Deep learning-enabled diagnosis of liver adenocarcinoma.

Albrecht, Thomas; Rossberg, Annik; Albrecht, Jana Dorothea; Nicolay, Jan Peter; Straub, Beate Katharina; Gerber, Tiemo Sven; Albrecht, Michael; Brinkmann, Fritz; Charbel, Alphonse; Schwab, Constantin; Schreck, Johannes; Brobeil, Alexander; Flechtenmacher, Christa; von Winterfeld, Moritz; Köhler, Bruno Christian; Springfeld, Christoph; Mehrabi, Arianeb; Singer, Stephan; Vogel, Monika Nadja; Neumann, Olaf; ... (2023). Deep learning-enabled diagnosis of liver adenocarcinoma. Gastroenterology, 165(5), pp. 1262-1275. Elsevier 10.1053/j.gastro.2023.07.026

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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, radiological, and immunohistochemical information. We present a deep learning model (HEPNET) to distinguish intrahepatic cholangiocarcinoma (iCCA) from colorectal liver metastasis (CRM) as the most frequent primary and secondary forms of liver adenocarcinoma with clinical-grade accuracy using hematoxylin and eosin-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 (AUROC) 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 AUROC 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 six 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.

CONCLUSION

Here, we provide a ready-to-use tool with a 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.

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