Department of Diagnostic Radiology and Interventional Radiology, St. Marianna, University School of Medicine, 2-16-1, Sugao, Miyamae, Kawasaki, Kanagawa, 216-8511, Japan.
Department of Advanced Biomedical Imaging and Informatics, St. Marianna, University School of Medicine, 2-16-1, Sugao, Miyamae, Kawasaki, Kanagawa, 216-8511, Japan.
Cardiovasc Intervent Radiol. 2024 Nov;47(11):1495-1505. doi: 10.1007/s00270-024-03854-2. Epub 2024 Oct 6.
To create and evaluate prediction models of local tumor recurrence after successful conventional transcatheter arterial chemoembolization (c-TACE) via radiomics analysis of lipiodol deposition using cone-beam computed tomography (CBCT) images obtained at the completion of TACE.
A total of 103 hepatocellular carcinoma nodules in 71 patients, who achieved a complete response (CR) based on the modified Response Evaluation Criteria in Solid Tumors 1 month after TACE, were categorized into two groups: prolonged CR and recurrence groups. Three types of areas were segmented on CBCT: whole segment (WS), tumor segment (TS), and peritumor segment (PS). From each segment, 105 radiomic features were extracted. The nodules were randomly divided into training and test datasets at a ratio of 7:3. Following feature reduction for each segment, three models (clinical, radiomics, and clinical-radiomics models) were developed to predict recurrence based on logistic regression.
The clinical-radiomics model of WS showed the best performance, with the area under the curve values of 0.853 (95% confidence interval: 0.765-0.941) in training and 0.752 (0.580-0.924) in test dataset. In the analysis of radiomic feature importance of all models, among all radiomic features, glcm_MaximumProbability, shape_MeshVolume and shape_MajorAxisLength had negative coefficients. In contrast, shape_SurfaceVolumeRatio, shape_Elongation, glszm_SizeZoneNonUniformityNormalized, and gldm_GrayLevelNonUniformity had positive coefficients.
In this study, a machine-learning model based on cone-beam CT images obtained at the completion of c-TACE was able to predict local tumor recurrence after successful c-TACE. Nonuniform lipiodol deposition and irregular shapes may increase the likelihood of recurrence.
通过对 TACE 完成时获得的锥形束 CT（CBCT）图像中碘油沉积进行放射组学分析，创建并评估成功常规经导管动脉化疗栓塞（c-TACE）后局部肿瘤复发的预测模型。
共纳入 71 例患者的 103 个肝癌结节，这些患者在 TACE 后 1 个月根据改良实体瘤反应评估标准 1 实现完全缓解（CR），并将其分为两组：延长 CR 组和复发组。在 CBCT 上对三个区域进行分割：全节段（WS）、肿瘤节段（TS）和肿瘤周围节段（PS）。从每个节段提取 105 个放射组学特征。结节随机分为训练集和测试集，比例为 7:3。对每个节段进行特征降维后，基于逻辑回归开发三种模型（临床、放射组学和临床放射组学模型）以预测复发。
WS 的临床放射组学模型表现最佳，在训练数据集和测试数据集的曲线下面积分别为 0.853（95%置信区间：0.765-0.941）和 0.752（0.580-0.924）。在所有模型的放射组学特征重要性分析中，在所有放射组学特征中，glcm_MaximumProbability、shape_MeshVolume 和 shape_MajorAxisLength 具有负系数。相比之下，shape_SurfaceVolumeRatio、shape_Elongation、glszm_SizeZoneNonUniformityNormalized 和 gldm_GrayLevelNonUniformity 具有正系数。
在这项研究中，基于 TACE 完成时获得的锥形束 CT 图像的机器学习模型能够预测成功 c-TACE 后局部肿瘤复发。非均匀碘油沉积和不规则形状可能会增加复发的可能性。
