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. 2023 Nov;165(5):1262-1275.
doi: 10.1053/j.gastro.2023.07.026. Epub 2023 Aug 9.

Deep Learning-Enabled Diagnosis of Liver Adenocarcinoma

Thomas Albrecht  1 , Annik Rossberg  2 , Jana Dorothea Albrecht  3 , Jan Peter Nicolay  3 , Beate Katharina Straub  4 , Tiemo Sven Gerber  4 , Michael Albrecht  5 , Fritz Brinkmann  2 , Alphonse Charbel  2 , Constantin Schwab  2 , Johannes Schreck  2 , Alexander Brobeil  2 , Christa Flechtenmacher  2 , Moritz von Winterfeld  2 , Bruno Christian Köhler  6 , Christoph Springfeld  6 , Arianeb Mehrabi  7 , Stephan Singer  8 , Monika Nadja Vogel  9 , Olaf Neumann  2 , Albrecht Stenzinger  2 , Peter Schirmacher  10 , Cleo-Aron Weis  2 , Stephanie Roessler  10 , Jakob Nikolas Kather  11 , Benjamin Goeppert  12
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Free article

Deep Learning-Enabled Diagnosis of Liver Adenocarcinoma

Thomas Albrecht et al. Gastroenterology. 2023 Nov.
Free article

Abstract

Background & aims: Diagnosis of adenocarcinoma in the liver is a frequent scenario in routine pathology and has a critical impact on clinical decision making. However, rendering a correct diagnosis can be challenging, and often requires the integration of clinical, radiologic, and immunohistochemical information. We present a deep learning model (HEPNET) to distinguish intrahepatic cholangiocarcinoma from colorectal liver metastasis, as the most frequent primary and secondary forms of liver adenocarcinoma, with clinical grade accuracy using H&E-stained whole-slide images.

Methods: HEPNET was trained on 714,589 image tiles from 456 patients who were randomly selected in a stratified manner from a pool of 571 patients who underwent surgical resection or biopsy at Heidelberg University Hospital. Model performance was evaluated on a hold-out internal test set comprising 115 patients and externally validated on 159 patients recruited at Mainz University Hospital.

Results: On the hold-out internal test set, HEPNET achieved an area under the receiver operating characteristic curve of 0.994 (95% CI, 0.989-1.000) and an accuracy of 96.522% (95% CI, 94.521%-98.694%) at the patient level. Validation on the external test set yielded an area under the receiver operating characteristic curve of 0.997 (95% CI, 0.995-1.000), corresponding to an accuracy of 98.113% (95% CI, 96.907%-100.000%). HEPNET surpassed the performance of 6 pathology experts with different levels of experience in a reader study of 50 patients (P = .0005), boosted the performance of resident pathologists to the level of senior pathologists, and reduced potential downstream analyses.

Conclusions: We provided a ready-to-use tool with clinical grade performance that may facilitate routine pathology by rendering a definitive diagnosis and guiding ancillary testing. The incorporation of HEPNET into pathology laboratories may optimize the diagnostic workflow, complemented by test-related labor and cost savings.

Keywords: Artificial Intelligence; Biliary Tract Cancer; Digital Pathology; Intestinal Cancer.

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