Faisalabad, Pakistan
| | - Rahan Ashraf
- Department of Computer Science, National Textile University, Faisalabad, Pakistan
| | - Toqeer Mahmood
- Department of Computer Science, National Textile University, Faisalabad, Pakistan
| | - Sajida Parveen
- Department of Computer Science, National Textile University, Faisalabad, Pakistan
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Lu CX, Zhou J, Feng YC, Meng SJ, Guo XL, Su WS, Ngo T, Hsu TH, Lin P, Huang J, Liu ST, Palacio MLB, Change WL, Qin G, Hu YQ, Zhan LH. Artificial intelligence models assisting physicians in quantifying pancreatic necrosis in acute pancreatitis. Quant Imaging Med Surg 2025; 15:135-148. [PMID: 39839053 PMCID: PMC11744103 DOI: 10.21037/qims-24-841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2024] [Accepted: 11/11/2024] [Indexed: 01/23/2025]
Abstract
Background Acute pancreatitis (AP) is a potentially life-threatening condition characterized by inflammation of the pancreas, which can lead to complications such as pancreatic necrosis. The modified computed tomography severity index (MCTSI) is a widely used tool for assessing the severity of AP, particularly the extent of pancreatic necrosis. The accurate and timely assessment of the necrosis volume is crucial in guiding treatment decisions and improving patient outcomes. However, the current diagnostic process relies heavily on the manual interpretation of computed tomography (CT) scans, which can be subjective and prone to variability among clinicians. This study aimed to develop a deep-learning network model to assist clinicians in diagnosing the volume ratio of pancreatic necrosis based on the MCTSI for AP. Methods The datasets comprised retrospectively collected plain and contrast-enhanced CT scans from 144 patients (6 with scores of 0 points, 42 with scores of 2 points, and 65 with scores of 4 points) and the National Institutes of Health contrast-enhanced CT scans from 45 patients with scores of 0 points. An improved fully convolutional neural networks for volumetric medical image segmentation (V-Net) model was developed to segment the pancreatic volume (i.e., the whole pancreas, necrotic pancreatic tissue, and non-necrotic pancreatic tissue) and to quantify the split volume ratios. The improved strategy included three stages of body up- and down-sampling adapted to the task of segmentation in AP, and the selection of objects, loss function, and smoothing coefficients. The model interpretations were compared with those of clinicians with different levels of experience. The reference standard was manually segmented by a pancreatic radiologist. Accuracy, macro recall, and macro specificity were employed to compare the diagnostic efficacy of the model and the clinicians. Results In total, 144 patients (mean age: 44±13 years; 40 females, 104 males) were included in the study. Optimal training results were obtained using the necrotic pancreatic tissue and whole pancreas as the input objects, and combining dice loss and 500 smoothing coefficients as the loss function for training. The dice coefficient for the whole pancreas was 0.811 and that for the necrotic pancreatic tissue was 0.761. The performance of the artificial intelligence model and clinicians were compared. The accuracy, macro recall, and macro specificity of the improved V-net were 0.854, 0.850 and 0.923, respectively, which were all significantly higher than those of the senior and junior clinicians (P<0.05). Conclusions Our proposed model could improve the effectiveness of clinicians in diagnosing pancreatic necrosis volume ratios in clinical settings.
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Affiliation(s)
- Cheng-Xiang Lu
- Department of Intensive Care Unit, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Jiali Zhou
- Department of Gastroenterology, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
| | - Yong-Chang Feng
- California Science and Technology University, California, CA, USA
| | - Si-Jun Meng
- Jiying Technology Co., Ltd., Hong Kong, China
| | - Xue-Ling Guo
- Department of Intensive Care Unit, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Wen-Song Su
- Department of Intensive Care Unit, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Tue Ngo
- California Science and Technology University, California, CA, USA
| | - Tse Hao Hsu
- California Science and Technology University, California, CA, USA
| | - Peng Lin
- California Science and Technology University, California, CA, USA
| | - James Huang
- California Science and Technology University, California, CA, USA
| | - Si-Tong Liu
- California Science and Technology University, California, CA, USA
| | | | - Wei-Lin Change
- California Science and Technology University, California, CA, USA
| | - Glen Qin
- California Science and Technology University, California, CA, USA
| | - Yi-Qun Hu
- Department of Gastroenterology, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
| | - Ling-Hui Zhan
- Department of Intensive Care Unit, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
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Lopes Costa GL, Tasca Petroski G, Machado LG, Eulalio Santos B, de Oliveira Ramos F, Feuerschuette Neto LM, De Luca Canto G. Accuracy of machine learning models for pre-diagnosis and diagnosis of pancreatic ductal adenocarcinoma in contrast-CT images: a systematic review and meta-analysis. Abdom Radiol (NY) 2024:10.1007/s00261-024-04771-1. [PMID: 39720966 DOI: 10.1007/s00261-024-04771-1] [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: 08/22/2024] [Revised: 12/12/2024] [Accepted: 12/14/2024] [Indexed: 12/26/2024]
Abstract
PURPOSE To evaluate the diagnostic ability and methodological quality of ML models in detecting Pancreatic Ductal Adenocarcinoma (PDAC) in Contrast CT images. METHOD Included studies assessed adults diagnosed with PDAC, confirmed by histopathology. Metrics of tests were interpreted by ML algorithms. Studies provided data on sensitivity and specificity. Studies that did not meet the inclusion criteria, segmentation-focused studies, multiple classifiers or non-diagnostic studies were excluded. PubMed, Cochrane Central Register of Controlled Trials, and Embase were searched without restrictions. Risk of bias was assessed using QUADAS-2, methodological quality was evaluated using Radiomics Quality Score (RQS) and a Checklist for AI in Medical Imaging (CLAIM). Bivariate random-effects models were used for meta-analysis of sensitivity and specificity, I2 values and subgroup analysis used to assess heterogeneity. RESULTS Nine studies were included and 12,788 participants were evaluated, of which 3,997 were included in the meta-analysis. AI models based on CT scans showed an accuracy of 88.7% (IC 95%, 87.7%-89.7%), sensitivity of 87.9% (95% CI, 82.9%-91.6%), and specificity of 92.2% (95% CI, 86.8%-95.5%). The average score of six radiomics studies was 17.83 RQS points. Nine ML methods had an average CLAIM score of 30.55 points. CONCLUSIONS Our study is the first to quantitatively interpret various independent research, offering insights for clinical application. Despite favorable sensitivity and specificity results, the studies were of low quality, limiting definitive conclusions. Further research is necessary to validate these models before widespread adoption.
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Affiliation(s)
- Geraldo Lucas Lopes Costa
- Federal University of Santa Catarina, Florianópolis, Brazil.
- Brazilian Center for Evidence-Based Research, Federal University of Santa Catarina, Florianópolis, Brazil.
| | - Guido Tasca Petroski
- Federal University of Santa Catarina, Florianópolis, Brazil
- Brazilian Center for Evidence-Based Research, Federal University of Santa Catarina, Florianópolis, Brazil
| | - Luis Guilherme Machado
- Federal University of Santa Catarina, Florianópolis, Brazil
- Brazilian Center for Evidence-Based Research, Federal University of Santa Catarina, Florianópolis, Brazil
| | | | | | | | - Graziela De Luca Canto
- Brazilian Center for Evidence-Based Research, Federal University of Santa Catarina, Florianópolis, Brazil
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Seyithanoglu D, Durak G, Keles E, Medetalibeyoglu A, Hong Z, Zhang Z, Taktak YB, Cebeci T, Tiwari P, Velichko YS, Yazici C, Tirkes T, Miller FH, Keswani RN, Spampinato C, Wallace MB, Bagci U. Advances for Managing Pancreatic Cystic Lesions: Integrating Imaging and AI Innovations. Cancers (Basel) 2024; 16:4268. [PMID: 39766167 PMCID: PMC11674829 DOI: 10.3390/cancers16244268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2024] [Revised: 12/08/2024] [Accepted: 12/19/2024] [Indexed: 01/11/2025] Open
Abstract
Pancreatic cystic lesions (PCLs) represent a spectrum of non-neoplasms and neoplasms with varying malignant potential, posing significant challenges in diagnosis and management. While some PCLs are precursors to pancreatic cancer, others remain benign, necessitating accurate differentiation for optimal patient care. Conventional approaches to PCL management rely heavily on radiographic imaging, and endoscopic ultrasound (EUS) guided fine-needle aspiration (FNA), coupled with clinical and biochemical data. However, the observer-dependent nature of image interpretation and the complex morphology of PCLs can lead to diagnostic uncertainty and variability in patient management strategies. This review critically evaluates current PCL diagnosis and surveillance practices, showing features of the different lesions and highlighting the potential limitations of conventional methods. We then explore the potential of artificial intelligence (AI) to transform PCL management. AI-driven strategies, including deep learning algorithms for automated pancreas and lesion segmentation, and radiomics for analyzing heterogeneity, can improve diagnostic accuracy and risk stratification. These advanced techniques can provide more objective and reproducible assessments, aiding clinicians in decision-making regarding follow-up intervals and surgical interventions. Early results suggest that AI-driven methods can significantly improve patient outcomes by enabling earlier detection of high-risk lesions and reducing unnecessary procedures for benign cysts. Finally, this review emphasizes that AI-driven approaches could potentially reshape the landscape of PCL management, ultimately leading to improved pancreatic cancer prevention.
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Affiliation(s)
- Deniz Seyithanoglu
- Machine and Hybrid Intelligence Lab, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA; (D.S.); (G.D.); (E.K.); (A.M.); (Z.H.); (Z.Z.); (Y.S.V.); (F.H.M.); (R.N.K.)
- Istanbul Faculty of Medicine, Istanbul University, Istanbul 38000, Turkey; (Y.B.T.); (T.C.)
| | - Gorkem Durak
- Machine and Hybrid Intelligence Lab, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA; (D.S.); (G.D.); (E.K.); (A.M.); (Z.H.); (Z.Z.); (Y.S.V.); (F.H.M.); (R.N.K.)
| | - Elif Keles
- Machine and Hybrid Intelligence Lab, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA; (D.S.); (G.D.); (E.K.); (A.M.); (Z.H.); (Z.Z.); (Y.S.V.); (F.H.M.); (R.N.K.)
| | - Alpay Medetalibeyoglu
- Machine and Hybrid Intelligence Lab, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA; (D.S.); (G.D.); (E.K.); (A.M.); (Z.H.); (Z.Z.); (Y.S.V.); (F.H.M.); (R.N.K.)
- Istanbul Faculty of Medicine, Istanbul University, Istanbul 38000, Turkey; (Y.B.T.); (T.C.)
| | - Ziliang Hong
- Machine and Hybrid Intelligence Lab, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA; (D.S.); (G.D.); (E.K.); (A.M.); (Z.H.); (Z.Z.); (Y.S.V.); (F.H.M.); (R.N.K.)
| | - Zheyuan Zhang
- Machine and Hybrid Intelligence Lab, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA; (D.S.); (G.D.); (E.K.); (A.M.); (Z.H.); (Z.Z.); (Y.S.V.); (F.H.M.); (R.N.K.)
| | - Yavuz B. Taktak
- Istanbul Faculty of Medicine, Istanbul University, Istanbul 38000, Turkey; (Y.B.T.); (T.C.)
| | - Timurhan Cebeci
- Istanbul Faculty of Medicine, Istanbul University, Istanbul 38000, Turkey; (Y.B.T.); (T.C.)
| | - Pallavi Tiwari
- Department of Radiology, BME, University of Wisconsin-Madison, Madison, WI 53707, USA;
- William S. Middleton Memorial Veterans Affairs (VA) Healthcare, 2500 Overlook Terrace, Madison, WI 53705, USA
| | - Yuri S. Velichko
- Machine and Hybrid Intelligence Lab, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA; (D.S.); (G.D.); (E.K.); (A.M.); (Z.H.); (Z.Z.); (Y.S.V.); (F.H.M.); (R.N.K.)
| | - Cemal Yazici
- Department of Gastroenterology, University of Illinois at Chicago, Chicago, IL 60611, USA;
| | - Temel Tirkes
- Department of Radiology, Indiana University, Indianapolis, IN 46202, USA;
| | - Frank H. Miller
- Machine and Hybrid Intelligence Lab, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA; (D.S.); (G.D.); (E.K.); (A.M.); (Z.H.); (Z.Z.); (Y.S.V.); (F.H.M.); (R.N.K.)
| | - Rajesh N. Keswani
- Machine and Hybrid Intelligence Lab, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA; (D.S.); (G.D.); (E.K.); (A.M.); (Z.H.); (Z.Z.); (Y.S.V.); (F.H.M.); (R.N.K.)
| | - Concetto Spampinato
- Department of Electrical, Electronics and Computer Engineering, University of Catania, 95124 Catania, Italy;
| | - Michael B. Wallace
- Department of Gastroenterology, Mayo Clinic Florida, Jacksonville, FL 32224, USA;
| | - Ulas Bagci
- Machine and Hybrid Intelligence Lab, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA; (D.S.); (G.D.); (E.K.); (A.M.); (Z.H.); (Z.Z.); (Y.S.V.); (F.H.M.); (R.N.K.)
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Tiraboschi C, Parenti F, Sangalli F, Resovi A, Belotti D, Lanzarone E. Automatic Segmentation of Metastatic Livers by Means of U-Net-Based Procedures. Cancers (Basel) 2024; 16:4159. [PMID: 39766059 PMCID: PMC11674041 DOI: 10.3390/cancers16244159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2024] [Revised: 11/26/2024] [Accepted: 12/08/2024] [Indexed: 01/11/2025] Open
Abstract
Background: The liver is one of the most common sites for the spread of pancreatic ductal adenocarcinoma (PDAC) cells, with metastases present in about 80% of patients. Clinical and preclinical studies of PDAC require quantification of the liver's metastatic burden from several acquired images, which can benefit from automatic image segmentation tools. Methods: We developed three neural networks based on U-net architecture to automatically segment the healthy liver area (HL), the metastatic liver area (MLA), and liver metastases (LM) in micro-CT images of a mouse model of PDAC with liver metastasis. Three alternative U-nets were trained for each structure to be segmented following appropriate image preprocessing and the one with the highest performance was then chosen and applied for each case. Results: Good performance was achieved, with accuracy of 92.6%, 88.6%, and 91.5%, specificity of 95.5%, 93.8%, and 99.9%, Dice of 71.6%, 74.4%, and 29.9%, and negative predicted value (NPV) of 97.9%, 91.5%, and 91.5% on the pilot validation set for the chosen HL, MLA, and LM networks, respectively. Conclusions: The networks provided good performance and advantages in terms of saving time and ensuring reproducibility.
