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Vaickus LJ, Kerr DA, Velez Torres JM, Levy J. Artificial Intelligence Applications in Cytopathology: Current State of the Art. Surg Pathol Clin 2024; 17:521-531. [PMID: 39129146 DOI: 10.1016/j.path.2024.04.011] [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] [Indexed: 08/13/2024]
Abstract
The practice of cytopathology has been significantly refined in recent years, largely through the creation of consensus rule sets for the diagnosis of particular specimens (Bethesda, Milan, Paris, and so forth). In general, these diagnostic systems have focused on reducing intraobserver variance, removing nebulous/redundant categories, reducing the use of "atypical" diagnoses, and promoting the use of quantitative scoring systems while providing a uniform language to communicate these results. Computational pathology is a natural offshoot of this process in that it promises 100% reproducible diagnoses rendered by quantitative processes that are free from many of the biases of human practitioners.
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Affiliation(s)
- Louis J Vaickus
- Department of Pathology and Laboratory Medicine, Dartmouth-Hitchcock Medical Center, One Medical Center Drive, Lebanon, NH 03756, USA; Geisel School of Medicine at Dartmouth, Hanover, NH 03750, USA.
| | - Darcy A Kerr
- Department of Pathology and Laboratory Medicine, Dartmouth-Hitchcock Medical Center, One Medical Center Drive, Lebanon, NH 03756, USA; Geisel School of Medicine at Dartmouth, Hanover, NH 03750, USA. https://twitter.com/darcykerrMD
| | - Jaylou M Velez Torres
- Department of Pathology and Laboratory Medicine, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Joshua Levy
- Department of Pathology and Laboratory Medicine, Dartmouth-Hitchcock Medical Center, One Medical Center Drive, Lebanon, NH 03756, USA; Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Los Angeles, CA 90048, USA
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2
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Liu W, Zhang B, Liu T, Jiang J, Liu Y. Artificial Intelligence in Pancreatic Image Analysis: A Review. SENSORS (BASEL, SWITZERLAND) 2024; 24:4749. [PMID: 39066145 PMCID: PMC11280964 DOI: 10.3390/s24144749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2024] [Revised: 07/15/2024] [Accepted: 07/16/2024] [Indexed: 07/28/2024]
Abstract
Pancreatic cancer is a highly lethal disease with a poor prognosis. Its early diagnosis and accurate treatment mainly rely on medical imaging, so accurate medical image analysis is especially vital for pancreatic cancer patients. However, medical image analysis of pancreatic cancer is facing challenges due to ambiguous symptoms, high misdiagnosis rates, and significant financial costs. Artificial intelligence (AI) offers a promising solution by relieving medical personnel's workload, improving clinical decision-making, and reducing patient costs. This study focuses on AI applications such as segmentation, classification, object detection, and prognosis prediction across five types of medical imaging: CT, MRI, EUS, PET, and pathological images, as well as integrating these imaging modalities to boost diagnostic accuracy and treatment efficiency. In addition, this study discusses current hot topics and future directions aimed at overcoming the challenges in AI-enabled automated pancreatic cancer diagnosis algorithms.
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Affiliation(s)
- Weixuan Liu
- Sydney Smart Technology College, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China; (W.L.); (B.Z.)
| | - Bairui Zhang
- Sydney Smart Technology College, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China; (W.L.); (B.Z.)
| | - Tao Liu
- School of Mathematics and Statistics, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China;
| | - Juntao Jiang
- College of Control Science and Engineering, Zhejiang University, Hangzhou 310058, China
| | - Yong Liu
- College of Control Science and Engineering, Zhejiang University, Hangzhou 310058, China
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Ou DX, Lu CW, Chen LW, Lee WY, Hu HW, Chuang JH, Lin MW, Chen KY, Chiu LY, Chen JS, Chen CM, Hsieh MS. Deep Learning Analysis for Predicting Tumor Spread through Air Space in Early-Stage Lung Adenocarcinoma Pathology Images. Cancers (Basel) 2024; 16:2132. [PMID: 38893251 PMCID: PMC11172106 DOI: 10.3390/cancers16112132] [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: 04/19/2024] [Revised: 05/25/2024] [Accepted: 06/01/2024] [Indexed: 06/21/2024] Open
Abstract
The presence of spread through air spaces (STASs) in early-stage lung adenocarcinoma is a significant prognostic factor associated with disease recurrence and poor outcomes. Although current STAS detection methods rely on pathological examinations, the advent of artificial intelligence (AI) offers opportunities for automated histopathological image analysis. This study developed a deep learning (DL) model for STAS prediction and investigated the correlation between the prediction results and patient outcomes. To develop the DL-based STAS prediction model, 1053 digital pathology whole-slide images (WSIs) from the competition dataset were enrolled in the training set, and 227 WSIs from the National Taiwan University Hospital were enrolled for external validation. A YOLOv5-based framework comprising preprocessing, candidate detection, false-positive reduction, and patient-based prediction was proposed for STAS prediction. The model achieved an area under the curve (AUC) of 0.83 in predicting STAS presence, with 72% accuracy, 81% sensitivity, and 63% specificity. Additionally, the DL model demonstrated a prognostic value in disease-free survival compared to that of pathological evaluation. These findings suggest that DL-based STAS prediction could serve as an adjunctive screening tool and facilitate clinical decision-making in patients with early-stage lung adenocarcinoma.
