Qu Wei-Feng, Tian Meng-Xin, Qiu Jing-Tao, Guo Yu-Cheng, Tao Chen-Yang, Liu Wei-Ren, Tang Zheng, Qian Kun, Wang Zhi-Xun, Li Xiao-Yu, Hu Wei-An, Zhou Jian, Fan Jia, Zou Hao, Hou Ying-Yong, Shi Ying-Hong
Department of Liver Surgery, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Key Laboratory of Carcinogenesis and Cancer Invasion of Ministry of Education, Shanghai, China.
Department of General Surgery, Zhongshan Hospital, Fudan University, Shanghai, China.
Front Oncol. 2022 Aug 19;12:968202. doi: 10.3389/fonc.2022.968202. eCollection 2022.
Postoperative recurrence impedes the curability of early-stage hepatocellular carcinoma (E-HCC). We aimed to establish a novel recurrence-related pathological prognosticator with artificial intelligence, and investigate the relationship between pathological features and the local immunological microenvironment.
A total of 576 whole-slide images (WSIs) were collected from 547 patients with E-HCC in the Zhongshan cohort, which was randomly divided into a training cohort and a validation cohort. The external validation cohort comprised 147 Tumor Node Metastasis (TNM) stage I patients from The Cancer Genome Atlas (TCGA) database. Six types of HCC tissues were identified by a weakly supervised convolutional neural network. A recurrence-related histological score (HS) was constructed and validated. The correlation between immune microenvironment and HS was evaluated through extensive immunohistochemical data.
The overall classification accuracy of HCC tissues was 94.17%. The C-indexes of HS in the training, validation and TCGA cohorts were 0.804, 0.739 and 0.708, respectively. Multivariate analysis showed that the HS (HR= 4.05, 95% CI: 3.40-4.84) was an independent predictor for recurrence-free survival. Patients in HS high-risk group had elevated preoperative alpha-fetoprotein levels, poorer tumor differentiation and a higher proportion of microvascular invasion. The immunohistochemistry data linked the HS to local immune cell infiltration. HS was positively correlated with the expression level of peritumoral CD14 cells (= 0.013), and negatively with the intratumoral CD8 cells (< 0.001).
The study established a novel histological score that predicted short-term and long-term recurrence for E-HCCs using deep learning, which could facilitate clinical decision making in recurrence prediction and management.
术后复发阻碍了早期肝细胞癌（E-HCC）的治愈。我们旨在利用人工智能建立一种新的与复发相关的病理预后指标，并研究病理特征与局部免疫微环境之间的关系。
从中山队列的547例E-HCC患者中收集了576张全切片图像（WSIs），随机分为训练队列和验证队列。外部验证队列包括来自癌症基因组图谱（TCGA）数据库的147例肿瘤淋巴结转移（TNM）I期患者。通过弱监督卷积神经网络识别六种类型的肝癌组织。构建并验证了与复发相关的组织学评分（HS）。通过广泛的免疫组化数据评估免疫微环境与HS之间的相关性。
肝癌组织的总体分类准确率为94.17%。HS在训练、验证和TCGA队列中的C指数分别为0.804、0.739和0.708。多变量分析显示，HS（HR=4.05，95%CI：3.40-4.84）是无复发生存的独立预测因子。HS高危组患者术前甲胎蛋白水平升高，肿瘤分化较差，微血管侵犯比例较高。免疫组化数据将HS与局部免疫细胞浸润联系起来。HS与瘤周CD14细胞的表达水平呈正相关（=0.013），与瘤内CD8细胞呈负相关（<0.001）。
本研究建立了一种新的组织学评分，利用深度学习预测E-HCC的短期和长期复发，这有助于复发预测和管理中的临床决策。
