Diagnostic and Interventional Radiology
Artificial Intelligence and Informatics - Original Article

CT images-based 3D convolutional neural network to predict early recurrence of solitary hepatocellular carcinoma after radical hepatectomy

1.

Department of Medical Quality Management, Nanfang Hospital, Southern Medical University, Guangzhou, China

2.

Hepatology Unit and Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou, China

3.

Big data center, Nanfang Hospital, Southern Medical University, Guangzhou, China

4.

Department of Oncology, The First Affiliated Hospital, University of South China, Hengyang, China

Diagn Interv Radiol 2020; 1: -
Read: 82 Published: 12 August 2021

PURPOSE: The high rate of recurrence of hepatocellular carcinoma(HCC) after radical hepatectomy is an important factor that affects the long-term survival of patients. This study aimed to develop a computed tomography(CT) images-based three-dimensional(3D) convolutional neural network(CNN) for the preoperative prediction of early recurrence(ER) (≤ 2 years) after radical hepatectomy in patients with solitary HCC, and to compare the effects of segmentation sampling (SS) and non-segmentation sampling (NSS) on the prediction performance of 3D-CNN.

METHODS: Contrast-enhanced CT images of 220 HCC patients were used in this study (training group = 178 and test group = 42). We used SS and NSS to select the volume-of-interest (VOI) to train SS-3D-CNN and NSS-3D-CNN separately. The prediction accuracy was evaluated using the test group. Finally, Gradient-weighted Class Activation Mappings (Grad-CAMs) were plotted to analyze the difference of prediction logic between the SS-3D-CNN and NSS-3D-CNN.

RESULTS: The areas under the receiver operating characteristic curves (AUCs) of the SS-3D-CNN and NSS-3D-CNN in the training group were 0.824 (95% CI 0.764–0.885) and 0.868 (95% CI 0.815–0.921). The AUC of the SS-3D-CNN and NSS-3D-CNN in the test group were 0.789 (95% CI 0.637–0.941) and 0.560 (95% CI 0.378–0.742). The SS-3D-CNN could stratify patients into low- and high-risk groups, with significant differences in recurrence-free survival(RFS) (P < 0.001). But NSS-3D-CNN could not effectively stratify them in the test group. According to the Grad-CAMs, compared with SS-3D-CNN, NSS-3D-CNN was obviously interfered by the nearby tissues.

CONCLUSION: SS-3D-CNN may be of clinical use for identifying high-risk patients and formulate individualised treatment and follow-up strategies. SS is better than NSS in improving the performance of 3D-CNN in our study.
 

EISSN 1305-3612