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Affiliation(s)
- De-Xiang Ou
- Department of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, Taipei 10617, Taiwan; (D.-X.O.); (L.-W.C.); (K.-Y.C.)
| | - Chao-Wen Lu
- Division of Thoracic Surgery, Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei 100, Taiwan; (C.-W.L.); (J.-H.C.)
- Graduate Institute of Pathology, National Taiwan University College of Medicine, Taipei 100, Taiwan
| | - Li-Wei Chen
- Department of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, Taipei 10617, Taiwan; (D.-X.O.); (L.-W.C.); (K.-Y.C.)
| | - Wen-Yao Lee
- Division of Thoracic Surgery, Department of Surgery, Fu Jen Catholic University Hospital, No. 69, Guizi Road, Taishan District, New Taipei City 24352, Taiwan;
| | - Hsiang-Wei Hu
- Department of Pathology, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei 100, Taiwan;
| | - Jen-Hao Chuang
- Division of Thoracic Surgery, Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei 100, Taiwan; (C.-W.L.); (J.-H.C.)
| | - Mong-Wei Lin
- Division of Thoracic Surgery, Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei 100, Taiwan; (C.-W.L.); (J.-H.C.)
| | - Kuan-Yu Chen
- Department of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, Taipei 10617, Taiwan; (D.-X.O.); (L.-W.C.); (K.-Y.C.)
| | - Ling-Ying Chiu
- Institute of Medicine, Chung Shan Medical University, Taichung 40201, Taiwan;
| | - Jin-Shing Chen
- Division of Thoracic Surgery, Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei 100, Taiwan; (C.-W.L.); (J.-H.C.)
| | - Chung-Ming Chen
- Department of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, Taipei 10617, Taiwan; (D.-X.O.); (L.-W.C.); (K.-Y.C.)
| | - Min-Shu Hsieh
- Graduate Institute of Pathology, National Taiwan University College of Medicine, Taipei 100, Taiwan
- Department of Pathology, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei 100, Taiwan;
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Codipilly DC, Faghani S, Hagan C, Lewis J, Erickson BJ, Iyer PG. The Evolving Role of Artificial Intelligence in Gastrointestinal Histopathology: An Update. Clin Gastroenterol Hepatol 2024; 22:1170-1180. [PMID: 38154727 DOI: 10.1016/j.cgh.2023.11.044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 11/20/2023] [Accepted: 11/21/2023] [Indexed: 12/30/2023]
Abstract
Significant advances in artificial intelligence (AI) over the past decade potentially may lead to dramatic effects on clinical practice. Digitized histology represents an area ripe for AI implementation. We describe several current needs within the world of gastrointestinal histopathology, and outline, using currently studied models, how AI potentially can address them. We also highlight pitfalls as AI makes inroads into clinical practice.
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Affiliation(s)
- D Chamil Codipilly
- Barrett's Esophagus Unit, Division of Gastroenterology and Hepatology, Mayo Clinic Rochester, Rochester, Minnesota
| | - Shahriar Faghani
- Mayo Artificial Intelligence Laboratory, Department of Radiology, Mayo Clinic, Rochester, Minnesota
| | - Catherine Hagan
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Jason Lewis
- Department of Pathology, Mayo Clinic, Jacksonville, Florida
| | - Bradley J Erickson
- Mayo Artificial Intelligence Laboratory, Department of Radiology, Mayo Clinic, Rochester, Minnesota
| | - Prasad G Iyer
- Barrett's Esophagus Unit, Division of Gastroenterology and Hepatology, Mayo Clinic Rochester, Rochester, Minnesota.
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Ishikawa T, Yamao K, Mizutani Y, Iida T, Kawashima H. Cutting edge of endoscopic ultrasound-guided fine-needle aspiration for solid pancreatic lesions. J Med Ultrason (2001) 2024; 51:209-217. [PMID: 37914883 PMCID: PMC11098899 DOI: 10.1007/s10396-023-01375-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 08/31/2023] [Indexed: 11/03/2023]
Abstract
This article provides an extensive review of the advancements and future perspectives related to endoscopic ultrasound-guided tissue acquisition (EUS-TA) for the diagnosis of solid pancreatic lesions (SPLs). EUS-TA, including fine-needle aspiration (EUS-FNA) and fine-needle biopsy (EUS-FNB), has revolutionized the collection of specimens from intra-abdominal organs, including the pancreas. Improvements in the design of needles, collection methods, and specimen processing techniques have improved the diagnostic performance. This review highlights the latest findings regarding needle evolution, actuation number, sampling methods, specimen evaluation techniques, application of artificial intelligence (AI) for diagnostic purposes, and use of comprehensive genomic profiling (CGP). It acknowledges the rising use of Franseen and fork-tip needles for EUS-FNB and emphasizes that the optimal number of actuations requires further study. Methods such as the door-knocking and fanning techniques have shown promise for increasing diagnostic performance. Macroscopic on-site evaluation (MOSE) is presented as a practical rapid specimen evaluation method, and the integration of AI is identified as a potentially impactful development. The study also underscores the importance of optimal sampling for CGP, which can enhance the precision of cancer treatment. Ongoing research and technological innovations will further improve the accuracy and efficacy of EUS-TA.
