Prediction of microvascular invasion and pathological differentiation of hepatocellular carcinoma based on a deep learning model

作者全名："He, Xiaojuan; Xu, Yang; Zhou, Chaoyang; Song, Rao; Liu, Yangyang; Zhang, Haiping; Wang, Yudong; Fan, Qianrui; Wang, Dawei; Chen, Weidao; Wang, Jian; Guo, Dajing"

作者地址："[He, Xiaojuan; Xu, Yang; Song, Rao; Liu, Yangyang; Zhang, Haiping; Guo, Dajing] Chongqing Med Univ, Affiliated Hosp 2, Dept Radiol, Chongqing 400010, Peoples R China; [Zhou, Chaoyang; Wang, Jian] Army Mil Med Univ, Affiliated Hosp 1, Dept Radiol, Chongqing 400038, Peoples R China; [Wang, Yudong; Fan, Qianrui; Wang, Dawei; Chen, Weidao] Ocean Int Ctr, Inst Res, InferVis, Beijing 100025, Peoples R China"

通信作者："Guo, DJ (通讯作者)，Chongqing Med Univ, Affiliated Hosp 2, Dept Radiol, Chongqing 400010, Peoples R China."

来源：EUROPEAN JOURNAL OF RADIOLOGY

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:001181644200001

JCR分区：Q1

影响因子：3.2

年份：2024

卷号：172

期号：　

开始页：　

结束页：　

文献类型：Article

关键词：Hepatocellular carcinoma; Microvascular invasion; Pathological differentiation; Deep learning; Computed tomography

摘要："Purpose: To develop a deep learning (DL) model based on preoperative contrast-enhanced computed tomography (CECT) images to predict microvascular invasion (MVI) and pathological differentiation of hepatocellular carcinoma (HCC). Methods: This retrospective study included 640 consecutive patients who underwent surgical resection and were pathologically diagnosed with HCC at two medical institutions from April 2017 to May 2022. CECT images and relevant clinical parameters were collected. All the data were divided into 368 training sets, 138 test sets and 134 validation sets. Through DL, a segmentation model was used to obtain a region of interest (ROI) of the liver, and a classification model was established to predict the pathological status of HCC. Results: The liver segmentation model based on the 3D U-Network had a mean intersection over union (mIoU) score of 0.9120 and a Dice score of 0.9473. Among all the classification prediction models based on the Swin transformer, the fusion models combining image information and clinical parameters exhibited the best performance. The area under the curve (AUC) of the fusion model for predicting the MVI status was 0.941, its accuracy was 0.917, and its specificity was 0.908. The AUC values of the fusion model for predicting poorly differentiated, moderately differentiated and highly differentiated HCC based on the test set were 0.962, 0.957 and 0.996, respectively. Conclusion: The established DL models established can be used to noninvasively and effectively predict the MVI status and the degree of pathological differentiation of HCC, and aid in clinical diagnosis and treatment."

基金机构：Chongqing Medical Scientific Research Project (Joint Project of Chongqing Health Commission and Science and Technology Bureau) [2022ZDXM026]

基金资助正文：Chongqing Medical Scientific Research Project (Joint Project of Chongqing Health Commission and Science and Technology Bureau) (Grant No. 2022ZDXM026) .