Assessment of Ablative Margin After Microwave Ablation for Hepatocellular Carcinoma Using Deep Learning-Based Deformable Image Registration - An, Chao; Jiang, Yiquan; Huang, Zhimei; Gu, Yangkui; Zhang, Tianqi; Ma, Ling; Huang, Jinhua (Frontiers in Oncology, 2020)
Aim: To assess the ablative margin (AM) after microwave ablation (MWA) for hepatocellular carcinoma (HCC) with a deep learning-based deformable image registration (DIR) technique and analyse the relation between the AM and local tumour progression(LTP). Patients and methods: From November 2012 to April 2019, 141 consecutive patients with single HCC (diameter ≤ 5 cm) who underwent MWA were reviewed. Baseline characteristics were ‎collected to identify the risk factors for the determination of LTP after MWA. Contrast-enhanced magnetic resonance imaging scans were performed within 1 month before and 3 months after treatment. Complete ablation was confirmed for all lesions. The AM was measured based on the margin size between the tumour region and the deformed ablative region. To correct the misalignment, DIR between images before and after ablation was achieved by an unsupervised landmark-constrained convolutional neural network. The patients were classified into two groups according to their AMs: group A (AM ≤ 5 mm) and group B (AM > 5 mm). The cumulative LTP rates were compared between the two groups using Kaplan-Meier curves and the log-rank test. Multivariate analyses were performed on clinicopathological variables to identify factors affecting LTP. Results: After a median follow-up period of 28.9 months, LTP was found in 19 patients. The mean tumour and ablation zone sizes were 2.3 ± 0.9 cm and 3.8 ± 1.2 cm, respectively. The mean minimum ablation margin was 3.4 ± 0.7 mm (range, 0-16 mm). After MWA, the 6-, 12-, and 24-month LTP rates were 9.9 %, 20.6 %, and 24.8 %, respectively, for group A and 4.0 %, 8.4 %, and 8.4 %, respectively, for group B, showing significant differences between the two groups (P = 0.001). Multivariate analysis showed that being > 65 years of age (P =0.032, hazard ratio [HR]: 4.587, 95 % confidence interval [CI], 1.045–22.296) and having a minimum AM <5 mm (P = 0.010, HR: 4.587, 95 % CI, 1.045–22.296) were independent risk factors for LTP after MWA. Conclusion: The novel technology of unsupervised landmark-constrained convolutional neural network-based DIR is feasible and useful in evaluating the ablative effect of MWA for HCC.