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Affiliation(s)
- Takuya Ishikawa
- Department of Gastroenterology and Hepatology, Nagoya University Graduate School of Medicine, 65 Tsurumai-Cho, Showa-Ku, Nagoya, 466-8560, Japan.
| | - Kentaro Yamao
- Department of Endoscopy, Nagoya University Hospital, Nagoya, Japan
| | - Yasuyuki Mizutani
- Department of Gastroenterology and Hepatology, Nagoya University Graduate School of Medicine, 65 Tsurumai-Cho, Showa-Ku, Nagoya, 466-8560, Japan
| | - Tadashi Iida
- Department of Gastroenterology and Hepatology, Nagoya University Graduate School of Medicine, 65 Tsurumai-Cho, Showa-Ku, Nagoya, 466-8560, Japan
| | - Hiroki Kawashima
- Department of Gastroenterology and Hepatology, Nagoya University Graduate School of Medicine, 65 Tsurumai-Cho, Showa-Ku, Nagoya, 466-8560, Japan
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Yan S, Li Y, Pan L, Jiang H, Gong L, Jin F. The application of artificial intelligence for Rapid On-Site Evaluation during flexible bronchoscopy. Front Oncol 2024; 14:1360831. [PMID: 38529376 PMCID: PMC10961380 DOI: 10.3389/fonc.2024.1360831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2023] [Accepted: 02/23/2024] [Indexed: 03/27/2024] Open
Abstract
Background Rapid On-Site Evaluation (ROSE) during flexible bronchoscopy (FB) can improve the adequacy of biopsy specimens and diagnostic yield of lung cancer. However, the lack of cytopathologists has restricted the wide use of ROSE. Objective To develop a ROSE artificial intelligence (AI) system using deep learning techniques to differentiate malignant from benign lesions based on ROSE cytological images, and evaluate the clinical performance of the ROSE AI system. Method 6357 ROSE cytological images from 721 patients who underwent transbronchial biopsy were collected from January to July 2023 at the Tangdu Hospital, Air Force Medical University. A ROSE AI system, composed of a deep convolutional neural network (DCNN), was developed to identify whether there were malignant cells in the ROSE cytological images. Internal testing, external testing, and human-machine competition were used to evaluate the performance of the system. Results The ROSE AI system identified images containing lung malignant cells with the accuracy of 92.97% and 90.26% on the internal testing dataset and external testing dataset respectively, and its performance was comparable to that of the experienced cytopathologist. The ROSE AI system also showed promising performance in diagnosing lung cancer based on ROSE cytological images, with accuracy of 89.61% and 87.59%, and sensitivity of 90.57% and 94.90% on the internal testing dataset and external testing dataset respectively. More specifically, the agreement between the ROSE AI system and the experienced cytopathologist in diagnosing common types of lung cancer, including squamous cell carcinoma, adenocarcinoma, and small cell lung cancer, demonstrated almost perfect consistency in both the internal testing dataset (κ = 0.930 ) and the external testing dataset (κ = 0.932 ). Conclusions The ROSE AI system demonstrated feasibility and robustness in identifying specimen adequacy, showing potential enhancement in the diagnostic yield of FB. Nevertheless, additional enhancements, incorporating a more diverse range of training data and leveraging advanced AI models with increased capabilities, along with rigorous validation through extensive multi-center randomized control assays, are crucial to guarantee the seamless and effective integration of this technology into clinical practice.
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Affiliation(s)
- Shuang Yan
- Department of Pulmonary and Critical Care Medicine, Tangdu Hospital, Air Force Medical University, Xi’an, China
| | | | - Lei Pan
- Department of Pulmonary and Critical Care Medicine, Tangdu Hospital, Air Force Medical University, Xi’an, China
| | - Hua Jiang
- Department of Pulmonary and Critical Care Medicine, Tangdu Hospital, Air Force Medical University, Xi’an, China
| | - Li Gong
- Department of Pathology, Tangdu Hospital, Air Force Medical University, Xi’an, China
| | - Faguang Jin
- Department of Pulmonary and Critical Care Medicine, Tangdu Hospital, Air Force Medical University, Xi’an, China
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Lin O, Alperstein S, Barkan GA, Cuda JM, Kezlarian B, Jhala D, Jin X, Mehrotra S, Monaco SE, Rao J, Saieg M, Thrall M, Pantanowitz L. American Society of Cytopathology Telecytology validation recommendations for rapid on-site evaluation (ROSE). J Am Soc Cytopathol 2024; 13:111-121. [PMID: 38310002 DOI: 10.1016/j.jasc.2023.12.001] [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: 10/13/2023] [Revised: 12/03/2023] [Accepted: 12/06/2023] [Indexed: 02/05/2024]
Abstract
Telecytology has multiple applications, including rapid onsite evaluation (ROSE) of fine-needle aspiration (FNA) specimens. It can enhance cytopathology practice by increasing productivity, reducing costs, and providing subspecialty expertise in areas with limited access to a cytopathologist. However, there are currently no specific validation guidelines to ensure safe practice and compliance with regulations. This initiative, promoted by the American Society of Cytopathology (ASC), intends to propose recommendations for telecytology implementation. These recommendations propose that the validation process should include testing of all hardware and software, both separately and as a whole; training of all individuals who will participate in telecytology with regular competency evaluations; a structured approach using retrospective slides with defined diagnoses for validation and prospective cases for verification and quality assurance. Telecytology processes must be integrated into the laboratory's quality management system and benchmarks for discrepancy rates between preliminary and final diagnoses should be established and monitored. Special attention should be paid to minimize discrepancies that downgrade malignant cases to benign (false positive on telecytology). Currently, billing and reimbursement codes for telecytology are not yet available. Once, they are, recommendation of the appropriate usage of these codes would be a part of the recommendations. These proposed guidelines are intended to be a resource for laboratories that are considering implementing telecytology. These recommendations can help to ensure the safe and effective use of telecytology and maximize its benefits for patients.