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Affiliation(s)
- Camilla Tiraboschi
- Department of Management, Information and Production Engineering, University of Bergamo, 24044 Dalmine, BG, Italy
| | - Federica Parenti
- Department of Management, Information and Production Engineering, University of Bergamo, 24044 Dalmine, BG, Italy
| | - Fabio Sangalli
- Department of Biomedical Engineering, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, 24126 Bergamo, BG, Italy
| | - Andrea Resovi
- Department of Oncology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, 24126 Bergamo, BG, Italy
| | - Dorina Belotti
- Department of Oncology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, 24126 Bergamo, BG, Italy
| | - Ettore Lanzarone
- Department of Management, Information and Production Engineering, University of Bergamo, 24044 Dalmine, BG, Italy
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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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Knox L. Pancreatic Cancer: The Advanced Practitioner's Role in Early Diagnosis and Management. J Adv Pract Oncol 2024; 15:444-450. [PMID: 39830222 PMCID: PMC11741095 DOI: 10.6004/jadpro.2024.15.7.3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2025] Open
Abstract
Pancreatic cancer is one of the most fatal cancers in the United States. Currently, it is the third leading cause of cancer-related deaths, and it is estimated that by 2030, it will be the second leading cause of cancer-related deaths behind lung cancer. It has poor overall survival rates, even with aggressive treatment. Quality of life is low in this patient population, due to poor prognosis at diagnosis and complex symptomatology. The purpose of this article is to explore the role of the advanced practitioner in the diagnosis, treatment, and symptom management of pancreatic cancer.
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Affiliation(s)
- Lindsay Knox
- From Carson Newman University and Tennessee Cancer Specialists, Powell, Tennessee
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10
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Akmeşe ÖF. Data privacy-aware machine learning approach in pancreatic cancer diagnosis. BMC Med Inform Decis Mak 2024; 24:248. [PMID: 39237927 PMCID: PMC11375871 DOI: 10.1186/s12911-024-02657-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2024] [Accepted: 08/29/2024] [Indexed: 09/07/2024] Open
Abstract
PROBLEM Pancreatic ductal adenocarcinoma (PDAC) is considered a highly lethal cancer due to its advanced stage diagnosis. The five-year survival rate after diagnosis is less than 10%. However, if diagnosed early, the five-year survival rate can reach up to 70%. Early diagnosis of PDAC can aid treatment and improve survival rates by taking necessary precautions. The challenge is to develop a reliable, data privacy-aware machine learning approach that can accurately diagnose pancreatic cancer with biomarkers. AIM The study aims to diagnose a patient's pancreatic cancer while ensuring the confidentiality of patient records. In addition, the study aims to guide researchers and clinicians in developing innovative methods for diagnosing pancreatic cancer. METHODS Machine learning, a branch of artificial intelligence, can identify patterns by analyzing large datasets. The study pre-processed a dataset containing urine biomarkers with operations such as filling in missing values, cleaning outliers, and feature selection. The data was encrypted using the Fernet encryption algorithm to ensure confidentiality. Ten separate machine learning models were applied to predict individuals with PDAC. Performance metrics such as F1 score, recall, precision, and accuracy were used in the modeling process. RESULTS Among the 590 clinical records analyzed, 199 (33.7%) belonged to patients with pancreatic cancer, 208 (35.3%) to patients with non-cancerous pancreatic disorders (such as benign hepatobiliary disease), and 183 (31%) to healthy individuals. The LGBM algorithm showed the highest efficiency by achieving an accuracy of 98.8%. The accuracy of the other algorithms ranged from 98 to 86%. In order to understand which features are more critical and which data the model is based on, the analysis found that the features "plasma_CA19_9", REG1A, TFF1, and LYVE1 have high importance levels. The LIME analysis also analyzed which features of the model are important in the decision-making process. CONCLUSIONS This research outlines a data privacy-aware machine learning tool for predicting PDAC. The results show that a promising approach can be presented for clinical application. Future research should expand the dataset and focus on validation by applying it to various populations.
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Affiliation(s)
- Ömer Faruk Akmeşe
- Department of Computer Engineering, Hitit University Çorum, Çorum, 19030, Türkiye.
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Zhang G, Gao Q, Zhan Q, Wang L, Song B, Chen Y, Bian Y, Ma C, Lu J, Shao C. Label-free differentiation of pancreatic pathologies from normal pancreas utilizing end-to-end three-dimensional multimodal networks on CT. Clin Radiol 2024; 79:e1159-e1166. [PMID: 38969545 DOI: 10.1016/j.crad.2024.06.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 05/10/2024] [Accepted: 06/05/2024] [Indexed: 07/07/2024]
Abstract
AIMS To investigate the utilization of an end-to-end multimodal convolutional model in the rapid and accurate diagnosis of pancreatic diseases using abdominal CT images. MATERIALS AND METHODS In this study, a novel lightweight label-free end-to-end multimodal network (eeMulNet) model was proposed for the rapid and precise diagnosis of abnormal pancreas. The eeMulNet consists of two steps: pancreatic region localization and multimodal CT diagnosis integrating textual and image data. A research dataset comprising 715 CT scans with various types of pancreas diseases and 228 CT scans from a control group was collected. The training set and independent test set for the multimodal classification network were randomly divided in an 8:2 ratio (755 for training and 188 for testing). RESULTS The eeMulNet model demonstrated outstanding performance on an independent test set of 188 CT scans (Normal: 45, Abnormal: 143), with an area under the curve (AUC) of 1.0, accuracy of 100%, and sensitivity of 100%. The average testing duration per patient was 41.04 seconds, while the classification network took only 0.04 seconds. CONCLUSIONS The proposed eeMulNet model offers a promising approach for the diagnosis of pancreatic diseases. It can support the identification of suspicious cases during daily radiology work and enhance the accuracy of pancreatic disease diagnosis. The codes and models of eeMulNet are publicly available at Rudeguy1/eeMulNet (github.com).
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Affiliation(s)
- G Zhang
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China; Department of Radiology, Changhai Hospital of Shanghai, Naval Medical University, Shanghai 200433, China.
| | - Q Gao
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China; Department of Radiology, Changhai Hospital of Shanghai, Naval Medical University, Shanghai 200433, China.
| | - Q Zhan
- Department of Radiology, Changhai Hospital of Shanghai, Naval Medical University, Shanghai 200433, China.
| | - L Wang
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
| | - B Song
- Department of Pancreatic Surgery, Changhai Hospital of Shanghai, Naval Medical University, Shanghai 200433, China.
| | - Y Chen
- College of Electronic and Information Engineering, Tongji University, Shanghai 201804, China.
| | - Y Bian
- Department of Radiology, Changhai Hospital of Shanghai, Naval Medical University, Shanghai 200433, China.
| | - C Ma
- Department of Radiology, Changhai Hospital of Shanghai, Naval Medical University, Shanghai 200433, China; College of Electronic and Information Engineering, Tongji University, Shanghai 201804, China.
| | - J Lu
- Department of Radiology, Changhai Hospital of Shanghai, Naval Medical University, Shanghai 200433, China.
| | - C Shao
- Department of Radiology, Changhai Hospital of Shanghai, Naval Medical University, Shanghai 200433, China.
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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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13
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Ramaekers M, Viviers CGA, Hellström TAE, Ewals LJS, Tasios N, Jacobs I, Nederend J, Sommen FVD, Luyer MDP. Improved Pancreatic Cancer Detection and Localization on CT Scans: A Computer-Aided Detection Model Utilizing Secondary Features. Cancers (Basel) 2024; 16:2403. [PMID: 39001465 PMCID: PMC11240790 DOI: 10.3390/cancers16132403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2024] [Revised: 06/27/2024] [Accepted: 06/28/2024] [Indexed: 07/16/2024] Open
Abstract
The early detection of pancreatic ductal adenocarcinoma (PDAC) is essential for optimal treatment of pancreatic cancer patients. We propose a tumor detection framework to improve the detection of pancreatic head tumors on CT scans. In this retrospective research study, CT images of 99 patients with pancreatic head cancer and 98 control cases from the Catharina Hospital Eindhoven were collected. A multi-stage 3D U-Net-based approach was used for PDAC detection including clinically significant secondary features such as pancreatic duct and common bile duct dilation. The developed algorithm was evaluated using a local test set comprising 59 CT scans. The model was externally validated in 28 pancreatic cancer cases of a publicly available medical decathlon dataset. The tumor detection framework achieved a sensitivity of 0.97 and a specificity of 1.00, with an area under the receiver operating curve (AUROC) of 0.99, in detecting pancreatic head cancer in the local test set. In the external test set, we obtained similar results, with a sensitivity of 1.00. The model provided the tumor location with acceptable accuracy obtaining a DICE Similarity Coefficient (DSC) of 0.37. This study shows that a tumor detection framework utilizing CT scans and secondary signs of pancreatic cancer can detect pancreatic tumors with high accuracy.
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Affiliation(s)
- Mark Ramaekers
- Department of Surgery, Catharina Cancer Institute, Catharina Hospital Eindhoven, EJ 5623 Eindhoven, The Netherlands
| | - Christiaan G A Viviers
- Department of Electrical Engineering, Eindhoven University of Technology, AZ 5612 Eindhoven, The Netherlands
| | - Terese A E Hellström
- Department of Electrical Engineering, Eindhoven University of Technology, AZ 5612 Eindhoven, The Netherlands
| | - Lotte J S Ewals
- Department of Radiology, Catharina Cancer Institute, Catharina Hospital Eindhoven, EJ 5623 Eindhoven, The Netherlands
| | - Nick Tasios
- Department of Hospital Services and Informatics, Philips Research, AE 5656 Eindhoven, The Netherlands
| | - Igor Jacobs
- Department of Hospital Services and Informatics, Philips Research, AE 5656 Eindhoven, The Netherlands
| | - Joost Nederend
- Department of Radiology, Catharina Cancer Institute, Catharina Hospital Eindhoven, EJ 5623 Eindhoven, The Netherlands
| | - Fons van der Sommen
- Department of Electrical Engineering, Eindhoven University of Technology, AZ 5612 Eindhoven, The Netherlands
| | - Misha D P Luyer
- Department of Surgery, Catharina Cancer Institute, Catharina Hospital Eindhoven, EJ 5623 Eindhoven, The Netherlands
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14
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Pan X, Jiao K, Li X, Feng L, Tian Y, Wu L, Zhang P, Wang K, Chen S, Yang B, Chen W. Artificial intelligence-based tools with automated segmentation and measurement on CT images to assist accurate and fast diagnosis in acute pancreatitis. Br J Radiol 2024; 97:1268-1277. [PMID: 38730541 PMCID: PMC11186564 DOI: 10.1093/bjr/tqae091] [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: 08/04/2023] [Revised: 03/22/2024] [Accepted: 05/04/2024] [Indexed: 05/13/2024] Open
Abstract
OBJECTIVES To develop an artificial intelligence (AI) tool with automated pancreas segmentation and measurement of pancreatic morphological information on CT images to assist improved and faster diagnosis in acute pancreatitis. METHODS This study retrospectively contained 1124 patients suspected for AP and received non-contrast and enhanced abdominal CT examination between September 2013 and September 2022. Patients were divided into training (N = 688), validation (N = 145), testing dataset [N = 291; N = 104 for normal pancreas, N = 98 for AP, N = 89 for AP complicated with PDAC (AP&PDAC)]. A model based on convolutional neural network (MSAnet) was developed. The pancreas segmentation and measurement were performed via eight open-source models and MSAnet based tools, and the efficacy was evaluated using dice similarity coefficient (DSC) and intersection over union (IoU). The DSC and IoU for patients with different ages were also compared. The outline of tumour and oedema in the AP and were segmented by clustering. The diagnostic efficacy for radiologists with or without the assistance of MSAnet tool in AP and AP&PDAC was evaluated using receiver operation curve and confusion matrix. RESULTS Among all models, MSAnet based tool showed best performance on the training and validation dataset, and had high efficacy on testing dataset. The performance was age-affected. With assistance of the AI tool, the diagnosis time was significantly shortened by 26.8% and 32.7% for junior and senior radiologists, respectively. The area under curve (AUC) in diagnosis of AP was improved from 0.91 to 0.96 for junior radiologist and 0.98 to 0.99 for senior radiologist. In AP&PDAC diagnosis, AUC was increased from 0.85 to 0.92 for junior and 0.97 to 0.99 for senior. CONCLUSION MSAnet based tools showed good pancreas segmentation and measurement performance, which help radiologists improve diagnosis efficacy and workflow in both AP and AP with PDAC conditions. ADVANCES IN KNOWLEDGE This study developed an AI tool with automated pancreas segmentation and measurement and provided evidence for AI tool assistance in improving the workflow and accuracy of AP diagnosis.
