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.
原发性肝癌，尤其是肝细胞癌（HCC）和胆管癌（CC）的诊断，即使对专家而言也是一个具有挑战性且劳动密集型的过程，而继发性肝癌进一步增加了诊断的复杂性。人工智能（AI）通过利用数字化全切片图像（WSIs）促进肿瘤的组织病理学分类，为这些诊断难题提供了有前景的解决方案。本研究旨在开发一种深度学习模型，利用组织病理学图像区分HCC、CC和转移性结直肠癌（mCRC），并探讨其临床意义。研究使用来自HCC、CC和mCRC的WSIs训练分类器。在正常组织/肿瘤分类任务中，HCC、CC和mCRC的曲线下面积（AUC）分别为0.989、0.988和0.991。利用筛选后的肿瘤组织训练HCC/其他癌症类型分类器，可有效区分HCC与CC及mCRC，综合AUC达0.998。随后，CC/mCRC分类器区分CC与mCRC的综合AUC为0.995。然而，在外部数据集测试中，HCC/其他癌症类型分类器表现欠佳，AUC仅为0.745。将原始训练数据集与外部数据集合并并重新训练后，分类性能显著提升，所有分类器AUC均达到1.000。尽管这些结果令人鼓舞并为肝癌诊断提供了重要见解，但模型的优化与验证仍需进一步研究。
肿瘤(癌症)患者之家