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Affiliation(s)
- Oscar Lin
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York.
| | - Susan Alperstein
- Department of Pathology and Laboratory Medicine, New York Presbyterian Hospital, New York, New York
| | - Güliz A Barkan
- Department of Pathology and Laboratory Medicine, Loyola University Medical Center, Maywood, Illinois
| | - Jacqueline M Cuda
- Department of Pathology and Laboratory Services, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
| | - Brie Kezlarian
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Darshana Jhala
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Pittsburgh, Pennsylvania
| | - Xiaobing Jin
- Department of Pathology, University of Michigan, Ann Arbor, Michigan
| | - Swati Mehrotra
- Department of Pathology and Laboratory Medicine, Loyola University Medical Center, Maywood, Illinois
| | - Sara E Monaco
- Department of Pathology, Geisinger Medical Center, Danville, Pennsylvania
| | - Jianyu Rao
- Department of Pathology and Laboratory, UCLA Health, Los Angeles, California
| | - Mauro Saieg
- Department of Pathology, Santa Casa Medical School, Sao Paulo, Brazil
| | - Michael Thrall
- Department of Pathology, Houston Methodist Hospital, Houston, Texas
| | - Liron Pantanowitz
- Department of Pathology and Laboratory Services, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
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Tomimatsu T, Yamashita K, Sakata T, Kamezaki R, Ikeda R, Shiraishi S, Uchiyama Y, Ito S. Development of an automated region-of-interest-setting method based on a deep neural network for brain perfusion single photon emission computed tomography quantification methods. ASIA OCEANIA JOURNAL OF NUCLEAR MEDICINE & BIOLOGY 2024; 12:120-130. [PMID: 39050240 PMCID: PMC11263778 DOI: 10.22038/aojnmb.2024.75375.1528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 11/29/2023] [Accepted: 01/20/2024] [Indexed: 07/27/2024]
Abstract
Objectives A simple noninvasive microsphere (SIMS) method using 123I-IMP and an improved brain uptake ratio (IBUR) method using 99mTc-ECD for the quantitative measurement of regional cerebral blood flow have been recently reported. The input functions of these methods were determined using the administered dose, which was obtained by analyzing the time activity curve of the pulmonary artery (PA) for SIMS and the ascending aorta (AAo) for the IBUR methods for dynamic chest images. If the PA and AAo regions of interest (ROIs) can be determined using deep convolutional neural networks (DCNN) for segmentation, the accuracy of these ROI-setting methods can be improved through simple analytical operations to ensure repeatability and reproducibility. The purpose of this study was to develop new PA and AAo-ROI setting methods using a DCNN (DCNN-ROI method). Methods A U-Net architecture based on convolutional neural networks was used to determine the PA and AAo candidate regions. Images of 290 patients who underwent 123I-IMP RI-angiography and 108 patients who underwent 99mTc-ECD RI-angiography were used. The PA and AAo-ROI results for the DCNN-ROI method were compared to those obtained using manual methods. The counts for the input function on the PA and AAo-ROI were determined by integrating the area under the curve (AUC) counts of the time-activity curve of PA and AAo-ROI, respectively. The effectiveness of the DCNN-ROI method was elucidated through a comparison with the integrated AUC counts of the DCNN-ROI and the manual ROI. Results The coincidence ratio for the locations of the PA and AAo-ROI obtained using the DCNN method and that for the manual method was 100%. Strong correlations were observed between the AUC counts using the DCNN and manual methods. Conclusion New ROI- setting programs were developed using a deep convolution neural network DCNN to determine the input functions for the SIMS and IBUR methods. The accuracy of these methods is comparable to that of the manual method.