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Affiliation(s)
- Xuhang Pan
- Institute of Medical Imaging, Department of Radiology, Taihe Hospital, Hubei University of Medicine, Shiyan 442000, China
- School of Biomedical Engineering, Hubei University of Medicine, Shiyan 442000, China
| | - Kaijian Jiao
- Institute of Medical Imaging, Department of Radiology, Taihe Hospital, Hubei University of Medicine, Shiyan 442000, China
- School of Biomedical Engineering, Hubei University of Medicine, Shiyan 442000, China
| | - Xinyu Li
- Institute of Medical Imaging, Department of Radiology, Taihe Hospital, Hubei University of Medicine, Shiyan 442000, China
- School of Biomedical Engineering, Hubei University of Medicine, Shiyan 442000, China
| | - Linshuang Feng
- School of Biomedical Engineering, Hubei University of Medicine, Shiyan 442000, China
| | - Yige Tian
- School of Biomedical Engineering, Hubei University of Medicine, Shiyan 442000, China
| | - Lei Wu
- Institute of Medical Imaging, Department of Radiology, Taihe Hospital, Hubei University of Medicine, Shiyan 442000, China
- School of Biomedical Engineering, Hubei University of Medicine, Shiyan 442000, China
| | - Peng Zhang
- Institute of Medical Imaging, Department of Radiology, Taihe Hospital, Hubei University of Medicine, Shiyan 442000, China
- School of Biomedical Engineering, Hubei University of Medicine, Shiyan 442000, China
| | - Kejun Wang
- Institute of Medical Imaging, Department of Radiology, Taihe Hospital, Hubei University of Medicine, Shiyan 442000, China
| | - Suping Chen
- Advanced Application Team, GE Healthcare, Shanghai 200135, China
| | - Bo Yang
- Institute of Medical Imaging, Department of Radiology, Taihe Hospital, Hubei University of Medicine, Shiyan 442000, China
- School of Biomedical Engineering, Hubei University of Medicine, Shiyan 442000, China
| | - Wen Chen
- Institute of Medical Imaging, Department of Radiology, Taihe Hospital, Hubei University of Medicine, Shiyan 442000, China
- School of Biomedical Engineering, Hubei University of Medicine, Shiyan 442000, China
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15
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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: 0] [Impact Index Per Article: 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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Yuan N, Zhang Y, Lv K, Liu Y, Yang A, Hu P, Yu H, Han X, Guo X, Li J, Wang T, Lei B, Ma G. HCA-DAN: hierarchical class-aware domain adaptive network for gastric tumor segmentation in 3D CT images. Cancer Imaging 2024; 24:63. [PMID: 38773670 PMCID: PMC11107051 DOI: 10.1186/s40644-024-00711-w] [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: 03/08/2023] [Accepted: 05/11/2024] [Indexed: 05/24/2024] Open
Abstract
BACKGROUND Accurate segmentation of gastric tumors from CT scans provides useful image information for guiding the diagnosis and treatment of gastric cancer. However, automated gastric tumor segmentation from 3D CT images faces several challenges. The large variation of anisotropic spatial resolution limits the ability of 3D convolutional neural networks (CNNs) to learn features from different views. The background texture of gastric tumor is complex, and its size, shape and intensity distribution are highly variable, which makes it more difficult for deep learning methods to capture the boundary. In particular, while multi-center datasets increase sample size and representation ability, they suffer from inter-center heterogeneity. METHODS In this study, we propose a new cross-center 3D tumor segmentation method named Hierarchical Class-Aware Domain Adaptive Network (HCA-DAN), which includes a new 3D neural network that efficiently bridges an Anisotropic neural network and a Transformer (AsTr) for extracting multi-scale context features from the CT images with anisotropic resolution, and a hierarchical class-aware domain alignment (HCADA) module for adaptively aligning multi-scale context features across two domains by integrating a class attention map with class-specific information. We evaluate the proposed method on an in-house CT image dataset collected from four medical centers and validate its segmentation performance in both in-center and cross-center test scenarios. RESULTS Our baseline segmentation network (i.e., AsTr) achieves best results compared to other 3D segmentation models, with a mean dice similarity coefficient (DSC) of 59.26%, 55.97%, 48.83% and 67.28% in four in-center test tasks, and with a DSC of 56.42%, 55.94%, 46.54% and 60.62% in four cross-center test tasks. In addition, the proposed cross-center segmentation network (i.e., HCA-DAN) obtains excellent results compared to other unsupervised domain adaptation methods, with a DSC of 58.36%, 56.72%, 49.25%, and 62.20% in four cross-center test tasks. CONCLUSIONS Comprehensive experimental results demonstrate that the proposed method outperforms compared methods on this multi-center database and is promising for routine clinical workflows.
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Affiliation(s)
- Ning Yuan
- Department of Medical Imaging, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, China
| | - Yongtao Zhang
- School of Biomedical Engineering, Health Science Centers, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, China
| | - Kuan Lv
- Peking University China-Japan Friendship School of Clinical Medicine, Beijing, China
| | - Yiyao Liu
- School of Biomedical Engineering, Health Science Centers, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, China
| | - Aocai Yang
- Department of Radiology, China-Japan Friendship Hospital, No. 2 East Yinghua Road, Chaoyang District, Beijing, 100029, China
| | - Pianpian Hu
- Department of Radiology, China-Japan Friendship Hospital, No. 2 East Yinghua Road, Chaoyang District, Beijing, 100029, China
| | - Hongwei Yu
- Department of Radiology, China-Japan Friendship Hospital, No. 2 East Yinghua Road, Chaoyang District, Beijing, 100029, China
| | - Xiaowei Han
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Xing Guo
- Department of Medical Imaging, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, China
| | - Junfeng Li
- Department of Medical Imaging, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, China
| | - Tianfu Wang
- School of Biomedical Engineering, Health Science Centers, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, China
| | - Baiying Lei
- School of Biomedical Engineering, Health Science Centers, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, China
- AI Research Center for Medical Image Analysis and Diagnosis, Shenzhen University, Guangdong, China
| | - Guolin Ma
- Department of Radiology, China-Japan Friendship Hospital, No. 2 East Yinghua Road, Chaoyang District, Beijing, 100029, China.
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17
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Miao Q, Wang X, Cui J, Zheng H, Xie Y, Zhu K, Chai R, Jiang Y, Feng D, Zhang X, Shi F, Tan X, Fan G, Liang K. Artificial intelligence to predict T4 stage of pancreatic ductal adenocarcinoma using CT imaging. Comput Biol Med 2024; 171:108125. [PMID: 38340439 DOI: 10.1016/j.compbiomed.2024.108125] [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: 10/30/2023] [Revised: 02/02/2024] [Accepted: 02/05/2024] [Indexed: 02/12/2024]
Abstract
BACKGROUND The accurate assessment of T4 stage of pancreatic ductal adenocarcinoma (PDAC) has consistently presented a considerable difficulty for radiologists. This study aimed to develop and validate an automated artificial intelligence (AI) pipeline for the prediction of T4 stage of PDAC using contrast-enhanced CT imaging. METHODS The data were obtained retrospectively from consecutive patients with surgically resected and pathologically proved PDAC at two institutions between July 2017 and June 2022. Initially, a deep learning (DL) model was developed to segment PDAC. Subsequently, radiomics features were extracted from the automatically segmented region of interest (ROI), which encompassed both the tumor region and a 3 mm surrounding area, to construct a predictive model for determining T4 stage of PDAC. The assessment of the models' performance involved the calculation of the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. RESULTS The study encompassed a cohort of 509 PDAC patients, with a median age of 62 years (interquartile range: 55-67). The proportion of patients in T4 stage within the model was 16.9%. The model achieved an AUC of 0.849 (95% CI: 0.753-0.940), a sensitivity of 0.875, and a specificity of 0.728 in predicting T4 stage of PDAC. The performance of the model was determined to be comparable to that of two experienced abdominal radiologists (AUCs: 0.849 vs. 0.834 and 0.857). CONCLUSION The automated AI pipeline utilizing tumor and peritumor-related radiomics features demonstrated comparable performance to that of senior abdominal radiologists in predicting T4 stage of PDAC.
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Affiliation(s)
- Qi Miao
- Department of Radiology, The First Hospital of China Medical University, Shenyang, China
| | - Xuechun Wang
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Jingjing Cui
- Department of Research and Development, United Imaging Intelligence (Beijing) Co., Ltd., Bejing, China
| | - Haoxin Zheng
- Department of Computer Science, University of California, Los Angeles, USA
| | - Yan Xie
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Kexin Zhu
- Department of Radiology, The First Hospital of China Medical University, Shenyang, China
| | - Ruimei Chai
- Department of Radiology, The First Hospital of China Medical University, Shenyang, China
| | - Yuanxi Jiang
- Department of Radiology, The First Hospital of China Medical University, Shenyang, China
| | - Dongli Feng
- Department of Radiology, The First Hospital of China Medical University, Shenyang, China
| | - Xin Zhang
- Department of Radiology, The First Hospital of China Medical University, Shenyang, China
| | - Feng Shi
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Xiaodong Tan
- Department of General Surgery/Pancreatic and Thyroid Surgery, Shengjing Hospital of China Medical University, Shenyang, China
| | - Guoguang Fan
- Department of Radiology, The First Hospital of China Medical University, Shenyang, China.
| | - Keke Liang
- Department of General Surgery/Pancreatic and Thyroid Surgery, Shengjing Hospital of China Medical University, Shenyang, China.
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Zhao G, Chen X, Zhu M, Liu Y, Wang Y. Exploring the application and future outlook of Artificial intelligence in pancreatic cancer. Front Oncol 2024; 14:1345810. [PMID: 38450187 PMCID: PMC10915754 DOI: 10.3389/fonc.2024.1345810] [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: 11/28/2023] [Accepted: 01/29/2024] [Indexed: 03/08/2024] Open
Abstract
Pancreatic cancer, an exceptionally malignant tumor of the digestive system, presents a challenge due to its lack of typical early symptoms and highly invasive nature. The majority of pancreatic cancer patients are diagnosed when curative surgical resection is no longer possible, resulting in a poor overall prognosis. In recent years, the rapid progress of Artificial intelligence (AI) in the medical field has led to the extensive utilization of machine learning and deep learning as the prevailing approaches. Various models based on AI technology have been employed in the early screening, diagnosis, treatment, and prognostic prediction of pancreatic cancer patients. Furthermore, the development and application of three-dimensional visualization and augmented reality navigation techniques have also found their way into pancreatic cancer surgery. This article provides a concise summary of the current state of AI technology in pancreatic cancer and offers a promising outlook for its future applications.
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Affiliation(s)
- Guohua Zhao
- Department of General Surgery, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Liaoning, China
| | - Xi Chen
- Department of General Surgery, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Liaoning, China
- Department of Clinical integration of traditional Chinese and Western medicine, Liaoning University of Traditional Chinese Medicine, Liaoning, China
| | - Mengying Zhu
- Department of General Surgery, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Liaoning, China
- Department of Clinical integration of traditional Chinese and Western medicine, Liaoning University of Traditional Chinese Medicine, Liaoning, China
| | - Yang Liu
- Department of Ophthalmology, First Hospital of China Medical University, Liaoning, China
| | - Yue Wang
- Department of General Surgery, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Liaoning, China
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Shi Y, Tang H, Baine MJ, Hollingsworth MA, Du H, Zheng D, Zhang C, Yu H. 3DGAUnet: 3D Generative Adversarial Networks with a 3D U-Net Based Generator to Achieve the Accurate and Effective Synthesis of Clinical Tumor Image Data for Pancreatic Cancer. Cancers (Basel) 2023; 15:5496. [PMID: 38067200 PMCID: PMC10705188 DOI: 10.3390/cancers15235496] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 11/07/2023] [Accepted: 11/14/2023] [Indexed: 02/12/2024] Open
Abstract
Pancreatic ductal adenocarcinoma (PDAC) presents a critical global health challenge, and early detection is crucial for improving the 5-year survival rate. Recent medical imaging and computational algorithm advances offer potential solutions for early diagnosis. Deep learning, particularly in the form of convolutional neural networks (CNNs), has demonstrated success in medical image analysis tasks, including classification and segmentation. However, the limited availability of clinical data for training purposes continues to represent a significant obstacle. Data augmentation, generative adversarial networks (GANs), and cross-validation are potential techniques to address this limitation and improve model performance, but effective solutions are still rare for 3D PDAC, where the contrast is especially poor, owing to the high heterogeneity in both tumor and background tissues. In this study, we developed a new GAN-based model, named 3DGAUnet, for generating realistic 3D CT images of PDAC tumors and pancreatic tissue, which can generate the inter-slice connection data that the existing 2D CT image synthesis models lack. The transition to 3D models allowed the preservation of contextual information from adjacent slices, improving efficiency and accuracy, especially for the poor-contrast challenging case of PDAC. PDAC's challenging characteristics, such as an iso-attenuating or hypodense appearance and lack of well-defined margins, make tumor shape and texture learning challenging. To overcome these challenges and improve the performance of 3D GAN models, our innovation was to develop a 3D U-Net architecture for the generator, to improve shape and texture learning for PDAC tumors and pancreatic tissue. Thorough examination and validation across many datasets were conducted on the developed 3D GAN model, to ascertain the efficacy and applicability of the model in clinical contexts. Our approach offers a promising path for tackling the urgent requirement for creative and synergistic methods to combat PDAC. The development of this GAN-based model has the potential to alleviate data scarcity issues, elevate the quality of synthesized data, and thereby facilitate the progression of deep learning models, to enhance the accuracy and early detection of PDAC tumors, which could profoundly impact patient outcomes. Furthermore, the model has the potential to be adapted to other types of solid tumors, hence making significant contributions to the field of medical imaging in terms of image processing models.
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Affiliation(s)
- Yu Shi
- School of Computing, University of Nebraska-Lincoln, Lincoln, NE 68588, USA; (Y.S.); (H.T.)
- School of Biological Sciences, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
- Complex Biosystems Program, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
| | - Hannah Tang
- School of Computing, University of Nebraska-Lincoln, Lincoln, NE 68588, USA; (Y.S.); (H.T.)
| | - Michael J. Baine
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USA;
| | - Michael A. Hollingsworth
- Eppley Institute for Research in Cancer and Allied Diseases, University of Nebraska Medical Center, Omaha, NE 68198, USA;
| | - Huijing Du
- Department of Mathematics, University of Nebraska-Lincoln, Lincoln, NE 68588, USA;
| | - Dandan Zheng
- Department of Radiation Oncology, University of Rochester Medical Center, Rochester, NY 14626, USA;
| | - Chi Zhang
- School of Biological Sciences, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
| | - Hongfeng Yu
- School of Computing, University of Nebraska-Lincoln, Lincoln, NE 68588, USA; (Y.S.); (H.T.)
- Holland Computing Center, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
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Jiang J, Chao WL, Cao T, Culp S, Napoléon B, El-Dika S, Machicado JD, Pannala R, Mok S, Luthra AK, Akshintala VS, Muniraj T, Krishna SG. Improving Pancreatic Cyst Management: Artificial Intelligence-Powered Prediction of Advanced Neoplasms through Endoscopic Ultrasound-Guided Confocal Endomicroscopy. Biomimetics (Basel) 2023; 8:496. [PMID: 37887627 PMCID: PMC10604893 DOI: 10.3390/biomimetics8060496] [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: 08/02/2023] [Revised: 10/03/2023] [Accepted: 10/17/2023] [Indexed: 10/28/2023] Open
Abstract
Despite the increasing rate of detection of incidental pancreatic cystic lesions (PCLs), current standard-of-care methods for their diagnosis and risk stratification remain inadequate. Intraductal papillary mucinous neoplasms (IPMNs) are the most prevalent PCLs. The existing modalities, including endoscopic ultrasound and cyst fluid analysis, only achieve accuracy rates of 65-75% in identifying carcinoma or high-grade dysplasia in IPMNs. Furthermore, surgical resection of PCLs reveals that up to half exhibit only low-grade dysplastic changes or benign neoplasms. To reduce unnecessary and high-risk pancreatic surgeries, more precise diagnostic techniques are necessary. A promising approach involves integrating existing data, such as clinical features, cyst morphology, and data from cyst fluid analysis, with confocal endomicroscopy and radiomics to enhance the prediction of advanced neoplasms in PCLs. Artificial intelligence and machine learning modalities can play a crucial role in achieving this goal. In this review, we explore current and future techniques to leverage these advanced technologies to improve diagnostic accuracy in the context of PCLs.
