High performance in risk stratification of intraductal papillary mucinous neoplasms by confocal laser endomicroscopy image analysis with convolutional neural networks (with video)
EUS-guided needle-based confocal laser endomicroscopy (EUS-nCLE) can differentiate high-grade dysplasia/adenocarcinoma (HGD-Ca) in intraductal papillary mucinous neoplasms (IPMNs) but requires manual interpretation. We sought to derive predictive computer-aided diagnosis (CAD) and artificial intelli...
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| Published in | Gastrointestinal endoscopy Vol. 94; no. 1; pp. 78 - 87.e2 |
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| Main Authors | , , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Elsevier Inc
01.07.2021
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0016-5107 1097-6779 1097-6779 |
| DOI | 10.1016/j.gie.2020.12.054 |
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| Abstract | EUS-guided needle-based confocal laser endomicroscopy (EUS-nCLE) can differentiate high-grade dysplasia/adenocarcinoma (HGD-Ca) in intraductal papillary mucinous neoplasms (IPMNs) but requires manual interpretation. We sought to derive predictive computer-aided diagnosis (CAD) and artificial intelligence (AI) algorithms to facilitate accurate diagnosis and risk stratification of IPMNs.
A post hoc analysis of a single-center prospective study evaluating EUS-nCLE (2015-2019; INDEX study) was conducted using 15,027 video frames from 35 consecutive patients with histopathologically proven IPMNs (18 with HGD-Ca). We designed 2 CAD-convolutional neural network (CNN) algorithms: (1) a guided segmentation-based model (SBM), where the CNN-AI system was trained to detect and measure papillary epithelial thickness and darkness (indicative of cellular and nuclear stratification), and (2) a reasonably agnostic holistic-based model (HBM) where the CNN-AI system automatically extracted nCLE features for risk stratification. For the detection of HGD-Ca in IPMNs, the diagnostic performance of the CNN-CAD algorithms was compared with that of the American Gastroenterological Association (AGA) and revised Fukuoka guidelines.
Compared with the guidelines, both n-CLE-guided CNN-CAD algorithms yielded higher sensitivity (HBM, 83.3%; SBM, 83.3%; AGA, 55.6%; Fukuoka, 55.6%) and accuracy (SBM, 82.9%; HBM, 85.7%; AGA, 68.6%; Fukuoka, 74.3%) for diagnosing HGD-Ca, with comparable specificity (SBM, 82.4%; HBM, 88.2%; AGA, 82.4%; Fukuoka, 94.1%). Both CNN-CAD algorithms, the guided (SBM) and agnostic (HBM) models, were comparable in risk stratifying IPMNs.
EUS-nCLE-based CNN-CAD algorithms can accurately risk stratify IPMNs. Future multicenter validation studies and AI model improvements could enhance the accuracy and fully automatize the process for real-time interpretation. |
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| AbstractList | EUS-guided needle-based confocal laser endomicroscopy (EUS-nCLE) can differentiate high-grade dysplasia/adenocarcinoma (HGD-Ca) in intraductal papillary mucinous neoplasms (IPMNs) but requires manual interpretation. We sought to derive predictive computer-aided diagnosis (CAD) and artificial intelligence (AI) algorithms to facilitate accurate diagnosis and risk stratification of IPMNs.
A post hoc analysis of a single-center prospective study evaluating EUS-nCLE (2015-2019; INDEX study) was conducted using 15,027 video frames from 35 consecutive patients with histopathologically proven IPMNs (18 with HGD-Ca). We designed 2 CAD-convolutional neural network (CNN) algorithms: (1) a guided segmentation-based model (SBM), where the CNN-AI system was trained to detect and measure papillary epithelial thickness and darkness (indicative of cellular and nuclear stratification), and (2) a reasonably agnostic holistic-based model (HBM) where the CNN-AI system automatically extracted nCLE features for risk stratification. For the detection of HGD-Ca in IPMNs, the diagnostic performance of the CNN-CAD algorithms was compared with that of the American Gastroenterological Association (AGA) and revised Fukuoka guidelines.
Compared with the guidelines, both n-CLE-guided CNN-CAD algorithms yielded higher sensitivity (HBM, 83.3%; SBM, 83.3%; AGA, 55.6%; Fukuoka, 55.6%) and accuracy (SBM, 82.9%; HBM, 85.7%; AGA, 68.6%; Fukuoka, 74.3%) for diagnosing HGD-Ca, with comparable specificity (SBM, 82.4%; HBM, 88.2%; AGA, 82.4%; Fukuoka, 94.1%). Both CNN-CAD algorithms, the guided (SBM) and agnostic (HBM) models, were comparable in risk stratifying IPMNs.
