Purpose: To assess the technical success of radiofrequency ablation (RFA) in patients with hepatocellular carcinoma (HCC), an artificial intelligence (AI) model was developed to estimate the tumor coverage without the need for segmentation or registration tools.

Methods: A secondary retrospective analysis of 550 patients in the multicenter and multinational OPTIMA trial (3-7 cm solidary HCC lesions, randomized to RFA or RFA + LTLD) identified 182 patients with well-defined pre-RFA tumor and 1-month post-RFA devascularized ablation zones on enhanced CT. The ground-truth, or percent tumor coverage, was determined based on the result of semi-automatic 3D tumor and ablation zone segmentation and elastic registration. The isocenter of the tumor and ablation was isolated on 2D axial CT images. Feature extraction was performed, and classification and linear regression models were built. Images were augmented, and 728 image pairs were used for training and testing. The estimated percent tumor coverage using the models was compared to ground-truth. Validation was performed on eight patient cases from a separate institution, where RFA was performed, and pre- and post-ablation images were collected.

Results: In testing cohorts, the best model accuracy was with classification and moderate data augmentation (AUC = 0.86, TPR = 0.59, and TNR = 0.89, accuracy = 69%) and regression with random forest (RMSE = 12.6%, MAE = 9.8%). Validation in a separate institution did not achieve accuracy greater than random estimation. Visual review of training cases suggests that poor tumor coverage may be a result of atypical ablation zone shrinkage 1 month post-RFA, which may not be reflected in clinical utilization.

Conclusion: An AI model that uses 2D images at the center of the tumor and 1 month post-ablation can accurately estimate ablation tumor coverage. In separate validation cohorts, translation could be challenging.

Keywords: AI; Ablation confirmation; HCC; Radiofrequency ablation.

© 2025. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.

目的： 为了评估射频消融（RFA）在肝细胞癌（HCC）患者中的技术成功率，开发了一个人工智能（AI）模型来估计肿瘤覆盖情况，而无需使用分割或配准工具。

方法： 对OPTIMA试验中550名患者的二次回顾性分析（3-7厘米的实质性HCC病变，随机分配至RFA组或RFA加LTLD组）确定了182名在增强CT上具有明确术前和术后一个月去血管化消融区图像的患者。基于半自动三维肿瘤和消融区分割及弹性配准的结果，确定了真实的百分比肿瘤覆盖情况（ground-truth）。在二维轴向CT图像上隔离肿瘤和消融的等中心点。进行特征提取，并构建分类和线性回归模型。对图像进行了增强处理，使用728张图像对进行训练和测试。利用模型估算出的肿瘤覆盖率与真实的百分比覆盖情况进行比较。在另一家机构中，收集了八个接受RFA治疗且具有术前和术后影像资料的病例，用于验证。

结果： 在测试组中，最佳模型准确性出现在分类及适度数据增强（AUC = 0.86, TPR = 0.59, 和TNR = 0.89, 准确性= 69%）和随机森林回归（RMSE = 12.6%, MAE = 9.8%）。在另一家机构中的验证未能实现超过随机估计的准确性。对训练案例进行视觉审查表明，较差的肿瘤覆盖可能是由于术后一个月消融区非典型收缩所致，这可能不会反映在临床应用中。

结论： 使用位于肿瘤中心和RFA后一月的二维图像的人工智能模型可以准确估计消融肿瘤覆盖率。但在独立验证组中的转换可能会有挑战性。

关键词： AI；消融确认；HCC；射频消融。