Endoscopic prediction of submucosal invasion in Barrett’s cancer with the use of Artificial Intelligence: A pilot Study

  • Background and aims: The accurate differentiation between T1a and T1b Barrett’s cancer has both therapeutic and prognostic implications but is challenging even for experienced physicians. We trained an Artificial Intelligence (AI) system on the basis of deep artificial neural networks (deep learning) to differentiate between T1a and T1b Barrett’s cancer white-light images. Methods: Endoscopic images from three tertiary care centres in Germany were collected retrospectively. A deep learning system was trained and tested using the principles of cross-validation. A total of 230 white-light endoscopic images (108 T1a and 122 T1b) was evaluated with the AI-system. For comparison, the images were also classified by experts specialized in endoscopic diagnosis and treatment of Barrett’s cancer. Results: The sensitivity, specificity, F1 and accuracy of the AI-system in the differentiation between T1a and T1b cancer lesions was 0.77, 0.64, 0.73 and 0.71, respectively. There was no statistically significant difference between the performance of the AI-system and that of human experts with sensitivity, specificity, F1 and accuracy of 0.63, 0.78, 0.67 and 0.70 respectively. Conclusion: This pilot study demonstrates the first multicenter application of an AI-based system in the prediction of submucosal invasion in endoscopic images of Barrett’s cancer. AI scored equal to international experts in the field, but more work is necessary to improve the system and apply it to video sequences and in a real-life setting. Nevertheless, the correct prediction of submucosal invasion in Barret´s cancer remains challenging for both experts and AI.

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Author:Alanna EbigboORCiD, Robert Mendel, Tobias RückertORCiD, Laurin Schuster, Andreas Probst, Johannes Manzeneder, Friederike Prinz, Matthias Mende, Ingo Steinbrück, Siegbert Faiss, David Rauber, Luis Antonio De Souza Jr.ORCiD, João Paulo PapaORCiD, Pierre DeprezORCiD, Tsuneo OyamaORCiD, Akiko Takahashi, Stefan Seewald, Prateek Sharma, Michael F. Byrne, Christoph PalmORCiDGND, Helmut Messmann
Parent Title (English):Endoscopy
Place of publication:Stuttgart
Document Type:Article
Year of first Publication:2021
Release Date:2020/11/30
Tag:Adenocarcinoma; Artificial Intelligence; Barrett’s cancer; Machine learning; submucosal invasion
GND Keyword:Maschinelles Lernen; Neuronales Netz; Speiseröhrenkrebs; Diagnose
First Page:878
Last Page:883
Institutes:Fakultät Informatik und Mathematik
Regensburg Center of Health Sciences and Technology - RCHST
Fakultät Informatik und Mathematik / Regensburg Medical Image Computing (ReMIC)
research focus:Lebenswissenschaften und Ethik
Licence (German):Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG