HTRecNet: a deep learning study for efficient and accurate diagnosis of hepatocellular carcinoma and cholangiocarcinoma

作者：Jingze Li, Yupeng Niu, Jiang Du, Jiani Wu, Weichen Guo, Yujie Wang, Jian Wang, Jiong Mu · 发表于：Frontiers in Cell and Developmental Biology · 年份：2025 · DOI：10.3389/fcell.2025.1549811 · 被引用次数：4 · 研究领域：Cholangiocarcinoma and Gallbladder Cancer Studies、AI in cancer detection、Hepatocellular Carcinoma Treatment and Prognosis

Background: Hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA) represent the primary liver cancer types. Traditional diagnostic techniques, reliant on radiologist interpretation, are both time-intensive and often inadequate for detecting the less prevalent CCA. There is an emergent need to explore automated diagnostic methods using deep learning to address these challenges. Methods: This study introduces HTRecNet, a novel deep learning framework for enhanced diagnostic precision and efficiency. The model incorporates sophisticated data augmentation strategies to optimize feature extraction, ensuring robust performance even with constrained sample sizes. A comprehensive dataset of 5,432 histopathological images was divided into 5,096 for training and validation, and 336 for external testing. Evaluation was conducted using five-fold cross-validation and external validation, applying metrics such as accuracy, area under the receiver operating characteristic curve (AUC), and Matthews correlation coefficient (MCC) against established clinical benchmarks. Results: The training and validation cohorts comprised 1,536 images of normal liver tissue, 3,380 of HCC, and 180 of CCA. HTRecNet showed exceptional efficacy, consistently achieving AUC values over 0.99 across all categories. In external testing, the model reached an accuracy of 0.97 and an MCC of 0.95, affirming its reliability in distinguishing between normal, HCC, and CCA tissues. Conclusion: HTRecNet markedly enhance...