EUS-nCLE-based CNN-CAD algorithms can accurately risk stratify IPMNs. Future multicenter validation studies and AI model improvements could enhance the accuracy and fully automatize the process for real-time interpretation. EUS-guided needle-based confocal laser endomicroscopy (EUS-nCLE) can differentiate high-grade dysplasia/adenocarcinoma (HGD-Ca) in intraductal papillary mucinous neoplasms (IPMNs) but requires manual interpretation. We sought to derive predictive computer-aided diagnosis (CAD) and artificial intelligence (AI) algorithms to facilitate accurate diagnosis and risk stratification of IPMNs.BACKGROUND AND AIMSEUS-guided needle-based confocal laser endomicroscopy (EUS-nCLE) can differentiate high-grade dysplasia/adenocarcinoma (HGD-Ca) in intraductal papillary mucinous neoplasms (IPMNs) but requires manual interpretation. We sought to derive predictive computer-aided diagnosis (CAD) and artificial intelligence (AI) algorithms to facilitate accurate diagnosis and risk stratification of IPMNs.A post hoc analysis of a single-center prospective study evaluating EUS-nCLE (2015-2019; INDEX study) was conducted using 15,027 video frames from 35 consecutive patients with histopathologically proven IPMNs (18 with HGD-Ca). We designed 2 CAD-convolutional neural network (CNN) algorithms: (1) a guided segmentation-based model (SBM), where the CNN-AI system was trained to detect and measure papillary epithelial thickness and darkness (indicative of cellular and nuclear stratification), and (2) a reasonably agnostic holistic-based model (HBM) where the CNN-AI system automatically extracted nCLE features for risk stratification. For the detection of HGD-Ca in IPMNs, the diagnostic performance of the CNN-CAD algorithms was compared with that of the American Gastroenterological Association (AGA) and revised Fukuoka guidelines.METHODSA post hoc analysis of a single-center prospective study evaluating EUS-nCLE (2015-2019; INDEX study) was conducted using 15,027 video frames from 35 consecutive patients with histopathologically proven IPMNs (18 with HGD-Ca). We designed 2 CAD-convolutional neural network (CNN) algorithms: (1) a guided segmentation-based model (SBM), where the CNN-AI system was trained to detect and measure papillary epithelial thickness and darkness (indicative of cellular and nuclear stratification), and (2) a reasonably agnostic holistic-based model (HBM) where the CNN-AI system automatically extracted nCLE features for risk stratification. For the detection of HGD-Ca in IPMNs, the diagnostic performance of the CNN-CAD algorithms was compared with that of the American Gastroenterological Association (AGA) and revised Fukuoka guidelines.Compared with the guidelines, both n-CLE-guided CNN-CAD algorithms yielded higher sensitivity (HBM, 83.3%; SBM, 83.3%; AGA, 55.6%; Fukuoka, 55.6%) and accuracy (SBM, 82.9%; HBM, 85.7%; AGA, 68.6%; Fukuoka, 74.3%) for diagnosing HGD-Ca, with comparable specificity (SBM, 82.4%; HBM, 88.2%; AGA, 82.4%; Fukuoka, 94.1%). Both CNN-CAD algorithms, the guided (SBM) and agnostic (HBM) models, were comparable in risk stratifying IPMNs.RESULTSCompared with the guidelines, both n-CLE-guided CNN-CAD algorithms yielded higher sensitivity (HBM, 83.3%; SBM, 83.3%; AGA, 55.6%; Fukuoka, 55.6%) and accuracy (SBM, 82.9%; HBM, 85.7%; AGA, 68.6%; Fukuoka, 74.3%) for diagnosing HGD-Ca, with comparable specificity (SBM, 82.4%; HBM, 88.2%; AGA, 82.4%; Fukuoka, 94.1%). Both CNN-CAD algorithms, the guided (SBM) and agnostic (HBM) models, were comparable in risk stratifying IPMNs.EUS-nCLE-based CNN-CAD algorithms can accurately risk stratify IPMNs. Future multicenter validation studies and AI model improvements could enhance the accuracy and fully automatize the process for real-time interpretation.CONCLUSIONEUS-nCLE-based CNN-CAD algorithms can accurately risk stratify IPMNs. Future multicenter validation studies and AI model improvements could enhance the accuracy and fully automatize the process for real-time interpretation. |