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Affiliation(s)
- Joanna Jiang
- Division of Gastroenterology, Hepatology, and Nutrition, Department of Internal Medicine, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA
| | - Wei-Lun Chao
- Department of Computer Science and Engineering, College of Engineering, The Ohio State University, Columbus, OH 43210, USA
| | - Troy Cao
- College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Stacey Culp
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Bertrand Napoléon
- Department of Gastroenterology, Jean Mermoz Private Hospital, 69008 Lyon, France
| | - Samer El-Dika
- Division of Gastroenterology and Hepatology, Stanford University, Stanford, CA 94305, USA
| | - Jorge D. Machicado
- Division of Gastroenterology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Rahul Pannala
- Division of Gastroenterology and Hepatology, Mayo Clinic Arizona, Phoenix, AZ 85054, USA
| | - Shaffer Mok
- Division of Gastrointestinal Oncology, Moffitt Cancer Center, Tampa, FL 33612, USA
| | - Anjuli K. Luthra
- Division of Gastrointestinal Oncology, Moffitt Cancer Center, Tampa, FL 33612, USA
| | - Venkata S. Akshintala
- Division of Gastroenterology, Johns Hopkins Medical Institutions, Baltimore, MD 21287, USA
| | - Thiruvengadam Muniraj
- Department of Internal Medicine, Yale University School of Medicine, New Haven, CT 06510, USA
| | - Somashekar G. Krishna
- Division of Gastroenterology, Hepatology, and Nutrition, Department of Internal Medicine, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA
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21
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Liang H, Wang M, Wen Y, Du F, Jiang L, Geng X, Tang L, Yan H. Predicting acute pancreatitis severity with enhanced computed tomography scans using convolutional neural networks. Sci Rep 2023; 13:17514. [PMID: 37845380 PMCID: PMC10579320 DOI: 10.1038/s41598-023-44828-7] [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: 05/13/2023] [Accepted: 10/12/2023] [Indexed: 10/18/2023] Open
Abstract
This study aimed to evaluate acute pancreatitis (AP) severity using convolutional neural network (CNN) models with enhanced computed tomography (CT) scans. Three-dimensional DenseNet CNN models were developed and trained using the enhanced CT scans labeled with two severity assessment methods: the computed tomography severity index (CTSI) and Atlanta classification. Each labeling method was used independently for model training and validation. Model performance was evaluated using confusion matrices, areas under the receiver operating characteristic curve (AUC-ROC), accuracy, precision, recall, F1 score, and respective macro-average metrics. A total of 1,798 enhanced CT scans met the inclusion criteria were included in this study. The dataset was randomly divided into a training dataset (n = 1618) and a test dataset (n = 180) with a ratio of 9:1. The DenseNet model demonstrated promising predictions for both CTSI and Atlanta classification-labeled CT scans, with accuracy greater than 0.7 and AUC-ROC greater than 0.8. Specifically, when trained with CT scans labeled using CTSI, the DenseNet model achieved good performance, with a macro-average F1 score of 0.835 and a macro-average AUC-ROC of 0.980. The findings of this study affirm the feasibility of employing CNN models to predict the severity of AP using enhanced CT scans.
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Affiliation(s)
- Hongyin Liang
- Department of General Surgery, The General Hospital of Western Theater Command (Chengdu Military General Hospital), Chengdu, 610083, China
- Sichuan Provincial Key Laboratory of Pancreatic Injury and Repair, Chengdu, 610083, China
| | - Meng Wang
- Department of Traditional Chinese Medicine, The General Hospital of Western Theater Command (Chengdu Military General Hospital), Chengdu, 610083, China
| | - Yi Wen
- Department of General Surgery, The General Hospital of Western Theater Command (Chengdu Military General Hospital), Chengdu, 610083, China
- Sichuan Provincial Key Laboratory of Pancreatic Injury and Repair, Chengdu, 610083, China
| | - Feizhou Du
- Department of Radiology, The General Hospital of Western Theater Command (Chengdu Military General Hospital), Chengdu, 610083, China
| | - Li Jiang
- Department of Cardiac Surgery, The General Hospital of Western Theater Command (Chengdu Military General Hospital), Chengdu, 610083, China
| | - Xuelong Geng
- Department of Radiology, The General Hospital of Western Theater Command (Chengdu Military General Hospital), Chengdu, 610083, China
| | - Lijun Tang
- Department of General Surgery, The General Hospital of Western Theater Command (Chengdu Military General Hospital), Chengdu, 610083, China
- Sichuan Provincial Key Laboratory of Pancreatic Injury and Repair, Chengdu, 610083, China
| | - Hongtao Yan
- Department of Liver Transplantation and Hepato-biliary-pancreatic Surgery, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610016, China.
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22
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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: 2] [Impact Index Per Article: 1.0] [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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23
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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: 4] [Impact Index Per Article: 2.0] [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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24
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Viriyasaranon T, Chun JW, Koh YH, Cho JH, Jung MK, Kim SH, Kim HJ, Lee WJ, Choi JH, Woo SM. Annotation-Efficient Deep Learning Model for Pancreatic Cancer Diagnosis and Classification Using CT Images: A Retrospective Diagnostic Study. Cancers (Basel) 2023; 15:3392. [PMID: 37444502 DOI: 10.3390/cancers15133392] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 06/26/2023] [Accepted: 06/26/2023] [Indexed: 07/15/2023] Open
Abstract
The aim of this study was to develop a novel deep learning (DL) model without requiring large-annotated training datasets for detecting pancreatic cancer (PC) using computed tomography (CT) images. This retrospective diagnostic study was conducted using CT images collected from 2004 and 2019 from 4287 patients diagnosed with PC. We proposed a self-supervised learning algorithm (pseudo-lesion segmentation (PS)) for PC classification, which was trained with and without PS and validated on randomly divided training and validation sets. We further performed cross-racial external validation using open-access CT images from 361 patients. For internal validation, the accuracy and sensitivity for PC classification were 94.3% (92.8-95.4%) and 92.5% (90.0-94.4%), and 95.7% (94.5-96.7%) and 99.3 (98.4-99.7%) for the convolutional neural network (CNN) and transformer-based DL models (both with PS), respectively. Implementing PS on a small-sized training dataset (randomly sampled 10%) increased accuracy by 20.5% and sensitivity by 37.0%. For external validation, the accuracy and sensitivity were 82.5% (78.3-86.1%) and 81.7% (77.3-85.4%) and 87.8% (84.0-90.8%) and 86.5% (82.3-89.8%) for the CNN and transformer-based DL models (both with PS), respectively. PS self-supervised learning can increase DL-based PC classification performance, reliability, and robustness of the model for unseen, and even small, datasets. The proposed DL model is potentially useful for PC diagnosis.
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Affiliation(s)
- Thanaporn Viriyasaranon
- Graduate Program in System Health Science and Engineering, Division of Mechanical and Biomedical Engineering, Ewha Womans University, Seoul 03760, Republic of Korea
| | - Jung Won Chun
- Center for Liver and Pancreatobiliary Cancer, National Cancer Center, Goyang 10408, Republic of Korea
| | - Young Hwan Koh
- Center for Liver and Pancreatobiliary Cancer, National Cancer Center, Goyang 10408, Republic of Korea
| | - Jae Hee Cho
- Department of Internal Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - Min Kyu Jung
- Department of Internal Medicine, Kyungpook National University Hospital, Daegu 41944, Republic of Korea
| | - Seong-Hun Kim
- Department of Internal Medicine, Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju 54907, Republic of Korea
| | - Hyo Jung Kim
- Department of Gastroenterology, Korea University Guro Hospital, Seoul 10408, Republic of Korea
| | - Woo Jin Lee
- Center for Liver and Pancreatobiliary Cancer, National Cancer Center, Goyang 10408, Republic of Korea
| | - Jang-Hwan Choi
- Graduate Program in System Health Science and Engineering, Division of Mechanical and Biomedical Engineering, Ewha Womans University, Seoul 03760, Republic of Korea
| | - Sang Myung Woo
- Center for Liver and Pancreatobiliary Cancer, National Cancer Center, Goyang 10408, Republic of Korea
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25
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Ramaekers M, Viviers CGA, Janssen BV, Hellström TAE, Ewals L, van der Wulp K, Nederend J, Jacobs I, Pluyter JR, Mavroeidis D, van der Sommen F, Besselink MG, Luyer MDP. Computer-Aided Detection for Pancreatic Cancer Diagnosis: Radiological Challenges and Future Directions. J Clin Med 2023; 12:4209. [PMID: 37445243 DOI: 10.3390/jcm12134209] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 06/08/2023] [Accepted: 06/19/2023] [Indexed: 07/15/2023] Open
Abstract
Radiological imaging plays a crucial role in the detection and treatment of pancreatic ductal adenocarcinoma (PDAC). However, there are several challenges associated with the use of these techniques in daily clinical practice. Determination of the presence or absence of cancer using radiological imaging is difficult and requires specific expertise, especially after neoadjuvant therapy. Early detection and characterization of tumors would potentially increase the number of patients who are eligible for curative treatment. Over the last decades, artificial intelligence (AI)-based computer-aided detection (CAD) has rapidly evolved as a means for improving the radiological detection of cancer and the assessment of the extent of disease. Although the results of AI applications seem promising, widespread adoption in clinical practice has not taken place. This narrative review provides an overview of current radiological CAD systems in pancreatic cancer, highlights challenges that are pertinent to clinical practice, and discusses potential solutions for these challenges.
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Affiliation(s)
- Mark Ramaekers
- Department of Surgery, Catharina Cancer Institute, Catharina Hospital Eindhoven, 5623 EJ Eindhoven, The Netherlands
| | - Christiaan G A Viviers
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
| | - Boris V Janssen
- Department of Surgery, Amsterdam UMC, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands
- Cancer Center Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - Terese A E Hellström
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
| | - Lotte Ewals
- Department of Radiology, Catharina Cancer Institute, Catharina Hospital Eindhoven, 5623 EJ Eindhoven, The Netherlands
| | - Kasper van der Wulp
- Department of Radiology, Catharina Cancer Institute, Catharina Hospital Eindhoven, 5623 EJ Eindhoven, The Netherlands
| | - Joost Nederend
- Department of Radiology, Catharina Cancer Institute, Catharina Hospital Eindhoven, 5623 EJ Eindhoven, The Netherlands
| | - Igor Jacobs
- Department of Hospital Services and Informatics, Philips Research, 5656 AE Eindhoven, The Netherlands
| | - Jon R Pluyter
- Department of Experience Design, Philips Design, 5656 AE Eindhoven, The Netherlands
| | - Dimitrios Mavroeidis
- Department of Data Science, Philips Research, 5656 AE Eindhoven, The Netherlands
| | - Fons van der Sommen
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
| | - Marc G Besselink
- Department of Surgery, Amsterdam UMC, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands
- Cancer Center Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - Misha D P Luyer
- Department of Surgery, Catharina Cancer Institute, Catharina Hospital Eindhoven, 5623 EJ Eindhoven, The Netherlands
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26
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Liang X, Dai J, Zhou X, Liu L, Zhang C, Jiang Y, Li N, Niu T, Xie Y, Dai Z, Wang X. An Unsupervised Learning-Based Regional Deformable Model for Automated Multi-Organ Contour Propagation. J Digit Imaging 2023; 36:923-931. [PMID: 36717520 PMCID: PMC10287868 DOI: 10.1007/s10278-023-00779-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 01/11/2023] [Accepted: 01/13/2023] [Indexed: 02/01/2023] Open
Abstract
The aim of this study is to evaluate a regional deformable model based on a deep unsupervised learning model for automatic contour propagation in breast cone-beam computed tomography-guided adaptive radiation therapy. A deep unsupervised learning model was introduced to map breast's tumor bed, clinical target volume, heart, left lung, right lung, and spinal cord from planning computed tomography to cone-beam CT. To improve the traditional image registration method's performance, we used a regional deformable framework based on the narrow-band mapping, which can mitigate the effect of the image artifacts on the cone-beam CT. We retrospectively selected 373 anonymized cone-beam CT volumes from 111 patients with breast cancer. The cone-beam CTs are divided into three sets. 311 / 20 / 42 cone-beam CT images were used for training, validating, and testing. The manual contour was used as reference for the testing set. We compared the results between the reference and the model prediction for evaluating the performance. The mean Dice between manual reference segmentations and the model predicted segmentations for breast tumor bed, clinical target volume, heart, left lung, right lung, and spinal cord were 0.78 ± 0.09, 0.90 ± 0.03, 0.88 ± 0.04, 0.94 ± 0.03, 0.95 ± 0.02, and 0.77 ± 0.07, respectively. The results demonstrated a good agreement between the reference and the proposed contours. The proposed deep learning-based regional deformable model technique can automatically propagate contours for breast cancer adaptive radiotherapy. Deep learning in contour propagation was promising, but further investigation was warranted.
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Affiliation(s)
- Xiaokun Liang
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055 China
| | - Jingjing Dai
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055 China
| | - Xuanru Zhou
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055 China
| | - Lin Liu
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055 China
| | - Chulong Zhang
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055 China
| | - Yuming Jiang
- Department of Radiation Oncology, Stanford University, Stanford, CA 94305 USA
| | - Na Li
- Department of Biomedical Engineering, Guangdong Medical University, Dongguan, 523808 China
| | - Tianye Niu
- Shenzhen Bay Laboratory, Shenzhen, Guangdong 518118 China
- Peking University Aerospace School of Clinical Medicine, Aerospace Center Hospital, Beijing, 100049 China
| | - Yaoqin Xie
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055 China
| | - Zhenhui Dai
- Department of Radiation Therapy, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510120 China
| | - Xuetao Wang
- Department of Radiation Therapy, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510120 China
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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] [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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28
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Deep learning-enabled fully automated pipeline system for segmentation and classification of single-mass breast lesions using contrast-enhanced mammography: a prospective, multicentre study. EClinicalMedicine 2023; 58:101913. [PMID: 36969336 PMCID: PMC10034267 DOI: 10.1016/j.eclinm.2023.101913] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 03/01/2023] [Accepted: 03/02/2023] [Indexed: 03/19/2023] Open
Abstract
Background Breast cancer is the leading cause of cancer-related deaths in women. However, accurate diagnosis of breast cancer using medical images heavily relies on the experience of radiologists. This study aimed to develop an artificial intelligence model that diagnosed single-mass breast lesions on contrast-enhanced mammography (CEM) for assisting the diagnostic workflow. Methods A total of 1912 women with single-mass breast lesions on CEM images before biopsy or surgery were included from June 2017 to October 2022 at three centres in China. Samples were divided into training and validation sets, internal testing set, pooled external testing set, and prospective testing set. A fully automated pipeline system (FAPS) using RefineNet and the Xception + Pyramid pooling module (PPM) was developed to perform the segmentation and classification of breast lesions. The performances of six radiologists and adjustments in Breast Imaging Reporting and Data System (BI-RADS) category 4 under the FAPS-assisted strategy were explored in pooled external and prospective testing sets. The segmentation performance was assessed using the Dice similarity coefficient (DSC), and the classification was assessed using heatmaps, area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. The radiologists' reading time was recorded for comparison with the FAPS. This trial is registered with China Clinical Trial Registration Centre (ChiCTR2200063444). Findings The FAPS-based segmentation task achieved DSCs of 0.888 ± 0.101, 0.820 ± 0.148 and 0.837 ± 0.132 in the internal, pooled external and prospective testing sets, respectively. For the classification task, the FAPS achieved AUCs of 0.947 (95% confidence interval [CI]: 0.916-0.978), 0.940 (95% [CI]: 0.894-0.987) and 0.891 (95% [CI]: 0.816-0.945). It outperformed radiologists in terms of classification efficiency based on single lesions (6 s vs 3 min). Moreover, the FAPS-assisted strategy improved the performance of radiologists. BI-RADS category 4 in 12.4% and 13.3% of patients was adjusted in two testing sets with the assistance of FAPS, which may play an important guiding role in the selection of clinical management strategies. Interpretation The FAPS based on CEM demonstrated the potential for the segmentation and classification of breast lesions, and had good generalisation ability and clinical applicability. Funding This study was supported by the Taishan Scholar Foundation of Shandong Province of China (tsqn202211378), National Natural Science Foundation of China (82001775), Natural Science Foundation of Shandong Province of China (ZR2021MH120), and Special Fund for Breast Disease Research of Shandong Medical Association (YXH2021ZX055).
