1. Creation of a Prediction Model of Local Tumor Recurrence After a Successful Conventional Transcatheter Arterial Chemoembolization Using Cone-Beam Computed Tomography Based-Radiomics.
- Author
-
Hashimoto K, Haraguchi T, Nawata S, Wada S, Hamaguchi S, Nishio M, and Mimura H
- Subjects
- Humans, Male, Female, Middle Aged, Aged, Retrospective Studies, Ethiodized Oil administration & dosage, Predictive Value of Tests, Radiomics, Cone-Beam Computed Tomography methods, Chemoembolization, Therapeutic methods, Liver Neoplasms therapy, Liver Neoplasms diagnostic imaging, Carcinoma, Hepatocellular therapy, Carcinoma, Hepatocellular diagnostic imaging, Neoplasm Recurrence, Local diagnostic imaging
- Abstract
Purpose: 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., Materials and Methods: 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., Results: 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., Conclusion: 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., (© 2024. Springer Science+Business Media, LLC, part of Springer Nature and the Cardiovascular and Interventional Radiological Society of Europe (CIRSE).)
- Published
- 2024
- Full Text
- View/download PDF