Article ; Online: CT images-based 3D convolutional neural network to predict early recurrence of solitary hepatocellular carcinoma after radical hepatectomy.
Diagnostic and interventional radiology (Ankara, Turkey)
2022 Volume 28, Issue 6, Page(s) 524–531
Abstract: 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 3-dimensional (3D) ...
| Abstract | 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 3-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 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 NSS3D-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 < .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 formulating individualized treatment and follow-up strategies. SS is better than NSS in improving the performance of 3D-CNN in our study. |
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| MeSH term(s) | Humans ; Carcinoma, Hepatocellular/diagnostic imaging ; Carcinoma, Hepatocellular/surgery ; Hepatectomy ; Liver Neoplasms/diagnostic imaging ; Liver Neoplasms/surgery ; Tomography, X-Ray Computed ; Neural Networks, Computer |
| Language | English |
| Publishing date | 2022-10-25 |
| Publishing country | Turkey |
| Document type | Journal Article |
| ZDB-ID | 2184145-7 |
| ISSN | 1305-3612 ; 1305-3612 |
| ISSN (online) | 1305-3612 |
| ISSN | 1305-3612 |
| DOI | 10.5152/dir.2022.201097 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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