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29
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Jan Z, El Assadi F, Abd-Alrazaq A, Jithesh PV. Artificial Intelligence for the Prediction and Early Diagnosis of Pancreatic Cancer: Scoping Review. J Med Internet Res 2023; 25:e44248. [PMID: 37000507 PMCID: PMC10131763 DOI: 10.2196/44248] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 02/21/2023] [Indexed: 04/01/2023] Open
Abstract
BACKGROUND Pancreatic cancer is the 12th most common cancer worldwide, with an overall survival rate of 4.9%. Early diagnosis of pancreatic cancer is essential for timely treatment and survival. Artificial intelligence (AI) provides advanced models and algorithms for better diagnosis of pancreatic cancer. OBJECTIVE This study aims to explore AI models used for the prediction and early diagnosis of pancreatic cancers as reported in the literature. METHODS A scoping review was conducted and reported in line with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. PubMed, Google Scholar, Science Direct, BioRXiv, and MedRxiv were explored to identify relevant articles. Study selection and data extraction were independently conducted by 2 reviewers. Data extracted from the included studies were synthesized narratively. RESULTS Of the 1185 publications, 30 studies were included in the scoping review. The included articles reported the use of AI for 6 different purposes. Of these included articles, AI techniques were mostly used for the diagnosis of pancreatic cancer (14/30, 47%). Radiological images (14/30, 47%) were the most frequently used data in the included articles. Most of the included articles used data sets with a size of <1000 samples (11/30, 37%). Deep learning models were the most prominent branch of AI used for pancreatic cancer diagnosis in the studies, and the convolutional neural network was the most used algorithm (18/30, 60%). Six validation approaches were used in the included studies, of which the most frequently used approaches were k-fold cross-validation (10/30, 33%) and external validation (10/30, 33%). A higher level of accuracy (99%) was found in studies that used support vector machine, decision trees, and k-means clustering algorithms. CONCLUSIONS This review presents an overview of studies based on AI models and algorithms used to predict and diagnose pancreatic cancer patients. AI is expected to play a vital role in advancing pancreatic cancer prediction and diagnosis. Further research is required to provide data that support clinical decisions in health care.
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Affiliation(s)
- Zainab Jan
- College of Health & Life Sciences, Hamad Bin Khalifa University, Doha, Qatar
| | - Farah El Assadi
- College of Health & Life Sciences, Hamad Bin Khalifa University, Doha, Qatar
| | - Alaa Abd-Alrazaq
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
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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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Park HJ, Shin K, You MW, Kyung SG, Kim SY, Park SH, Byun JH, Kim N, Kim HJ. Deep Learning-based Detection of Solid and Cystic Pancreatic Neoplasms at Contrast-enhanced CT. Radiology 2023; 306:140-149. [PMID: 35997607 DOI: 10.1148/radiol.220171] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Background Deep learning (DL) may facilitate the diagnosis of various pancreatic lesions at imaging. Purpose To develop and validate a DL-based approach for automatic identification of patients with various solid and cystic pancreatic neoplasms at abdominal CT and compare its diagnostic performance with that of radiologists. Materials and Methods In this retrospective study, a three-dimensional nnU-Net-based DL model was trained using the CT data of patients who underwent resection for pancreatic lesions between January 2014 and March 2015 and a subset of patients without pancreatic abnormality who underwent CT in 2014. Performance of the DL-based approach to identify patients with pancreatic lesions was evaluated in a temporally independent cohort (test set 1) and a temporally and spatially independent cohort (test set 2) and was compared with that of two board-certified radiologists. Performance was assessed using receiver operating characteristic analysis. Results The study included 852 patients in the training set (median age, 60 years [range, 19-85 years]; 462 men), 603 patients in test set 1 (median age, 58 years [range, 18-82 years]; 376 men), and 589 patients in test set 2 (median age, 63 years [range, 18-99 years]; 343 men). In test set 1, the DL-based approach had an area under the receiver operating characteristic curve (AUC) of 0.91 (95% CI: 0.89, 0.94) and showed slightly worse performance in test set 2 (AUC, 0.87 [95% CI: 0.84, 0.89]). The DL-based approach showed high sensitivity in identifying patients with solid lesions of any size (98%-100%) or cystic lesions measuring 1.0 cm or larger (92%-93%), which was comparable with the radiologists (95%-100% for solid lesions [P = .51 to P > .99]; 93%-98% for cystic lesions ≥1.0 cm [P = .38 to P > .99]). Conclusion The deep learning-based approach demonstrated high performance in identifying patients with various solid and cystic pancreatic lesions at CT. © RSNA, 2022 Online supplemental material is available for this article.
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Affiliation(s)
- Hyo Jung Park
- From the Department of Radiology and Research Institute of Radiology (H.J.P., S.Y.K., S.H.P., J.H.B., H.J.K.) and Department of Bioengineering, Asan Medical Institute of Convergence Science and Technology (K.S., S.G.K., N.K.), Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea; and Department of Radiology, Kyung Hee University Hospital, Seoul, Republic of Korea (M.W.Y.)
| | - Keewon Shin
- From the Department of Radiology and Research Institute of Radiology (H.J.P., S.Y.K., S.H.P., J.H.B., H.J.K.) and Department of Bioengineering, Asan Medical Institute of Convergence Science and Technology (K.S., S.G.K., N.K.), Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea; and Department of Radiology, Kyung Hee University Hospital, Seoul, Republic of Korea (M.W.Y.)
| | - Myung-Won You
- From the Department of Radiology and Research Institute of Radiology (H.J.P., S.Y.K., S.H.P., J.H.B., H.J.K.) and Department of Bioengineering, Asan Medical Institute of Convergence Science and Technology (K.S., S.G.K., N.K.), Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea; and Department of Radiology, Kyung Hee University Hospital, Seoul, Republic of Korea (M.W.Y.)
| | - Sung-Gu Kyung
- From the Department of Radiology and Research Institute of Radiology (H.J.P., S.Y.K., S.H.P., J.H.B., H.J.K.) and Department of Bioengineering, Asan Medical Institute of Convergence Science and Technology (K.S., S.G.K., N.K.), Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea; and Department of Radiology, Kyung Hee University Hospital, Seoul, Republic of Korea (M.W.Y.)
| | - So Yeon Kim
- From the Department of Radiology and Research Institute of Radiology (H.J.P., S.Y.K., S.H.P., J.H.B., H.J.K.) and Department of Bioengineering, Asan Medical Institute of Convergence Science and Technology (K.S., S.G.K., N.K.), Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea; and Department of Radiology, Kyung Hee University Hospital, Seoul, Republic of Korea (M.W.Y.)
| | - Seong Ho Park
- From the Department of Radiology and Research Institute of Radiology (H.J.P., S.Y.K., S.H.P., J.H.B., H.J.K.) and Department of Bioengineering, Asan Medical Institute of Convergence Science and Technology (K.S., S.G.K., N.K.), Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea; and Department of Radiology, Kyung Hee University Hospital, Seoul, Republic of Korea (M.W.Y.)
| | - Jae Ho Byun
- From the Department of Radiology and Research Institute of Radiology (H.J.P., S.Y.K., S.H.P., J.H.B., H.J.K.) and Department of Bioengineering, Asan Medical Institute of Convergence Science and Technology (K.S., S.G.K., N.K.), Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea; and Department of Radiology, Kyung Hee University Hospital, Seoul, Republic of Korea (M.W.Y.)
| | - Namkug Kim
- From the Department of Radiology and Research Institute of Radiology (H.J.P., S.Y.K., S.H.P., J.H.B., H.J.K.) and Department of Bioengineering, Asan Medical Institute of Convergence Science and Technology (K.S., S.G.K., N.K.), Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea; and Department of Radiology, Kyung Hee University Hospital, Seoul, Republic of Korea (M.W.Y.)
| | - Hyoung Jung Kim
- From the Department of Radiology and Research Institute of Radiology (H.J.P., S.Y.K., S.H.P., J.H.B., H.J.K.) and Department of Bioengineering, Asan Medical Institute of Convergence Science and Technology (K.S., S.G.K., N.K.), Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea; and Department of Radiology, Kyung Hee University Hospital, Seoul, Republic of Korea (M.W.Y.)
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Chen PT, Wu T, Wang P, Chang D, Liu KL, Wu MS, Roth HR, Lee PC, Liao WC, Wang W. Pancreatic Cancer Detection on CT Scans with Deep Learning: A Nationwide Population-based Study. Radiology 2023; 306:172-182. [PMID: 36098642 DOI: 10.1148/radiol.220152] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Background Approximately 40% of pancreatic tumors smaller than 2 cm are missed at abdominal CT. Purpose To develop and to validate a deep learning (DL)-based tool able to detect pancreatic cancer at CT. Materials and Methods Retrospectively collected contrast-enhanced CT studies in patients diagnosed with pancreatic cancer between January 2006 and July 2018 were compared with CT studies of individuals with a normal pancreas (control group) obtained between January 2004 and December 2019. An end-to-end tool comprising a segmentation convolutional neural network (CNN) and a classifier ensembling five CNNs was developed and validated in the internal test set and a nationwide real-world validation set. The sensitivities of the computer-aided detection (CAD) tool and radiologist interpretation were compared using the McNemar test. Results A total of 546 patients with pancreatic cancer (mean age, 65 years ± 12 [SD], 297 men) and 733 control subjects were randomly divided into training, validation, and test sets. In the internal test set, the DL tool achieved 89.9% (98 of 109; 95% CI: 82.7, 94.9) sensitivity and 95.9% (141 of 147; 95% CI: 91.3, 98.5) specificity (area under the receiver operating characteristic curve [AUC], 0.96; 95% CI: 0.94, 0.99), without a significant difference (P = .11) in sensitivity compared with the original radiologist report (96.1% [98 of 102]; 95% CI: 90.3, 98.9). In a test set of 1473 real-world CT studies (669 malignant, 804 control) from institutions throughout Taiwan, the DL tool distinguished between CT malignant and control studies with 89.7% (600 of 669; 95% CI: 87.1, 91.9) sensitivity and 92.8% specificity (746 of 804; 95% CI: 90.8, 94.5) (AUC, 0.95; 95% CI: 0.94, 0.96), with 74.7% (68 of 91; 95% CI: 64.5, 83.3) sensitivity for malignancies smaller than 2 cm. Conclusion The deep learning-based tool enabled accurate detection of pancreatic cancer on CT scans, with reasonable sensitivity for tumors smaller than 2 cm. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Aisen and Rodrigues in this issue.
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Affiliation(s)
- Po-Ting Chen
- From the Department of Medical Imaging (P.T.C., K.L.L.) and Division of Gastroenterology and Hepatology, Department of Internal Medicine (M.S.W., W.C.L.), National Taiwan University Hospital, National Taiwan University College of Medicine, Taipei, Taiwan; Institute of Applied Mathematical Sciences (T.W., D.C., W.W.) and Departments of Computer Science and Information Engineering (P.W.) and Internal Medicine, College of Medicine (M.S.W., W.C.L.), National Taiwan University, No. 1, Section 4, Roosevelt Rd, Taipei 10617, Taiwan; Department of Medical Imaging, National Taiwan University Cancer Center, Taipei, Taiwan (K.L.L.); NVIDIA, Bethesda, Md (H.R.R.); and National Health Insurance Administration, Ministry of Health and Welfare, Taipei, Taiwan (P.C.L.)
| | - Tinghui Wu
- From the Department of Medical Imaging (P.T.C., K.L.L.) and Division of Gastroenterology and Hepatology, Department of Internal Medicine (M.S.W., W.C.L.), National Taiwan University Hospital, National Taiwan University College of Medicine, Taipei, Taiwan; Institute of Applied Mathematical Sciences (T.W., D.C., W.W.) and Departments of Computer Science and Information Engineering (P.W.) and Internal Medicine, College of Medicine (M.S.W., W.C.L.), National Taiwan University, No. 1, Section 4, Roosevelt Rd, Taipei 10617, Taiwan; Department of Medical Imaging, National Taiwan University Cancer Center, Taipei, Taiwan (K.L.L.); NVIDIA, Bethesda, Md (H.R.R.); and National Health Insurance Administration, Ministry of Health and Welfare, Taipei, Taiwan (P.C.L.)
| | - Pochuan Wang
- From the Department of Medical Imaging (P.T.C., K.L.L.) and Division of Gastroenterology and Hepatology, Department of Internal Medicine (M.S.W., W.C.L.), National Taiwan University Hospital, National Taiwan University College of Medicine, Taipei, Taiwan; Institute of Applied Mathematical Sciences (T.W., D.C., W.W.) and Departments of Computer Science and Information Engineering (P.W.) and Internal Medicine, College of Medicine (M.S.W., W.C.L.), National Taiwan University, No. 1, Section 4, Roosevelt Rd, Taipei 10617, Taiwan; Department of Medical Imaging, National Taiwan University Cancer Center, Taipei, Taiwan (K.L.L.); NVIDIA, Bethesda, Md (H.R.R.); and National Health Insurance Administration, Ministry of Health and Welfare, Taipei, Taiwan (P.C.L.)