| Author | Jajeh, Muhammed O. Cruz-Monserrate, Zobeida Machicado, Jorge D. Papachristou, Georgios I. Vishwanath, Aadit B. Alexander, Victoria L. Dubay, Kelly Middendorf, Dana M. Conwell, Darwin L. Maloof, Tassiana G. Pan, Tai-Yu Porter, Kyle Carlyn, David E. Hart, Phil A. Poland, Sarah Ueltschi, Olivia Krishna, Somashekar G. Chao, Wei-Lun |
| Author_xml | – sequence: 1 givenname: Jorge D. surname: Machicado fullname: Machicado, Jorge D. organization: Division of Gastroenterology and Hepatology, Mayo Clinic Health System, Eau Claire, Wisconsin, USA – sequence: 2 givenname: Wei-Lun surname: Chao fullname: Chao, Wei-Lun organization: Department of Computer Science and Engineering, College of Engineering, The Ohio State University, Columbus, Ohio, USA – sequence: 3 givenname: David E. surname: Carlyn fullname: Carlyn, David E. organization: Department of Computer Science and Engineering, College of Engineering, The Ohio State University, Columbus, Ohio, USA – sequence: 4 givenname: Tai-Yu surname: Pan fullname: Pan, Tai-Yu organization: Department of Computer Science and Engineering, College of Engineering, The Ohio State University, Columbus, Ohio, USA – sequence: 5 givenname: Sarah surname: Poland fullname: Poland, Sarah organization: The Ohio State University College of Medicine, Columbus, Ohio, USA – sequence: 6 givenname: Victoria L. surname: Alexander fullname: Alexander, Victoria L. organization: The Ohio State University College of Medicine, Columbus, Ohio, USA – sequence: 7 givenname: Tassiana G. surname: Maloof fullname: Maloof, Tassiana G. organization: The Ohio State University College of Medicine, Columbus, Ohio, USA – sequence: 8 givenname: Kelly surname: Dubay fullname: Dubay, Kelly organization: The Comprehensive Cancer Center–Arthur G. James Cancer Hospital and Richard J. Solove Research Institute, The Ohio State University, Columbus, Ohio, USA – sequence: 9 givenname: Olivia surname: Ueltschi fullname: Ueltschi, Olivia organization: The Comprehensive Cancer Center–Arthur G. James Cancer Hospital and Richard J. Solove Research Institute, The Ohio State University, Columbus, Ohio, USA – sequence: 10 givenname: Dana M. surname: Middendorf fullname: Middendorf, Dana M. organization: The Ohio State University College of Medicine, Columbus, Ohio, USA – sequence: 11 givenname: Muhammed O. surname: Jajeh fullname: Jajeh, Muhammed O. organization: Ohio University Heritage College of Osteopathic Medicine, Athens, Ohio, USA – sequence: 12 givenname: Aadit B. surname: Vishwanath fullname: Vishwanath, Aadit B. organization: Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan, USA – sequence: 13 givenname: Kyle surname: Porter fullname: Porter, Kyle organization: Center for Biostatistics, The Ohio State University Wexner Medical Center, Columbus, Ohio, USA – sequence: 14 givenname: Phil A. surname: Hart fullname: Hart, Phil A. organization: Division of Gastroenterology, Hepatology, and Nutrition, Division of Internal Medicine, The Ohio State University Wexner Medical Center, Columbus, Ohio, USA – sequence: 15 givenname: Georgios I. surname: Papachristou fullname: Papachristou, Georgios I. organization: Division of Gastroenterology, Hepatology, and Nutrition, Division of Internal Medicine, The Ohio State University Wexner Medical Center, Columbus, Ohio, USA – sequence: 16 givenname: Zobeida surname: Cruz-Monserrate fullname: Cruz-Monserrate, Zobeida organization: Division of Gastroenterology, Hepatology, and Nutrition, Division of Internal Medicine, The Ohio State University Wexner Medical Center, Columbus, Ohio, USA – sequence: 17 givenname: Darwin L. surname: Conwell fullname: Conwell, Darwin L. organization: Division of Gastroenterology, Hepatology, and Nutrition, Division of Internal Medicine, The Ohio State University Wexner Medical Center, Columbus, Ohio, USA – sequence: 18 givenname: Somashekar G. orcidid: 0000-0001-5748-7890 surname: Krishna fullname: Krishna, Somashekar G. organization: Division of Gastroenterology, Hepatology, and Nutrition, Division of Internal Medicine, The Ohio State University Wexner Medical Center, Columbus, Ohio, USA |
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| Keywords | CNN HGD-Ca CI LR CAD AI LGD IPMN ROI AUC SD HBM PCL AGA nCLE SBM |
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