## 4 Discussion  
This study evaluated the utility of radiomics derived from contrast-enhanced CT scans and AI models in predicting the response and prognosis to TACE treatment in patients with HCC. We conducted a comparative analysis between tumor responses to TACE predicted by the radiomics model and actual responses measured using mRECIST guidelines. Additionally, comparisons of PFS and OS were made between the mRECIST guidelines and radiomics model prediction. Variables were extracted using radiomics from both baseline and follow-up contrast-enhanced CT scans, and ultimately, four variables were selected through feature selection calibration. Ultimately, the C-index of the radiomics prediction model for survival analysis yielded a value of 0.582. Figure 4: Result of feature selection. Figure 5: Summary of the diagnostic performance of the radiomics features and clinical factors for the prognosis of HCC response to TACE treatment. TACE is the established standard treatment for intermediate-stage HCC, and CT serves as the primary imaging modality for evaluating treatment response. Lipidol is commonly utilized in TACE, exhibiting high attenuation on CT scans. The region exhibiting high attenuation due to lipidod uptake demonstrates a robust correlation with tumor necrosis [23]. However, the high attenuation displayed by lipidod on CT scans creates challenges in evaluating small enhancing viable lesions, as it gives rise to beam-hardening artifacts in the surrounding area [24]. The sensitivity of CT in detecting residual or recurrent disease after TACE with lipidod is less than 50% when MR is considered the reference standard [25; 26]. These findings indicate constraints in the assessment of TACE treatment response on CT scans through conventional visual assessment. Radiomics is a technique employed for the quantitative characterization of medical images, involving high-dimensional, quantitative data [27]. In contrast to conventional methods that treat medical images as visual data for manual inspection, radiomics presents a novel approach to extracting information embedded within the medical images [28], making it possible to identify high-dimensional variables beyond semantic features obtained through conventional visual assessments. There have been several radiomics studies aiming to identify variables associated with the response to TACE in patients with HCC. Several studies have shown that a radiomics model based on MRI effectively predicts the response to TACE [29; 30; 31; 32]. [PERSON] et al. found that a model, incorporating radiomics of post-TACE CT and clinical scores, effectively predicted the prognosis after TACE [33]. This model includes a single radiomic feature, Large Dependence High Gray Level Emphasis, and achieved a C-index of 0.67 for overall survival. This is in line with the results of our study that Large Dependence High Gray Level Emphasis in follow-up CT scans was the most significant feature associated with overall survival. This feature assesses the joint distribution of large dependence involving higher gray-level values [34]. Furthermore, it was one of the key features for distinguishing between histologic grades 1 and 3 in an MRI radiomics study of HCC [35]. In our study, the model was created incorporating a total of four features from baseline and post-TACE CT and demonstrated a C-index score of 0.582 and time-dependence AUROC of 0.6 for overall survival. Although the C-index and AUROC were not excellent, the radiomic features extracted in this process predicted treatment response better than manual analyses. Our study has several limitations. First, the study was limited to a relatively small number of samples, which may cause instability in the feature values. The sample size, especially the number of patients in the prospective test data (57 cases), may limit the generalizability of the study results. Future studies with larger samples are needed to fully validate the findings.