Prediction of microvascular invasion in hepatocellular carcinoma based on preoperative Gd-EOB-DTPA-enhanced MRI: Comparison of predictive performance among 2D, 2D-expansion and 3D deep learning models

Frontiers in Oncology

Oncology

Juan Gao¹  Weihua Feng²  Xiaoming Zhou²  Fang Liu²  Chongfeng Duan²  Tao Wang²  Ziwei Luo²  Haiyang Yu²  Yu Zhang²  Yichen Zang³  Zhen Li⁴  Jing Yu⁵  Lufan Chang⁵  Hao Liu⁵ 

[1] Department of Cardiology, Affiliated Hospital of Qingdao University, Qingdao, Shandong, China;Department of Radiology, Affiliated Hospital of Qingdao University, Qingdao, Shandong, China;Department of Ultrasound, Affiliated Hospital of Qingdao University, Qingdao, Shandong, China;School of Medical Imaging, Weifang Medical University, Weifang, Shandong, China;Yizhun Medical AI Co., Ltd, Beijing, China;

关键词: microvascular invasion;    hepatocellular carcinoma;    gadoxetic acid-enhanced MRI;    artificial intelligence;    deep learning;   

DOI  :  10.3389/fonc.2023.987781

 received in 2022-07-06, accepted in 2023-01-20,  发布年份 2023

来源: Frontiers

【 摘 要 】

PurposeTo evaluate and compare the predictive performance of different deep learning models using gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI in predicting microvascular invasion (MVI) in hepatocellular carcinoma.MethodsThe data of 233 patients with pathologically confirmed hepatocellular carcinoma (HCC) treated at our hospital from June 2016 to June 2021 were retrospectively analyzed. Three deep learning models were constructed based on three different delineate methods of the region of interest (ROI) using the Darwin Scientific Research Platform (Beijing Yizhun Intelligent Technology Co., Ltd., China). Manual segmentation of ROI was performed on the T1-weighted axial Hepatobiliary phase images. According to the ratio of 7:3, the samples were divided into a training set (N=163) and a validation set (N=70). The receiver operating characteristic (ROC) curve was used to evaluate the predictive performance of three models, and their sensitivity, specificity and accuracy were assessed.ResultsAmong 233 HCC patients, 109 were pathologically MVI positive, including 91 men and 18 women, with an average age of 58.20 ± 10.17 years; 124 patients were MVI negative, including 93 men and 31 women, with an average age of 58.26 ± 10.20 years. Among three deep learning models, 2D-expansion-DL model and 3D-DL model showed relatively good performance, the AUC value were 0.70 (P=0.003) (95% CI 0.57–0.82) and 0.72 (P<0.001) (95% CI 0.60–0.84), respectively. In the 2D-expansion-DL model, the accuracy, sensitivity and specificity were 0.7143, 0.739 and 0.688. In the 3D-DL model, the accuracy, sensitivity and specificity were 0.6714, 0.800 and 0.575, respectively. Compared with the 3D-DL model (based on 3D-ResNet), the 2D-DL model is smaller in scale and runs faster. The frames per second (FPS) for the 2D-DL model is 244.7566, which is much larger than that of the 3D-DL model (73.3374).ConclusionThe deep learning model based on Gd-EOB-DTPA-enhanced MRI could preoperatively evaluate MVI in HCC. Considering that the predictive performance of 2D-expansion-DL model was almost the same as the 3D-DL model and the former was relatively easy to implement, we prefer the 2D-expansion-DL model in practical research.

【 授权许可】

Unknown   
Copyright © 2023 Wang, Li, Yu, Duan, Feng, Chang, Yu, Liu, Gao, Zang, Luo, Liu, Zhang and Zhou