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Affiliation(s)
- Taeko Tomimatsu
- Graduate School of Health Sciences, Kumamoto University, Japan
| | | | - Takumi Sakata
- Graduate School of Health Sciences, Kumamoto University, Japan
| | - Ryosuke Kamezaki
- Department of Central Radiology Kumamoto University Hospital, Japan
| | - Ryuji Ikeda
- Department of Central Radiology Kumamoto University Hospital, Japan
| | - Shinya Shiraishi
- Department of Diagnostic Radiology, Faculty of Life Sciences, Kumamoto University, Japan
| | - Yoshikazu Uchiyama
- Department of Information and Communication Technology, Faculty of Engineering, University of Miyazaki, Japan
| | - Shigeki Ito
- Department of Medical Radiation Sciences, Faculty of Life Science, Kumamoto University, Japan
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Dhali A, Kipkorir V, Srichawla BS, Kumar H, Rathna RB, Ongidi I, Chaudhry T, Morara G, Nurani K, Cheruto D, Biswas J, Chieng LR, Dhali GK. Artificial intelligence assisted endoscopic ultrasound for detection of pancreatic space-occupying lesion: a systematic review and meta-analysis. Int J Surg 2023; 109:4298-4308. [PMID: 37800594 PMCID: PMC10720860 DOI: 10.1097/js9.0000000000000717] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 08/21/2023] [Indexed: 10/07/2023]
Abstract
BACKGROUND Diagnosing pancreatic lesions, including chronic pancreatitis, autoimmune pancreatitis, and pancreatic cancer, poses a challenge and, as a result, is time-consuming. To tackle this issue, artificial intelligence (AI) has been increasingly utilized over the years. AI can analyze large data sets with heightened accuracy, reduce interobserver variability, and can standardize the interpretation of radiologic and histopathologic lesions. Therefore, this study aims to review the use of AI in the detection and differentiation of pancreatic space-occupying lesions and to compare AI-assisted endoscopic ultrasound (EUS) with conventional EUS in terms of their detection capabilities. METHODS Literature searches were conducted through PubMed/Medline, SCOPUS, and Embase to identify studies eligible for inclusion. Original articles, including observational studies, randomized control trials, systematic reviews, meta-analyses, and case series specifically focused on AI-assisted EUS in adults, were included. Data were extracted and pooled, and a meta-analysis was conducted using Meta-xl. For results exhibiting significant heterogeneity, a random-effects model was employed; otherwise, a fixed-effects model was utilized. RESULTS A total of 21 studies were included in the review with four studies pooled for a meta-analysis. A pooled accuracy of 93.6% (CI 90.4-96.8%) was found using the random-effects model on four studies that showed significant heterogeneity ( P <0.05) in the Cochrane's Q test. Further, a pooled sensitivity of 93.9% (CI 92.4-95.3%) was found using a fixed-effects model on seven studies that showed no significant heterogeneity in the Cochrane's Q test. When it came to pooled specificity, a fixed-effects model was utilized in six studies that showed no significant heterogeneity in the Cochrane's Q test and determined as 93.1% (CI 90.7-95.4%). The pooled positive predictive value which was done using the random-effects model on six studies that showed significant heterogeneity was 91.6% (CI 87.3-95.8%). The pooled negative predictive value which was done using the random-effects model on six studies that showed significant heterogeneity was 93.6% (CI 90.4-96.8%). CONCLUSION AI-assisted EUS shows a high degree of accuracy in the detection and differentiation of pancreatic space-occupying lesions over conventional EUS. Its application may promote prompt and accurate diagnosis of pancreatic pathologies.
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Affiliation(s)
- Arkadeep Dhali
- NIHR Academic Clinical Fellow in Gastroenterology, University of Sheffield; Internal Medicine Trainee, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Vincent Kipkorir
- School of Medicine, Faculty of Health Sciences, University of Nairobi, Nairobi, Kenya
| | | | | | | | - Ibsen Ongidi
- School of Medicine, Faculty of Health Sciences, University of Nairobi, Nairobi, Kenya
| | - Talha Chaudhry
- School of Medicine, Faculty of Health Sciences, University of Nairobi, Nairobi, Kenya
| | - Gisore Morara
- School of Medicine, Faculty of Health Sciences, University of Nairobi, Nairobi, Kenya
| | - Khulud Nurani
- School of Medicine, Faculty of Health Sciences, University of Nairobi, Nairobi, Kenya
| | - Doreen Cheruto
- School of Medicine, Faculty of Health Sciences, University of Nairobi, Nairobi, Kenya
| | | | - Leonard R. Chieng
- NIHR Academic Clinical Fellow in Gastroenterology, University of Sheffield; Internal Medicine Trainee, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Gopal Krishna Dhali
- School of Digestive and Liver Diseases, Institute of Postgraduate Medical Education and Research, Kolkata, India
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Wu HL, Yao LW, Shi HY, Wu LL, Li X, Zhang CX, Chen BR, Zhang J, Tan W, Cui N, Zhou W, Zhang JX, Xiao B, Gong RR, Ding Z, Yu HG. Validation of a real-time biliopancreatic endoscopic ultrasonography analytical device in China: a prospective, single-centre, randomised, controlled trial. Lancet Digit Health 2023; 5:e812-e820. [PMID: 37775472 DOI: 10.1016/s2589-7500(23)00160-7] [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: 04/03/2023] [Revised: 06/16/2023] [Accepted: 08/09/2023] [Indexed: 10/01/2023]
Abstract
BACKGROUND Endoscopic ultrasonography (EUS) is a key procedure for the diagnosis of biliopancreatic diseases. However, the performance among EUS endoscopists varies greatly and leads to blind spots during the operation, which can impair the health outcomes of patients. We previously developed an artificial intelligence (AI) device that accurately identified EUS standard stations and significantly reduced the difficulty of ultrasonography image interpretation. In this study, we updated the device (named EUS-IREAD) and validated its performance in improving the quality of EUS procedures. METHODS In this single-centre, randomised, controlled trial, we updated EUS-IREAD so it consisted of five learning models to identify eight EUS stations and 24 anatomical structures. The trial was done at the Renmin Hospital of Wuhan University (Wuhan, China) and included patients aged 18 years or older with suspected biliopancreatic (pancreas and biliopancreatic duct) lesions due to clinical symptoms, radiological findings, or laboratory findings, and with a high risk of pancreatic cancer. Patients were randomly assigned (1:1) by a dedicated research assistant using a computer-generated random number series (with a block size of four) to undergo the EUS procedure with or without the assistance of EUS-IREAD. Endoscopists in the EUS-IREAD-assisted group were required to observe all standard stations and anatomical structures according to the prompts by the AI device. Data collectors, the independent data anaylsis team, and patients were masked to group allocation. The primary outcome was the missed scanning rate of standard stations between the two groups, which was assessed in patients who underwent EUS procedure in accordance with the assigned intervention (per protocol). This trial is registered with ClinicalTrials.gov, NCT05457101. FINDINGS Between July 9, 2022, and Feb 28, 2023, 290 patients (mean age 55·93 years [SD 14·06], 152 [52%] male, and 138 [48%] female) were randomly assigned and analysed, including 144 in the EUS-IREAD-assisted group and 146 in the control group. The EUS-IREAD-assisted group had a lower missed scanning rate of stations than the control group (4·5% [SD 0·8] vs 14·3% [1·0], -9·8% [95% CI -12·2 to -7·5]; odds ratio 3·6 [95% Cl 2·6 to 4·9]; p<0·0001). No significant adverse event was found during the study. INTERPRETATION Our study confirms the capability of EUS-IREAD to monitor the blind spots and reduce the missed rate of stations and structures during EUS procedures. The EUS-IREAD has the potential to play an essential part in EUS quality control. FUNDING Innovation Team Project of Health Commission of Hubei Province and College-enterprise Deepening Reform Project of Wuhan University.
