High performance in risk stratification of intraductal papillary mucinous neoplasms by confocal laser endomicroscopy image analysis with convolutional neural networks (with video)

Jorge D. Machicado, Wei Lun Chao, David E. Carlyn, Tai Yu Pan, Sarah Poland, Victoria L. Alexander, Tassiana G. Maloof, Kelly Dubay, Olivia Ueltschi, Dana M. Middendorf, Muhammed O. Jajeh, Aadit B. Vishwanath, Kyle Porter, Phil A. Hart, Georgios I. Papachristou, Zobeida Cruz-Monserrate, Darwin L. Conwell, Somashekar G. Krishna

Research output: Contribution to journalArticlepeer-review

41 Scopus citations

Abstract

Background and Aims: 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. Methods: 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. Results: 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. Conclusion: 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.

Original languageEnglish
Pages (from-to)78-87.e2
JournalGastrointestinal Endoscopy
Volume94
Issue number1
DOIs
StatePublished - Jul 2021

Bibliographical note

Publisher Copyright:
© 2021 American Society for Gastrointestinal Endoscopy

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Gastroenterology

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