| | - Dawei Chang
- From the Department of Medical Imaging (P.T.C., K.L.L.) and Division of Gastroenterology and Hepatology, Department of Internal Medicine (M.S.W., W.C.L.), National Taiwan University Hospital, National Taiwan University College of Medicine, Taipei, Taiwan; Institute of Applied Mathematical Sciences (T.W., D.C., W.W.) and Departments of Computer Science and Information Engineering (P.W.) and Internal Medicine, College of Medicine (M.S.W., W.C.L.), National Taiwan University, No. 1, Section 4, Roosevelt Rd, Taipei 10617, Taiwan; Department of Medical Imaging, National Taiwan University Cancer Center, Taipei, Taiwan (K.L.L.); NVIDIA, Bethesda, Md (H.R.R.); and National Health Insurance Administration, Ministry of Health and Welfare, Taipei, Taiwan (P.C.L.)
| | - Kao-Lang Liu
- From the Department of Medical Imaging (P.T.C., K.L.L.) and Division of Gastroenterology and Hepatology, Department of Internal Medicine (M.S.W., W.C.L.), National Taiwan University Hospital, National Taiwan University College of Medicine, Taipei, Taiwan; Institute of Applied Mathematical Sciences (T.W., D.C., W.W.) and Departments of Computer Science and Information Engineering (P.W.) and Internal Medicine, College of Medicine (M.S.W., W.C.L.), National Taiwan University, No. 1, Section 4, Roosevelt Rd, Taipei 10617, Taiwan; Department of Medical Imaging, National Taiwan University Cancer Center, Taipei, Taiwan (K.L.L.); NVIDIA, Bethesda, Md (H.R.R.); and National Health Insurance Administration, Ministry of Health and Welfare, Taipei, Taiwan (P.C.L.)
| | - Ming-Shiang Wu
- From the Department of Medical Imaging (P.T.C., K.L.L.) and Division of Gastroenterology and Hepatology, Department of Internal Medicine (M.S.W., W.C.L.), National Taiwan University Hospital, National Taiwan University College of Medicine, Taipei, Taiwan; Institute of Applied Mathematical Sciences (T.W., D.C., W.W.) and Departments of Computer Science and Information Engineering (P.W.) and Internal Medicine, College of Medicine (M.S.W., W.C.L.), National Taiwan University, No. 1, Section 4, Roosevelt Rd, Taipei 10617, Taiwan; Department of Medical Imaging, National Taiwan University Cancer Center, Taipei, Taiwan (K.L.L.); NVIDIA, Bethesda, Md (H.R.R.); and National Health Insurance Administration, Ministry of Health and Welfare, Taipei, Taiwan (P.C.L.)
| | - Holger R Roth
- From the Department of Medical Imaging (P.T.C., K.L.L.) and Division of Gastroenterology and Hepatology, Department of Internal Medicine (M.S.W., W.C.L.), National Taiwan University Hospital, National Taiwan University College of Medicine, Taipei, Taiwan; Institute of Applied Mathematical Sciences (T.W., D.C., W.W.) and Departments of Computer Science and Information Engineering (P.W.) and Internal Medicine, College of Medicine (M.S.W., W.C.L.), National Taiwan University, No. 1, Section 4, Roosevelt Rd, Taipei 10617, Taiwan; Department of Medical Imaging, National Taiwan University Cancer Center, Taipei, Taiwan (K.L.L.); NVIDIA, Bethesda, Md (H.R.R.); and National Health Insurance Administration, Ministry of Health and Welfare, Taipei, Taiwan (P.C.L.)
| | - Po-Chang Lee
- From the Department of Medical Imaging (P.T.C., K.L.L.) and Division of Gastroenterology and Hepatology, Department of Internal Medicine (M.S.W., W.C.L.), National Taiwan University Hospital, National Taiwan University College of Medicine, Taipei, Taiwan; Institute of Applied Mathematical Sciences (T.W., D.C., W.W.) and Departments of Computer Science and Information Engineering (P.W.) and Internal Medicine, College of Medicine (M.S.W., W.C.L.), National Taiwan University, No. 1, Section 4, Roosevelt Rd, Taipei 10617, Taiwan; Department of Medical Imaging, National Taiwan University Cancer Center, Taipei, Taiwan (K.L.L.); NVIDIA, Bethesda, Md (H.R.R.); and National Health Insurance Administration, Ministry of Health and Welfare, Taipei, Taiwan (P.C.L.)
| | - Wei-Chih Liao
- From the Department of Medical Imaging (P.T.C., K.L.L.) and Division of Gastroenterology and Hepatology, Department of Internal Medicine (M.S.W., W.C.L.), National Taiwan University Hospital, National Taiwan University College of Medicine, Taipei, Taiwan; Institute of Applied Mathematical Sciences (T.W., D.C., W.W.) and Departments of Computer Science and Information Engineering (P.W.) and Internal Medicine, College of Medicine (M.S.W., W.C.L.), National Taiwan University, No. 1, Section 4, Roosevelt Rd, Taipei 10617, Taiwan; Department of Medical Imaging, National Taiwan University Cancer Center, Taipei, Taiwan (K.L.L.); NVIDIA, Bethesda, Md (H.R.R.); and National Health Insurance Administration, Ministry of Health and Welfare, Taipei, Taiwan (P.C.L.)
| | - Weichung Wang
- From the Department of Medical Imaging (P.T.C., K.L.L.) and Division of Gastroenterology and Hepatology, Department of Internal Medicine (M.S.W., W.C.L.), National Taiwan University Hospital, National Taiwan University College of Medicine, Taipei, Taiwan; Institute of Applied Mathematical Sciences (T.W., D.C., W.W.) and Departments of Computer Science and Information Engineering (P.W.) and Internal Medicine, College of Medicine (M.S.W., W.C.L.), National Taiwan University, No. 1, Section 4, Roosevelt Rd, Taipei 10617, Taiwan; Department of Medical Imaging, National Taiwan University Cancer Center, Taipei, Taiwan (K.L.L.); NVIDIA, Bethesda, Md (H.R.R.); and National Health Insurance Administration, Ministry of Health and Welfare, Taipei, Taiwan (P.C.L.)
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Xue Y, Zhu J, Huang X, Xu X, Li X, Zheng Y, Zhu Z, Jin K, Ye J, Gong W, Si K. A multi-feature deep learning system to enhance glaucoma severity diagnosis with high accuracy and fast speed. J Biomed Inform 2022; 136:104233. [DOI: 10.1016/j.jbi.2022.104233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Revised: 09/21/2022] [Accepted: 10/17/2022] [Indexed: 11/06/2022]
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Zavalsiz MT, Alhajj S, Sailunaz K, Ozyer T, Alhajj R. Pancreatic Tumor Detection by Convolutional Neural Networks. 2022 INTERNATIONAL ARAB CONFERENCE ON INFORMATION TECHNOLOGY (ACIT) 2022. [DOI: 10.1109/acit57182.2022.9994181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Affiliation(s)
| | - Sleiman Alhajj
- International School of Medicine, Istanbul Medipol University,Istanbul,Turkey
| | - Kashfia Sailunaz
- University of Calgary,Department of Computer Science,Alberta,Canada
| | - Tansel Ozyer
- Ankara Medipol University,Department of Computer Engineering,Ankara,Turkey
| | - Reda Alhajj
- Istanbul Medipol University,Department of Computer Engineering,Istanbul,Turkey
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Jan Z, El Assadi F, Abd-alrazaq A, Jithesh PV. Artificial Intelligence for the Prediction and Early Diagnosis of Pancreatic Cancer: Scoping Review (Preprint).. [DOI: 10.2196/preprints.44248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
BACKGROUND
Pancreatic cancer is the 12th most common cancer worldwide, with an overall survival rate of 4.9%. Early diagnosis of pancreatic cancer is essential for timely treatment and survival. Artificial intelligence (AI) provides advanced models and algorithms for better diagnosis of pancreatic cancer.
OBJECTIVE
This study aims to explore AI models used for the prediction and early diagnosis of pancreatic cancers as reported in the literature.
METHODS
A scoping review was conducted and reported in line with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. PubMed, Google Scholar, Science Direct, BioRXiv, and MedRxiv were explored to identify relevant articles. Study selection and data extraction were independently conducted by 2 reviewers. Data extracted from the included studies were synthesized narratively.
RESULTS
Of the 1185 publications, 30 studies were included in the scoping review. The included articles reported the use of AI for 6 different purposes. Of these included articles, AI techniques were mostly used for the diagnosis of pancreatic cancer (14/30, 47%). Radiological images (14/30, 47%) were the most frequently used data in the included articles. Most of the included articles used data sets with a size of <1000 samples (11/30, 37%). Deep learning models were the most prominent branch of AI used for pancreatic cancer diagnosis in the studies, and the convolutional neural network was the most used algorithm (18/30, 60%). Six validation approaches were used in the included studies, of which the most frequently used approaches were k-fold cross-validation (10/30, 33%) and external validation (10/30, 33%). A higher level of accuracy (99%) was found in studies that used support vector machine, decision trees, and k-means clustering algorithms.
CONCLUSIONS
This review presents an overview of studies based on AI models and algorithms used to predict and diagnose pancreatic cancer patients. AI is expected to play a vital role in advancing pancreatic cancer prediction and diagnosis. Further research is required to provide data that support clinical decisions in health care.
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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: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [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: 42] [Impact Index Per Article: 14.0] [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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Lakkshmanan A, Ananth CA, Tiroumalmouroughane S. Multi-objective metaheuristics with intelligent deep learning model for pancreatic tumor diagnosis. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-221171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Pancreatic tumor is the deadliest disease which needs earlier identification to reduce the mortality rate. With this motivation, this study introduces a Multi-Objective Metaheuristics with Intelligent Deep Learning Model for Pancreatic Tumor Diagnosis (MOM-IDL) model. The proposed MOM-IDL technique encompasses an adaptive Weiner filter based pre-processing technique to enhance the image quality and get rid of the noise. In addition, multi-level thresholding based segmentation using Kapur’s entropy is employed where the threshold values are optimally chosen by the barnacles mating optimizer (BMO). Besides, densely connected network (DenseNet-169) is employed as a feature extractor and fuzzy support vector machine (FSVM) is utilized as a classifier. For improving the classification performance, the BMO technique was implemented for fine-tuning the parameters of the FSVM model. The design of MOBMO algorithm for threshold selection and parameter optimization processes shows the novelty of the work. A wide range of simulations take place on the benchmark dataset and the experimental results highlighted the enhanced performance of the MOM-IDL technique over the recent state of art techniques.
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Affiliation(s)
| | - C. Anbu Ananth
- Department of CSE, FEAT, Annamalai University, Chidamabaram, Tamilnadu, India
| | - S. Tiroumalmouroughane
- Department of IT, Perunthalaivar Kamarajar Institute of Engineering and Technology, Karaikal, Tamilnadu, India
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Anta JA, Martínez-Ballestero I, Eiroa D, García J, Rodríguez-Comas J. Artificial intelligence for the detection of pancreatic lesions. Int J Comput Assist Radiol Surg 2022; 17:1855-1865. [PMID: 35951286 DOI: 10.1007/s11548-022-02706-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 06/17/2022] [Indexed: 11/30/2022]
Abstract
PURPOSE Pancreatic cancer is one of the most lethal neoplasms among common cancers worldwide, and PCLs are well-known precursors of this type of cancer. Artificial intelligence (AI) could help to improve and speed up the detection and classification of pancreatic lesions. The aim of this review is to summarize the articles addressing the diagnostic yield of artificial intelligence applied to medical imaging (computed tomography [CT] and/or magnetic resonance [MR]) for the detection of pancreatic cancer and pancreatic cystic lesions. METHODS We performed a comprehensive literature search using PubMed, EMBASE, and Scopus (from January 2010 to April 2021) to identify full articles evaluating the diagnostic accuracy of AI-based methods processing CT or MR images to detect pancreatic ductal adenocarcinoma (PDAC) or pancreatic cystic lesions (PCLs). RESULTS We found 20 studies meeting our inclusion criteria. Most of the AI-based systems used were convolutional neural networks. Ten studies addressed the use of AI to detect PDAC, eight studies aimed to detect and classify PCLs, and 4 aimed to predict the presence of high-grade dysplasia or cancer. CONCLUSION AI techniques have shown to be a promising tool which is expected to be helpful for most radiologists' tasks. However, methodologic concerns must be addressed, and prospective clinical studies should be carried out before implementation in clinical practice.
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Affiliation(s)
- Julia Arribas Anta
- Scientific and Technical Department, Sycai Technologies S.L., Carrer Roc Boronat 117, MediaTIC Building, 08018, Barcelona, Spain.,Department of Gastroenterology, University Hospital, 12 Octubre. Av. de Córdoba, s/n, 28041, Madrid, Spain
| | - Iván Martínez-Ballestero
- Scientific and Technical Department, Sycai Technologies S.L., Carrer Roc Boronat 117, MediaTIC Building, 08018, Barcelona, Spain
| | - Daniel Eiroa
- Scientific and Technical Department, Sycai Technologies S.L., Carrer Roc Boronat 117, MediaTIC Building, 08018, Barcelona, Spain.,Department of Radiology, Institut de Diagnòstic per la Imatge (IDI), Hospital Universitari Vall d'Hebrón, Passeig de la Vall d'Hebron, 119-129, 08035, Barcelona, Spain
| | - Javier García
- Scientific and Technical Department, Sycai Technologies S.L., Carrer Roc Boronat 117, MediaTIC Building, 08018, Barcelona, Spain
| | - Júlia Rodríguez-Comas
- Scientific and Technical Department, Sycai Technologies S.L., Carrer Roc Boronat 117, MediaTIC Building, 08018, Barcelona, Spain.