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Affiliation(s)
- Hui Ling Wu
- Department of Gastroenterology, Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, and Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Li Wen Yao
- Department of Gastroenterology, Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, and Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Hui Ying Shi
- Department of Gastroenterology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Lian Lian Wu
- Department of Gastroenterology, Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, and Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Xun Li
- Department of Gastroenterology, Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, and Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Chen Xia Zhang
- Department of Gastroenterology, Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, and Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Bo Ru Chen
- Department of Gastroenterology, Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, and Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Jun Zhang
- Department of Gastroenterology, Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, and Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Wei Tan
- Department of Gastroenterology, Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, and Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Ning Cui
- Department of Gastroenterology, Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, and Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Wei Zhou
- Department of Gastroenterology, Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, and Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Ji Xiang Zhang
- Department of Gastroenterology, Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, and Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Bing Xiao
- Department of Gastroenterology, Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, and Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Rong Rong Gong
- Department of Gastroenterology, Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, and Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Zhen Ding
- Endoscopy Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China.
| | - Hong Gang Yu
- Department of Gastroenterology, Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, and Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China.
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11
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Sohn A, Miller D, Ribeiro E, Shankar N, Ali S, Hruban R, Baras A. A deep learning model to triage and predict adenocarcinoma on pancreas cytology whole slide imaging. Sci Rep 2023; 13:16517. [PMID: 37783684 PMCID: PMC10545767 DOI: 10.1038/s41598-023-42045-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: 05/17/2023] [Accepted: 09/04/2023] [Indexed: 10/04/2023] Open
Abstract
Pancreatic fine-needle aspirations are the gold-standard diagnostic procedure for the evaluation of pancreatic ductal adenocarcinoma. A suspicion for malignancy can escalate towards chemotherapy followed by a major surgery and therefore is a high-stakes task for the pathologist. In this paper, we propose a deep learning framework, MIPCL, that can serve as a helpful screening tool, predicting the presence or absence of cancer. We also reproduce two deep learning models that have found success in surgical pathology for our cytopathology study. Our MIPCL significantly improves over both models across all evaluated metrics (F1-Score: 87.97% vs 88.70% vs 91.07%; AUROC: 0.9159 vs. 0.9051 vs 0.9435). Additionally, our model is able to recover the most contributing regions on the slide for the final prediction. We also present a dataset curation strategy that increases the number of training examples from an existing dataset, thereby reducing the resource burden tied to collecting and scanning additional cases.
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Affiliation(s)
- Andrew Sohn
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Daniel Miller
- Department of Pathology, Saint Louis University School of Medicine, St. Louis, USA
| | - Efrain Ribeiro
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Nakul Shankar
- Department of Pathology, University of Colorado, Boulder, USA
| | - Syed Ali
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Ralph Hruban
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Alexander Baras
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
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12
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Qin X, Ran T, Chen Y, Zhang Y, Wang D, Zhou C, Zou D. Artificial Intelligence in Endoscopic Ultrasonography-Guided Fine-Needle Aspiration/Biopsy (EUS-FNA/B) for Solid Pancreatic Lesions: Opportunities and Challenges. Diagnostics (Basel) 2023; 13:3054. [PMID: 37835797 PMCID: PMC10572518 DOI: 10.3390/diagnostics13193054] [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: 08/01/2023] [Revised: 09/06/2023] [Accepted: 09/06/2023] [Indexed: 10/15/2023] Open
Abstract
Solid pancreatic lesions (SPLs) encompass a variety of benign and malignant diseases and accurate diagnosis is crucial for guiding appropriate treatment decisions. Endoscopic ultrasonography-guided fine-needle aspiration/biopsy (EUS-FNA/B) serves as a front-line diagnostic tool for pancreatic mass lesions and is widely used in clinical practice. Artificial intelligence (AI) is a mathematical technique that automates the learning and recognition of data patterns. Its strong self-learning ability and unbiased nature have led to its gradual adoption in the medical field. In this paper, we describe the fundamentals of AI and provide a summary of reports on AI in EUS-FNA/B to help endoscopists understand and realize its potential in improving pathological diagnosis and guiding targeted EUS-FNA/B. However, AI models have limitations and shortages that need to be addressed before clinical use. Furthermore, as most AI studies are retrospective, large-scale prospective clinical trials are necessary to evaluate their clinical usefulness accurately. Although AI in EUS-FNA/B is still in its infancy, the constant input of clinical data and the advancements in computer technology are expected to make computer-aided diagnosis and treatment more feasible.
