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A deep learning study developed an AI model to distinguish HCC, CC, and metastatic colorectal cancer (mCRC) using digital whole slide images (WSIs), supporting histopathological classification. Internal testing showed high AUC performance for tumor recognition and pairwise differentiation, while an external dataset reduced performance until external data were merged and retraining restored accuracy. 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Deep Learning for the Pathologic Diagnosis of Hepatocellular Carcinoma, Cholangiocarcinoma, and Metastatic Colorectal Cancer. Cancers 2023, 15, 5389. [https://](https://)[ ](https://)[doi.org/10.3390/cancers15225389](doi.org/10.3390/cancers15225389)  \nReceived: 4 October 2023  \nRevised: 1 November 2023  \nAccepted: 9 November 2023  \nPublished: 13 November 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Physiology, CMC Institute for Basic Medical Science, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea; [hjjang@catholic.ac.kr](hjjang@catholic.ac.kr)  \n2 Department of Pathology, Dankook University College of Medicine, Cheonan 31116, Republic of Korea; [cyjy555@daum.net](cyjy555@daum.net)  \n3 Department of Hospital Pathology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea; [glabella85@gmail.com](glabella85@gmail.com)  \n* [Correspondence: hakjjang@catholic.ac.kr](Correspondence: hakjjang@catholic.ac.kr); Tel.: +82-2-2258-1617  \nSimple Summary: The pathologic diagnosis of primary and secondary liver cancers can often bedifficult. Artificial intelligence (AI) presents potential solutions to these difficulties by aiding in the histopathological diagnosis of tumors using digital whole slide images (WSIs) . We developed an AI diagnostic assistant using a deep learning model for distinguishing hepatocellular carcinoma, cholangiocarcinoma, and metastatic colorectal cancer using WSIs. Overall, the classifiers were highly accurate, showing significant potential for improving liver cancer diagnosis and advancing precision medicine. However, additional research is required to further refine and validate these promising tools.  \nAbstract: Diagnosing primary liver cancers, particularly hepatocellular carcinoma (HCC) and cholangiocarcinoma (CC), is a challenging and labor-intensive process, even for experts, and secondary liver cancers further complicate the diagnosis. Artificial intelligence (AI) offers promising solutions to these diagnostic challenges by facilitating the histopathological classification of tumors using digital whole slide images (WSIs) . This study aimed to develop a deep learning model for distinguishing HCC, CC, and metastatic colorectal cancer (mCRC) using histopathological images and to discuss its clinical implications. The WSIs from HCC, CC, and mCRC were used to train the classifiers. For normal/tumor classification, the areas under the curve (AUCs) were 0.989, 0.988, and 0.991 for HCC, CC, and mCRC, respectively. Using proper tumor tissues, the HCC/other cancer type classifier was trained to effectively distinguish HCC from CC and mCRC, with a concatenated AUC of 0.998 . Subsequently, the CC/mCRC classifier differentiated CC from mCRC with a concatenated AUC of 0.995. However, testing on an external dataset revealed that the HCC/other cancer type classifier underperformed with an AUC of 0.745 . After combining the original training datasets with external datasets and retraining, the classification drastically improved, all achieving AUCs of 1.000 . Although these results are promising and offer crucial insights into liver cancer, further research is required for model refinement and validation.  \nKeywords: deep learning; pathology; hepatocellular carcinoma; cholangiocarcinoma; colorectal cancer  \n1. Introduction  \nPrimary liver canc","cbCaittKglEKm68W","https://ap.wps.com/l/cbCaittKglEKm68W","pdf",2346872,11,"English","# Introduction\n# Materials and Methods\n## Dataset and WSI Preparation\n## Model Development and Training\n## Evaluation Metrics\n# Results\n## Normal vs Tumor Classification\n## HCC vs Other Cancers\n## CC vs mCRC\n## External Validation and Retraining\n# Discussion\n# Conclusions","[{\"question\":\"What clinical problem does this deep learning model address?\",\"answer\":\"It targets the difficulty of histopathological diagnosis of primary liver cancers (HCC and CC) and the additional complexity introduced by metastatic colorectal cancer (mCRC) using digital whole slide images.\"},{\"question\":\"Which cancer types are distinguished by the proposed AI system?\",\"answer\":\"The model distinguishes hepatocellular carcinoma (HCC), cholangiocarcinoma (CC), and metastatic colorectal cancer (mCRC) from histopathological WSIs.\"},{\"question\":\"How did the model perform on internal vs external data?\",\"answer\":\"Internal results for classification were highly accurate with high AUC values. When tested on an external dataset, the HCC/other classifier underperformed, but retraining after combining internal and external datasets dramatically improved performance to AUCs reported as 1.000.\"}]","Deep Learning for the Pathologic Diagnosis of Hepatocellular Carcinoma, Cholangiocarcinoma, and Metastatic Colorectal Cancer - Clinical and Diagnostic Implications | PDF",1790273981,28]