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Schuurmans M, Alves N, Vendittelli P, Huisman H, Hermans J. Setting the Research Agenda for Clinical Artificial Intelligence in Pancreatic Adenocarcinoma Imaging. Cancers (Basel) 2022; 14:cancers14143498. [PMID: 35884559 PMCID: PMC9316850 DOI: 10.3390/cancers14143498] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 07/07/2022] [Accepted: 07/15/2022] [Indexed: 11/16/2022] Open
Abstract
Simple Summary Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers worldwide, associated with a 98% loss of life expectancy and a 30% increase in disability-adjusted life years. Image-based artificial intelligence (AI) can help improve outcomes for PDAC given that current clinical guidelines are non-uniform and lack evidence-based consensus. However, research on image-based AI for PDAC is too scattered and lacking in sufficient quality to be incorporated into clinical workflows. In this review, an international, multi-disciplinary team of the world’s leading experts in pancreatic cancer breaks down the patient pathway and pinpoints the current clinical touchpoints in each stage. The available PDAC imaging AI literature addressing each pathway stage is then rigorously analyzed, and current performance and pitfalls are identified in a comprehensive overview. Finally, the future research agenda for clinically relevant, image-driven AI in PDAC is proposed. Abstract Pancreatic ductal adenocarcinoma (PDAC), estimated to become the second leading cause of cancer deaths in western societies by 2030, was flagged as a neglected cancer by the European Commission and the United States Congress. Due to lack of investment in research and development, combined with a complex and aggressive tumour biology, PDAC overall survival has not significantly improved the past decades. Cross-sectional imaging and histopathology play a crucial role throughout the patient pathway. However, current clinical guidelines for diagnostic workup, patient stratification, treatment response assessment, and follow-up are non-uniform and lack evidence-based consensus. Artificial Intelligence (AI) can leverage multimodal data to improve patient outcomes, but PDAC AI research is too scattered and lacking in quality to be incorporated into clinical workflows. This review describes the patient pathway and derives touchpoints for image-based AI research in collaboration with a multi-disciplinary, multi-institutional expert panel. The literature exploring AI to address these touchpoints is thoroughly retrieved and analysed to identify the existing trends and knowledge gaps. The results show absence of multi-institutional, well-curated datasets, an essential building block for robust AI applications. Furthermore, most research is unimodal, does not use state-of-the-art AI techniques, and lacks reliable ground truth. Based on this, the future research agenda for clinically relevant, image-driven AI in PDAC is proposed.
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Affiliation(s)
- Megan Schuurmans
- Diagnostic Image Analysis Group, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands; (P.V.); (H.H.)
- Correspondence: (M.S.); (N.A.)
| | - Natália Alves
- Diagnostic Image Analysis Group, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands; (P.V.); (H.H.)
- Correspondence: (M.S.); (N.A.)
| | - Pierpaolo Vendittelli
- Diagnostic Image Analysis Group, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands; (P.V.); (H.H.)
| | - Henkjan Huisman
- Diagnostic Image Analysis Group, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands; (P.V.); (H.H.)
| | - John Hermans
- Department of Medical Imaging, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands;
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Painuli D, Bhardwaj S, Köse U. Recent advancement in cancer diagnosis using machine learning and deep learning techniques: A comprehensive review. Comput Biol Med 2022; 146:105580. [PMID: 35551012 DOI: 10.1016/j.compbiomed.2022.105580] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Revised: 04/14/2022] [Accepted: 04/30/2022] [Indexed: 02/07/2023]
Abstract
Being a second most cause of mortality worldwide, cancer has been identified as a perilous disease for human beings, where advance stage diagnosis may not help much in safeguarding patients from mortality. Thus, efforts to provide a sustainable architecture with proven cancer prevention estimate and provision for early diagnosis of cancer is the need of hours. Advent of machine learning methods enriched cancer diagnosis area with its overwhelmed efficiency & low error-rate then humans. A significant revolution has been witnessed in the development of machine learning & deep learning assisted system for segmentation & classification of various cancers during past decade. This research paper includes a review of various types of cancer detection via different data modalities using machine learning & deep learning-based methods along with different feature extraction techniques and benchmark datasets utilized in the recent six years studies. The focus of this study is to review, analyse, classify, and address the recent development in cancer detection and diagnosis of six types of cancers i.e., breast, lung, liver, skin, brain and pancreatic cancer, using machine learning & deep learning techniques. Various state-of-the-art technique are clustered into same group and results are examined through key performance indicators like accuracy, area under the curve, precision, sensitivity, dice score on benchmark datasets and concluded with future research work challenges.
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Affiliation(s)
- Deepak Painuli
- Department of Computer Science and Engineering, Gurukula Kangri Vishwavidyalaya, Haridwar, India.
| | - Suyash Bhardwaj
- Department of Computer Science and Engineering, Gurukula Kangri Vishwavidyalaya, Haridwar, India
| | - Utku Köse
- Department of Computer Engineering, Suleyman Demirel University, Isparta, Turkey
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Lin KW, Ang TL, Li JW. Role of artificial intelligence in early detection and screening for pancreatic adenocarcinoma. Artif Intell Med Imaging 2022; 3:21-32. [DOI: 10.35711/aimi.v3.i2.21] [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: 12/16/2021] [Revised: 02/12/2022] [Accepted: 03/17/2022] [Indexed: 02/06/2023] Open
Abstract
Pancreatic adenocarcinoma remains to be one of the deadliest malignancies in the world despite treatment advancement over the past few decades. Its low survival rates and poor prognosis can be attributed to ambiguity in recommendations for screening and late symptom onset, contributing to its late presentation. In the recent years, artificial intelligence (AI) as emerged as a field to aid in the process of clinical decision making. Considerable efforts have been made in the realm of AI to screen for and predict future development of pancreatic ductal adenocarcinoma. This review discusses the use of AI in early detection and screening for pancreatic adenocarcinoma, and factors which may limit its use in a clinical setting.
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Affiliation(s)
- Kenneth Weicong Lin
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore 529889, Singapore
| | - Tiing Leong Ang
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore 529889, Singapore
| | - James Weiquan Li
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore 529889, Singapore
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Vaiyapuri T, Dutta AK, Punithavathi ISH, Duraipandy P, Alotaibi SS, Alsolai H, Mohamed A, Mahgoub H. Intelligent Deep-Learning-Enabled Decision-Making Medical System for Pancreatic Tumor Classification on CT Images. Healthcare (Basel) 2022; 10:healthcare10040677. [PMID: 35455854 PMCID: PMC9027672 DOI: 10.3390/healthcare10040677] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 03/26/2022] [Accepted: 03/28/2022] [Indexed: 12/13/2022] Open
Abstract
Decision-making medical systems (DMS) refer to the design of decision techniques in the healthcare sector. They involve a procedure of employing ideas and decisions related to certain processes such as data acquisition, processing, judgment, and conclusion. Pancreatic cancer is a lethal type of cancer, and its prediction is ineffective with current techniques. Automated detection and classification of pancreatic tumors can be provided by the computer-aided diagnosis (CAD) model using radiological images such as computed tomography (CT) and magnetic resonance imaging (MRI). The recently developed machine learning (ML) and deep learning (DL) models can be utilized for the automated and timely detection of pancreatic cancer. In light of this, this article introduces an intelligent deep-learning-enabled decision-making medical system for pancreatic tumor classification (IDLDMS-PTC) using CT images. The major intention of the IDLDMS-PTC technique is to examine the CT images for the existence of pancreatic tumors. The IDLDMS-PTC model derives an emperor penguin optimizer (EPO) with multilevel thresholding (EPO-MLT) technique for pancreatic tumor segmentation. Additionally, the MobileNet model is applied as a feature extractor with optimal auto encoder (AE) for pancreatic tumor classification. In order to optimally adjust the weight and bias values of the AE technique, the multileader optimization (MLO) technique is utilized. The design of the EPO algorithm for optimal threshold selection and the MLO algorithm for parameter tuning shows the novelty. A wide range of simulations was executed on benchmark datasets, and the outcomes reported the promising performance of the IDLDMS-PTC model on the existing methods.
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Affiliation(s)
- Thavavel Vaiyapuri
- Department of Computer Sciences, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia;
| | - Ashit Kumar Dutta
- Department of Computer Science and Information Systems, College of Applied Sciences, AlMaarefa University, Ad Diriyah, Riyadh 13713, Saudi Arabia;
| | - I. S. Hephzi Punithavathi
- Department of Computer Science and Engineering, Sphoorthy Engineering College, Telangana, Hyderabad 501510, India;
| | - P. Duraipandy
- Department of Electrical and Electronics Engineering, J. B. Institute of Engineering and Technology, Telangana, Hyderabad 500075, India;
| | - Saud S. Alotaibi
- Department of Information Systems, College of Computing and Information System, Umm Al-Qura University, Mecca 21911, Saudi Arabia;
| | - Hadeel Alsolai
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia;
| | - Abdullah Mohamed
- Research Centre, Future University in Egypt, New Cairo, Cairo 11745, Egypt;
| | - Hany Mahgoub
- Department of Computer Science, College of Science & Art at Mahayil, King Khalid University, Abha 61421, Saudi Arabia
- Correspondence:
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Preuss K, Thach N, Liang X, Baine M, Chen J, Zhang C, Du H, Yu H, Lin C, Hollingsworth MA, Zheng D. Using Quantitative Imaging for Personalized Medicine in Pancreatic Cancer: A Review of Radiomics and Deep Learning Applications. Cancers (Basel) 2022; 14:cancers14071654. [PMID: 35406426 PMCID: PMC8997008 DOI: 10.3390/cancers14071654] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Revised: 03/16/2022] [Accepted: 03/18/2022] [Indexed: 12/12/2022] Open
Abstract
Simple Summary With a five-year survival rate of only 3% for the majority of patients, pancreatic cancer is a global healthcare challenge. Radiomics and deep learning, two novel quantitative imaging methods that treat medical images as minable data instead of just pictures, have shown promise in advancing personalized management of pancreatic cancer through diagnosing precursor diseases, early detection, accurate diagnosis, and treatment personalization. Radiomics and deep learning methods aim to collect hidden information in medical images that is missed by conventional radiology practices through expanding the data search and comparing information across different patients. Both methods have been studied and applied in pancreatic cancer. In this review, we focus on the current progress of these two methods in pancreatic cancer and provide a comprehensive narrative review on the topic. With better regulation, enhanced workflow, and larger prospective patient datasets, radiomics and deep learning methods could show real hope in the battle against pancreatic cancer through personalized precision medicine. Abstract As the most lethal major cancer, pancreatic cancer is a global healthcare challenge. Personalized medicine utilizing cutting-edge multi-omics data holds potential for major breakthroughs in tackling this critical problem. Radiomics and deep learning, two trendy quantitative imaging methods that take advantage of data science and modern medical imaging, have shown increasing promise in advancing the precision management of pancreatic cancer via diagnosing of precursor diseases, early detection, accurate diagnosis, and treatment personalization and optimization. Radiomics employs manually-crafted features, while deep learning applies computer-generated automatic features. These two methods aim to mine hidden information in medical images that is missed by conventional radiology and gain insights by systematically comparing the quantitative image information across different patients in order to characterize unique imaging phenotypes. Both methods have been studied and applied in various pancreatic cancer clinical applications. In this review, we begin with an introduction to the clinical problems and the technology. After providing technical overviews of the two methods, this review focuses on the current progress of clinical applications in precancerous lesion diagnosis, pancreatic cancer detection and diagnosis, prognosis prediction, treatment stratification, and radiogenomics. The limitations of current studies and methods are discussed, along with future directions. With better standardization and optimization of the workflow from image acquisition to analysis and with larger and especially prospective high-quality datasets, radiomics and deep learning methods could show real hope in the battle against pancreatic cancer through big data-based high-precision personalization.
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Affiliation(s)
- Kiersten Preuss
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USA; (K.P.); (N.T.); (M.B.); (J.C.); (C.L.)
- Department of Nutrition and Health Sciences, University of Nebraska Lincoln, Lincoln, NE 68588, USA
| | - Nate Thach
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USA; (K.P.); (N.T.); (M.B.); (J.C.); (C.L.)
- Department of Computer Science, University of Nebraska Lincoln, Lincoln, NE 68588, USA;
| | - Xiaoying Liang
- Department of Radiation Oncology, Mayo Clinic, Jacksonville, FL 32224, USA;
| | - Michael Baine
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USA; (K.P.); (N.T.); (M.B.); (J.C.); (C.L.)
| | - Justin Chen
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USA; (K.P.); (N.T.); (M.B.); (J.C.); (C.L.)
- Naperville North High School, Naperville, IL 60563, USA
| | - Chi Zhang
- School of Biological Sciences, University of Nebraska Lincoln, Lincoln, NE 68588, USA;
| | - Huijing Du
- Department of Mathematics, University of Nebraska Lincoln, Lincoln, NE 68588, USA;
| | - Hongfeng Yu
- Department of Computer Science, University of Nebraska Lincoln, Lincoln, NE 68588, USA;
| | - Chi Lin
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USA; (K.P.); (N.T.); (M.B.); (J.C.); (C.L.)
| | - Michael A. Hollingsworth
- Eppley Institute for Research in Cancer, University of Nebraska Medical Center, Omaha, NE 68198, USA;
| | - Dandan Zheng
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USA; (K.P.); (N.T.); (M.B.); (J.C.); (C.L.)
- Department of Radiation Oncology, University of Rochester, Rochester, NY 14626, USA
- Correspondence: ; Tel.: +1-(585)-276-3255
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Schlanger D, Graur F, Popa C, Moiș E, Al Hajjar N. The role of artificial intelligence in pancreatic surgery: a systematic review. Updates Surg 2022; 74:417-429. [PMID: 35237939 DOI: 10.1007/s13304-022-01255-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 02/10/2022] [Indexed: 12/13/2022]
Abstract
Artificial intelligence (AI), including machine learning (ML), is being slowly incorporated in medical practice, to provide a more precise and personalized approach. Pancreatic surgery is an evolving field, which offers the only curative option for patients with pancreatic cancer. Increasing amounts of data are available in medicine: AI and ML can help incorporate large amounts of information in clinical practice. We conducted a systematic review, based on PRISMA criteria, of studies that explored the use of AI or ML algorithms in pancreatic surgery. To our knowledge, this is the first systematic review on this topic. Twenty-five eligible studies were included in this review; 12 studies with implications in the preoperative diagnosis, while 13 studies had implications in patient evolution. Preoperative diagnosis, such as predicting the malignancy of IPMNs, differential diagnosis between pancreatic cystic lesions, classification of different pancreatic tumours, and establishment of the correct management for each of these lesions, can be facilitated through different AI or ML algorithms. Postoperative evolution can also be predicted, and some studies reported prediction models for complications, including postoperative pancreatic fistula, while other studies have analysed the implications for prognosis evaluation (from predicting a textbook outcome, the risk of metastasis or relapse, or the mortality rate and survival). One study discussed the possibility of predicting an intraoperative complication-massive intraoperative bleeding. Artificial intelligence and machine learning models have promising applications in pancreatic surgery, in the preoperative period (high-accuracy diagnosis) and postoperative setting (prognosis evaluation and complication prediction), and the intraoperative applications have been less explored.