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Affiliation(s)
| | | | | | | | | | - Chunhua Zhou
- Department of Gastroenterology, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200025, China; (X.Q.); (T.R.); (Y.C.); (Y.Z.); (D.W.)
| | - Duowu Zou
- Department of Gastroenterology, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200025, China; (X.Q.); (T.R.); (Y.C.); (Y.Z.); (D.W.)
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13
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Li M, Tang F, Lao J, Yang Y, Cao J, Song R, Wu P, Wang Y. Multicomponent prediction of 2-year mortality and amputation in patients with diabetic foot using a random survival forest model: Uric acid, alanine transaminase, urine protein and platelet as important predictors. Int Wound J 2023; 21:e14376. [PMID: 37743574 PMCID: PMC10824700 DOI: 10.1111/iwj.14376] [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: 07/20/2023] [Accepted: 08/24/2023] [Indexed: 09/26/2023] Open
Abstract
The current methods for the prediction of mortality and amputation for inpatients with diabetic foot (DF) use only conventional, simple variables, which limits their performance. Here, we used a random survival forest (RSF) model and multicomponent variables to improve the prediction of mortality and amputation for these patients. We performed a retrospective cohort study of 175 inpatients with DF who were recruited between 2014 and 2021. Thirty-one predictors in six categories were considered as potential covariates. Seventy percent (n = 122) of the participants were randomly selected to constitute a training set, and 30% (n = 53) were assigned to a testing set. The RSF model was used to screen appropriate variables for their value as predictors of 2-year all-cause mortality and amputation, and a multicomponent prediction model was established. Model performance was evaluated using the area under the curve (AUC) and the Hosmer-Lemeshow test. The AUCs were compared using the Delong test. Seventeen variables were selected to predict mortality and 23 were selected to predict amputation. Uric acid and alanine transaminase were the top two most useful variables for the prediction of mortality, whereas urine protein and platelet were the top variables for the prediction of amputation. The AUCs were 0.913 and 0.851 for the prediction of mortality for the training and testing sets, respectively; and the equivalent AUCs were 0.963 and 0.893 for the prediction of amputation. There were no significant differences between the AUCs for the training and testing sets for both the mortality and amputation models. These models showed a good degree of fit. Thus, the RSF model can predict mortality and amputation in inpatients with DF. This multicomponent prediction model could help clinicians consider predictors of different dimensions to effectively prevent DF from clinical outcomes .
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Affiliation(s)
- Mingzhuo Li
- Department of Plastic SurgeryThe First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan HospitalJinanChina
- Center for Big Data Research in Health and MedicineThe First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan HospitalJinanChina
- Jinan Clinical Research Center for Tissue Engineering Skin Regeneration and Wound RepairJinanChina
- Shandong Data Open Innovative Application LaboratoryJinanChina
| | - Fang Tang
- Center for Big Data Research in Health and MedicineThe First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan HospitalJinanChina
- Shandong Data Open Innovative Application LaboratoryJinanChina
| | - Jiahui Lao
- Center for Big Data Research in Health and MedicineThe First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan HospitalJinanChina
- Shandong Data Open Innovative Application LaboratoryJinanChina
| | - Yang Yang
- Center for Big Data Research in Health and MedicineThe First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan HospitalJinanChina
- Shandong Data Open Innovative Application LaboratoryJinanChina
| | - Jia Cao
- Center for Big Data Research in Health and MedicineThe First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan HospitalJinanChina
- Shandong Data Open Innovative Application LaboratoryJinanChina
| | - Ru Song
- Department of Plastic SurgeryThe First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan HospitalJinanChina
- Jinan Clinical Research Center for Tissue Engineering Skin Regeneration and Wound RepairJinanChina
| | - Peng Wu
- Department of Plastic SurgeryThe First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan HospitalJinanChina
- Jinan Clinical Research Center for Tissue Engineering Skin Regeneration and Wound RepairJinanChina
| | - Yibing Wang
- Department of Plastic SurgeryThe First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan HospitalJinanChina
- Center for Big Data Research in Health and MedicineThe First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan HospitalJinanChina
- Jinan Clinical Research Center for Tissue Engineering Skin Regeneration and Wound RepairJinanChina
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14
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Huang J, Fan X, Liu W. Applications and Prospects of Artificial Intelligence-Assisted Endoscopic Ultrasound in Digestive System Diseases. Diagnostics (Basel) 2023; 13:2815. [PMID: 37685350 PMCID: PMC10487217 DOI: 10.3390/diagnostics13172815] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 08/22/2023] [Accepted: 08/27/2023] [Indexed: 09/10/2023] Open
Abstract
Endoscopic ultrasound (EUS) has emerged as a widely utilized tool in the diagnosis of digestive diseases. In recent years, the potential of artificial intelligence (AI) in healthcare has been gradually recognized, and its superiority in the field of EUS is becoming apparent. Machine learning (ML) and deep learning (DL) are the two main AI algorithms. This paper aims to outline the applications and prospects of artificial intelligence-assisted endoscopic ultrasound (EUS-AI) in digestive diseases over the past decade. The results demonstrated that EUS-AI has shown superiority or at least equivalence to traditional methods in the diagnosis, prognosis, and quality control of subepithelial lesions, early esophageal cancer, early gastric cancer, and pancreatic diseases including pancreatic cystic lesions, autoimmune pancreatitis, and pancreatic cancer. The implementation of EUS-AI has opened up new avenues for individualized precision medicine and has introduced novel diagnostic and treatment approaches for digestive diseases.
