Assessment of Ablative Margin After Microwave Ablation for Hepatocellular Carcinoma Using Deep Learning-Based Deformable Image Registration

Abstract

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 analyze the relation between the AM and local tumor 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 tumor 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 tumor 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). The DIR technique had higher AUC for 2-year LTP without a significant difference compared with the registration assessment without DL (P = 0.325). The 6-, 12-, and 24month LTP rates were 9.9, 20.6, and 24.8%, respectively, in group A, and 4.0, 8.4, and 8.4%, respectively, in group B. There were significant differences between the two groups (P = 0.011). Multivariate analysis showed that being >65 years of age (P = 0.032, hazard ratio (HR): 2.463, 95% confidence interval (CI), 1.028-6.152) and AM ≤ 5 mm (P = 0.010, HR: 3.195, 95% CI, 1.324-7.752) were independent risk factors for LTP after MWA. An et al. Using Deep Learning for Registration 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.

Artificial intelligence in interventional radiology: state of the art

“…Current methodologies lack intra-procedural accuracy, posing limitations in assessing ablative margin and tissue contraction. Several retrospective studies have employed DL to address these difficulties, demonstrating its utility in achieving deformable image registration and auto-segmentation [ 23 – 25 ]. The COVER-ALL randomised controlled trial investigated a novel AI-based intra-procedural approach to optimise tumour coverage and minimise non-target tissue ablation, potentially elevating liver ablation efficacy [ 26 ].…”

Applications of Microwaves in Medicine Leveraging Artificial Intelligence: Future Perspectives

“…Chao An et al developed an ML model to predict the early recurrence of cancer in patients treated with MWA in the early stages of hepatic cell carcinoma (HCC) based on the clinical text data. After using SHapley Additive exPlanations (SHAP) and local interpretable model-agnostic explanations (LIME) algorithms as interpretation algorithms, the authors finally concluded that the ML method using the XGBoost model helps physicians with decision making before MWA for HCC in clinical practice and trials [96]. Although these studies have incorporated AI for thermal ablative techniques, further research in the development of ML models is necessary for more accurate diagnosis and treatment outcomes.…”

The Role of Radiomics and AI Technologies in the Segmentation, Detection, and Management of Hepatocellular Carcinoma

“…An et al [ 101 ] designed a deep learning-based deformable image registration (DIR) technique to assess the ablation margin in HCC treated by MWA and correlate it with local tumor progression (LTP). They compared post-contrast MRI images dated one month prior and three months after MWA to measure the ablative margin, which was defined as the distance between the original tumor and the deformed ablated region.…”