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Affiliation(s)
- D Schlanger
- "Iuliu Haţieganu" University of Medicine and Pharmacy, Cluj-Napoca, Romania street Emil Isac no 13, 400023, Cluj-Napoca, Romania.,Surgery Department, Regional Institute of Gastroenterology and Hepatology "Prof. Dr. O. Fodor", Cluj-Napoca, Romania. Street Croitorilor no 19-21, 400162, Cluj-Napoca, Romania
| | - F Graur
- "Iuliu Haţieganu" University of Medicine and Pharmacy, Cluj-Napoca, Romania street Emil Isac no 13, 400023, Cluj-Napoca, Romania. .,Surgery Department, Regional Institute of Gastroenterology and Hepatology "Prof. Dr. O. Fodor", Cluj-Napoca, Romania. Street Croitorilor no 19-21, 400162, Cluj-Napoca, Romania.
| | - C Popa
- "Iuliu Haţieganu" University of Medicine and Pharmacy, Cluj-Napoca, Romania street Emil Isac no 13, 400023, Cluj-Napoca, Romania.,Surgery Department, Regional Institute of Gastroenterology and Hepatology "Prof. Dr. O. Fodor", Cluj-Napoca, Romania. Street Croitorilor no 19-21, 400162, Cluj-Napoca, Romania
| | - E Moiș
- "Iuliu Haţieganu" University of Medicine and Pharmacy, Cluj-Napoca, Romania street Emil Isac no 13, 400023, Cluj-Napoca, Romania.,Surgery Department, Regional Institute of Gastroenterology and Hepatology "Prof. Dr. O. Fodor", Cluj-Napoca, Romania. Street Croitorilor no 19-21, 400162, Cluj-Napoca, Romania
| | - N Al Hajjar
- "Iuliu Haţieganu" University of Medicine and Pharmacy, Cluj-Napoca, Romania street Emil Isac no 13, 400023, Cluj-Napoca, Romania.,Surgery Department, Regional Institute of Gastroenterology and Hepatology "Prof. Dr. O. Fodor", Cluj-Napoca, Romania. Street Croitorilor no 19-21, 400162, Cluj-Napoca, Romania
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Fully Automatic Deep Learning Framework for Pancreatic Ductal Adenocarcinoma Detection on Computed Tomography. Cancers (Basel) 2022; 14:cancers14020376. [PMID: 35053538 PMCID: PMC8774174 DOI: 10.3390/cancers14020376] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 01/06/2022] [Accepted: 01/12/2022] [Indexed: 02/01/2023] Open
Abstract
Simple Summary Early image-based diagnosis is crucial to improve outcomes in pancreatic ductal adenocarcinoma (PDAC) patients, but is challenging even for experienced radiologists. Artificial intelligence has the potential to assist in early diagnosis by leveraging high amounts of data to automatically detect small (<2 cm) lesions. In this study, the state-of-the-art, self-configuring framework for medical segmentation nnUnet was used to develop a fully automatic pipeline for the detection and localization of PDAC lesions on contrast-enhanced computed tomography scans, with a focus on small lesions. Furthermore, the impact of integrating the surrounding anatomy (which is known to be relevant to clinical diagnosis) into deep learning models was assessed. The developed automatic framework was tested in an external, publicly available test set, and the results showed that state-of-the-art deep learning can detect small PDAC lesions and benefits from anatomy information. Abstract Early detection improves prognosis in pancreatic ductal adenocarcinoma (PDAC), but is challenging as lesions are often small and poorly defined on contrast-enhanced computed tomography scans (CE-CT). Deep learning can facilitate PDAC diagnosis; however, current models still fail to identify small (<2 cm) lesions. In this study, state-of-the-art deep learning models were used to develop an automatic framework for PDAC detection, focusing on small lesions. Additionally, the impact of integrating the surrounding anatomy was investigated. CE-CT scans from a cohort of 119 pathology-proven PDAC patients and a cohort of 123 patients without PDAC were used to train a nnUnet for automatic lesion detection and segmentation (nnUnet_T). Two additional nnUnets were trained to investigate the impact of anatomy integration: (1) segmenting the pancreas and tumor (nnUnet_TP), and (2) segmenting the pancreas, tumor, and multiple surrounding anatomical structures (nnUnet_MS). An external, publicly available test set was used to compare the performance of the three networks. The nnUnet_MS achieved the best performance, with an area under the receiver operating characteristic curve of 0.91 for the whole test set and 0.88 for tumors <2 cm, showing that state-of-the-art deep learning can detect small PDAC and benefits from anatomy information.
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Althobaiti MM, Almulihi A, Ashour AA, Mansour RF, Gupta D. Design of Optimal Deep Learning-Based Pancreatic Tumor and Nontumor Classification Model Using Computed Tomography Scans. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:2872461. [PMID: 35070232 PMCID: PMC8769827 DOI: 10.1155/2022/2872461] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 12/10/2021] [Accepted: 12/17/2021] [Indexed: 12/18/2022]
Abstract
Pancreatic tumor is a lethal kind of tumor and its prediction is really poor in the current scenario. Automated pancreatic tumor classification using computer-aided diagnosis (CAD) model is necessary to track, predict, and classify the existence of pancreatic tumors. Artificial intelligence (AI) can offer extensive diagnostic expertise and accurate interventional image interpretation. With this motivation, this study designs an optimal deep learning based pancreatic tumor and nontumor classification (ODL-PTNTC) model using CT images. The goal of the ODL-PTNTC technique is to detect and classify the existence of pancreatic tumors and nontumor. The proposed ODL-PTNTC technique includes adaptive window filtering (AWF) technique to remove noise existing in it. In addition, sailfish optimizer based Kapur's Thresholding (SFO-KT) technique is employed for image segmentation process. Moreover, feature extraction using Capsule Network (CapsNet) is derived to generate a set of feature vectors. Furthermore, Political Optimizer (PO) with Cascade Forward Neural Network (CFNN) is employed for classification purposes. In order to validate the enhanced performance of the ODL-PTNTC technique, a series of simulations take place and the results are investigated under several aspects. A comprehensive comparative results analysis stated the promising performance of the ODL-PTNTC technique over the recent approaches.
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Affiliation(s)
- Maha M. Althobaiti
- Department of Computer Science College of Computing and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
| | - Ahmed Almulihi
- Department of Computer Science College of Computing and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
| | - Amal Adnan Ashour
- Department of Oral & Maxillofacial Surgery and Diagnostic Sciences Faculty of Dentistry, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
| | - Romany F. Mansour
- Department of Mathematics Faculty of Science, New Valley University, El-Kharga 72511, Egypt
| | - Deepak Gupta
- Department of Computer Science & Engineering, Maharaja Agrasen Institute of Technology, Delhi, India
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Oh JH, Kim HG, Lee KM, Ryu CW, Park S, Jang JH, Choi HS, Kim EJ. Effective End-to-End Deep Learning Process in Medical Imaging Using Independent Task Learning: Application for Diagnosis of Maxillary Sinusitis. Yonsei Med J 2021; 62:1125-1135. [PMID: 34816643 PMCID: PMC8612852 DOI: 10.3349/ymj.2021.62.12.1125] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 08/25/2021] [Accepted: 09/27/2021] [Indexed: 12/29/2022] Open
Abstract
PURPOSE This study aimed to propose an effective end-to-end process in medical imaging using an independent task learning (ITL) algorithm and to evaluate its performance in maxillary sinusitis applications. MATERIALS AND METHODS For the internal dataset, 2122 Waters' view X-ray images, which included 1376 normal and 746 sinusitis images, were divided into training (n=1824) and test (n=298) datasets. For external validation, 700 images, including 379 normal and 321 sinusitis images, from three different institutions were evaluated. To develop the automatic diagnosis system algorithm, four processing steps were performed: 1) preprocessing for ITL, 2) facial patch detection, 3) maxillary sinusitis detection, and 4) a localization report with the sinusitis detector. RESULTS The accuracy of facial patch detection, which was the first step in the end-to-end algorithm, was 100%, 100%, 99.5%, and 97.5% for the internal set and external validation sets #1, #2, and #3, respectively. The accuracy and area under the receiver operating characteristic curve (AUC) of maxillary sinusitis detection were 88.93% (0.89), 91.67% (0.90), 90.45% (0.86), and 85.13% (0.85) for the internal set and external validation sets #1, #2, and #3, respectively. The accuracy and AUC of the fully automatic sinusitis diagnosis system, including site localization, were 79.87% (0.80), 84.67% (0.82), 83.92% (0.82), and 73.85% (0.74) for the internal set and external validation sets #1, #2, and #3, respectively. CONCLUSION ITL application for maxillary sinusitis showed reasonable performance in internal and external validation tests, compared with applications used in previous studies.
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Affiliation(s)
- Jang-Hoon Oh
- Department of Biomedical Science and Technology, Graduate School, Kyung Hee University, Seoul, Korea
| | - Hyug-Gi Kim
- Department of Radiology, Kyung Hee University College of Medicine, Kyung Hee University Hospital, Seoul, Korea
| | - Kyung Mi Lee
- Department of Radiology, Kyung Hee University College of Medicine, Kyung Hee University Hospital, Seoul, Korea.
| | - Chang-Woo Ryu
- Department of Radiology, Kyung Hee University College of Medicine, Kyung Hee University Hospital at Gangdong, Seoul, Korea
| | - Soonchan Park
- Department of Radiology, Kyung Hee University College of Medicine, Kyung Hee University Hospital at Gangdong, Seoul, Korea
| | - Ji Hye Jang
- Department of Radiology, Korea Cancer Center Hospital, Seoul, Korea
| | - Hyun Seok Choi
- Department of Radiology, Seoul Medical Center, Seoul, Korea
| | - Eui Jong Kim
- Department of Radiology, Kyung Hee University College of Medicine, Kyung Hee University Hospital, Seoul, Korea
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Hayashi H, Uemura N, Matsumura K, Zhao L, Sato H, Shiraishi Y, Yamashita YI, Baba H. Recent advances in artificial intelligence for pancreatic ductal adenocarcinoma. World J Gastroenterol 2021; 27:7480-7496. [PMID: 34887644 PMCID: PMC8613738 DOI: 10.3748/wjg.v27.i43.7480] [Citation(s) in RCA: 18] [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: 04/03/2021] [Revised: 08/02/2021] [Accepted: 11/15/2021] [Indexed: 02/06/2023] Open
Abstract
Pancreatic ductal adenocarcinoma (PDAC) remains the most lethal type of cancer. The 5-year survival rate for patients with early-stage diagnosis can be as high as 20%, suggesting that early diagnosis plays a pivotal role in the prognostic improvement of PDAC cases. In the medical field, the broad availability of biomedical data has led to the advent of the "big data" era. To overcome this deadly disease, how to fully exploit big data is a new challenge in the era of precision medicine. Artificial intelligence (AI) is the ability of a machine to learn and display intelligence to solve problems. AI can help to transform big data into clinically actionable insights more efficiently, reduce inevitable errors to improve diagnostic accuracy, and make real-time predictions. AI-based omics analyses will become the next alterative approach to overcome this poor-prognostic disease by discovering biomarkers for early detection, providing molecular/genomic subtyping, offering treatment guidance, and predicting recurrence and survival. Advances in AI may therefore improve PDAC survival outcomes in the near future. The present review mainly focuses on recent advances of AI in PDAC for clinicians. We believe that breakthroughs will soon emerge to fight this deadly disease using AI-navigated precision medicine.
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Affiliation(s)
- Hiromitsu Hayashi
- Department of Gastroenterological Surgery, Graduate School of Life Sciences, Kumamoto University, Kumamoto 860-8556, Japan
| | - Norio Uemura
- Department of Gastroenterological Surgery, Graduate School of Life Sciences, Kumamoto University, Kumamoto 860-8556, Japan
| | - Kazuki Matsumura
- Department of Gastroenterological Surgery, Graduate School of Life Sciences, Kumamoto University, Kumamoto 860-8556, Japan
| | - Liu Zhao
- Department of Gastroenterological Surgery, Graduate School of Life Sciences, Kumamoto University, Kumamoto 860-8556, Japan
| | - Hiroki Sato
- Department of Gastroenterological Surgery, Graduate School of Life Sciences, Kumamoto University, Kumamoto 860-8556, Japan
| | - Yuta Shiraishi
- Department of Gastroenterological Surgery, Graduate School of Life Sciences, Kumamoto University, Kumamoto 860-8556, Japan
| | - Yo-ichi Yamashita
- Department of Gastroenterological Surgery, Graduate School of Life Sciences, Kumamoto University, Kumamoto 860-8556, Japan
| | - Hideo Baba
- Department of Gastroenterological Surgery, Graduate School of Life Sciences, Kumamoto University, Kumamoto 860-8556, Japan
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50
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Ge P, Luo Y, Chen H, Liu J, Guo H, Xu C, Qu J, Zhang G, Chen H. Application of Mass Spectrometry in Pancreatic Cancer Translational Research. Front Oncol 2021; 11:667427. [PMID: 34707986 PMCID: PMC8544753 DOI: 10.3389/fonc.2021.667427] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2021] [Accepted: 05/31/2021] [Indexed: 12/15/2022] Open
Abstract
Pancreatic cancer (PC) is one of the most common malignant tumors in the digestive tract worldwide, with increased morbidity and mortality. In recent years, with the development of surgery, chemotherapy, radiotherapy, targeted therapy, and immunotherapy, and the change of the medical thinking model, remarkable progress has been made in researching comprehensive diagnosis and treatment of PC. However, the present situation of diagnostic and treatment of PC is still unsatisfactory. There is an urgent need for academia to fully integrate the basic research and clinical data from PC to form a research model conducive to clinical translation and promote the proper treatment of PC. This paper summarized the translation progress of mass spectrometry (MS) in the pathogenesis, diagnosis, prognosis, and PC treatment to promote the basic research results of PC into clinical diagnosis and treatment.
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Affiliation(s)
- Peng Ge
- Department of General Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, China.,Institute (College) of Integrative Medicine, Dalian Medical University, Dalian, China.,Laboratory of Integrative Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Yalan Luo
- Department of General Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, China.,Institute (College) of Integrative Medicine, Dalian Medical University, Dalian, China
| | - Haiyang Chen
- Department of General Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, China.,Institute (College) of Integrative Medicine, Dalian Medical University, Dalian, China.,Laboratory of Integrative Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Jiayue Liu
- Department of General Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, China.,Institute (College) of Integrative Medicine, Dalian Medical University, Dalian, China.,Laboratory of Integrative Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Haoya Guo
- Department of General Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, China.,Institute (College) of Integrative Medicine, Dalian Medical University, Dalian, China.,Laboratory of Integrative Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Caiming Xu
- Department of General Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Jialin Qu
- Institute (College) of Integrative Medicine, Dalian Medical University, Dalian, China.,Laboratory of Integrative Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Guixin Zhang
- Department of General Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, China.,Institute (College) of Integrative Medicine, Dalian Medical University, Dalian, China
| | - Hailong Chen
- Department of General Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, China.,Institute (College) of Integrative Medicine, Dalian Medical University, Dalian, China
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