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Affiliation(s)
| | | | - Wentian Liu
- Department of Gastroenterology and Hepatology, Tianjin Medical University General Hospital, No. 154, Anshan Road, Heping District, Tianjin 300052, China; (J.H.); (X.F.)
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15
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Tovar DR, Rosenthal MH, Maitra A, Koay EJ. Potential of artificial intelligence in the risk stratification for and early detection of pancreatic cancer. ARTIFICIAL INTELLIGENCE SURGERY 2023; 3:14-26. [PMID: 37124705 PMCID: PMC10141523 DOI: 10.20517/ais.2022.38] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is the third most lethal cancer in the United States, with a 5-year life expectancy of 11%. Most symptoms manifest at an advanced stage of the disease when surgery is no longer appropriate. The dire prognosis of PDAC warrants new strategies to improve the outcomes of patients, and early detection has garnered significant attention. However, early detection of PDAC is most often incidental, emphasizing the importance of developing new early detection screening strategies. Due to the low incidence of the disease in the general population, much of the focus for screening has turned to individuals at high risk of PDAC. This enriches the screening population and balances the risks associated with pancreas interventions. The cancers that are found in these high-risk individuals by MRI and/or EUS screening show favorable 73% 5-year overall survival. Even with the emphasis on screening in enriched high-risk populations, only a minority of incident cancers are detected this way. One strategy to improve early detection outcomes is to integrate artificial intelligence (AI) into biomarker discovery and risk models. This expert review summarizes recent publications that have developed AI algorithms for the applications of risk stratification of PDAC using radiomics and electronic health records. Furthermore, this review illustrates the current uses of radiomics and biomarkers in AI for early detection of PDAC. Finally, various challenges and potential solutions are highlighted regarding the use of AI in medicine for early detection purposes.
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Affiliation(s)
- Daniela R. Tovar
- Department of Gastrointestinal Radiation Oncology, The University of Texas, Anderson Cancer Center, Houston, TX 77030, USA
| | | | - Anirban Maitra
- Department of Radiology, The University of Texas, Anderson Cancer Center, Houston, TX 77030, USA
| | - Eugene J. Koay
- Department of Gastrointestinal Radiation Oncology, The University of Texas, Anderson Cancer Center, Houston, TX 77030, USA
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16
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HUANG CONGGAI, LUO XING, WANG SHAOHUA, WAN YU, WANG JIEQIONG, TANG XIAOQIN, SCHATZ CHRISTOPH, ZHANG HUILING, HAYBAECK JOHANNES, YANG ZHIHUI. Minimally Invasive Cytopathology and Accurate Diagnosis: Technical Procedures and Ancillary Techniques. In Vivo 2023; 37:11-21. [PMID: 36593030 PMCID: PMC9843757 DOI: 10.21873/invivo.13050] [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: 11/08/2022] [Revised: 12/15/2022] [Accepted: 12/16/2022] [Indexed: 01/03/2023]
Abstract
In recent years, the demand for cytopathological accurate diagnoses has increased as expanding minimally invasive procedures obtain materials from patients with advanced cancer for diagnostic, prognostic, and predictive purposes. However, inadequate knowledge of cytopathological technical procedures and ancillary techniques by clinicians remains the most common reason for the limited availability of cytopathology. The objectives of this review were to understand the technical procedures, ancillary techniques, and application and effectiveness of various types of tests in cytopathology. Each of the many ancillary technologies described in the literature has specific advantages and limitations and laboratories select one or more methods depending on their infrastructure and expertise to achieve the goal from initial screening of the disease to the final diagnosis of the cytopathology. This paper systematically reviews the development of cytopathology, summarizes the existing problems in cytopathology and the new progress of auxiliary examination, to provide a theoretical basis for the advanced development of cytopathological diagnostic technologies and to consolidate the minimally invasive and accurate diagnosis of cytopathologies for clinicians. Cytopathology offers many advantages over other clinical examinations, particularly for minimally invasive and accurate diagnosis.
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Affiliation(s)
- CONGGAI HUANG
- Department of Pathology, The Affiliated Hospital of Southwest Medical University, Luzhou, P.R. China
| | - XING LUO
- Department of Pathology, The Affiliated Hospital of Southwest Medical University, Luzhou, P.R. China
| | - SHAOHUA WANG
- Department of Pathology, The Affiliated Hospital of Southwest Medical University, Luzhou, P.R. China
| | - YU WAN
- Department of Pathology, The Affiliated Hospital of Southwest Medical University, Luzhou, P.R. China
| | - JIEQIONG WANG
- Department of Pathology, The Affiliated Hospital of Southwest Medical University, Luzhou, P.R. China
| | - XIAOQIN TANG
- Department of Pathology, The Affiliated Hospital of Southwest Medical University, Luzhou, P.R. China
| | - CHRISTOPH SCHATZ
- Institute of Pathology, Neuropathology and Molecular Pathology, Medical University of Innsbruck, Innsbruck, Austria
| | - HUILING ZHANG
- Department of Pathology, The Affiliated Hospital of Southwest Medical University, Luzhou, P.R. China
| | - JOHANNES HAYBAECK
- Institute of Pathology, Neuropathology and Molecular Pathology, Medical University of Innsbruck, Innsbruck, Austria,Diagnostic and Research Institute of Pathology, Medical University of Graz, Graz, Austria
| | - ZHIHUI YANG
- Department of Pathology, The Affiliated Hospital of Southwest Medical University, Luzhou, P.R. China
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