## 4 Discussion  
In this study, a novel approach for radiodosiomics analysis was proposed. The treatment failure prediction model was able to stratify patients into low- and high-risk groups. The CT-\(R\), CT-\(L\), DD-\(L\), DD-\(T\), DD-\(RT\), DD-\(LT\), and CD-\(RLT\) models may play a role in  
Figure 4: Treatment failure-free probability curves of CT-\(R\) (**a**), CT-\(L\) (**b**), DD-\(L\) (**c**), DD-\(T\) (**d**), DD-\(RT\) (**e**), DD-\(LT\) (**f**) and CD-\(RLT\) (**g**) models in the training (**left**) and test dataset (**right**). Yellow line: High-risk group, blue line: Low-risk group. The dash line represents 95% confidence interval. predicting treatment failure after CRT in patients with HNSCC. The LBP or topology-based features were used in six of the seven models (CT-\(L\), DD-\(L\), DD-\(T\), DD-\(RT\), DD-\(LT\), and CD-\(RLT\)); they are useful for predicting treatment failure. Five of the seven models (DD-\(L\), DD-\(T\), DD-\(RT\), DD-\(LT\), and CD-\(RLT\)) were based on the features extracted from dose distribution. Therefore, the LBP- and topology-based features, and the features extracted from dose distribution, could be useful for predicting treatment failure prior to treatment planning of CRT. [PERSON] et al. developed two radiomic-based models to predict locoregional recurrence and distant metastasis before CRT in patients with HNSCC [8]. C-indices for prediction performance were 0.52 for radiomics-locoregional recurrence and 0.49 for the radiomics- distant metastasis models. Our CT-\(R\), CT-\(L\), DD-\(L\), DD-\(T\), DD-\(RT\), DD-\(LT\), and CD-\(RLT\) models demonstrated C-indices of 0.64, 0.75, 0.64, 0.76, 0.75, 0.76, and 0.64, respectively. Therefore, our proposed approach can accurately predict treatment failure prior to treatment planning in patients with HNSCC. [PERSON] et al. developed recurrence prediction models for patients with HNSCC [9]. The C-index of the conventional CT radiomics model was 0.54. The C-indices of the dosiomics and CT radiodosiomics models were 0.60 and 0.60, respectively. By comparing our treatment failure prediction models (CT-\(R\), CT-\(L\), DD-\(L\), DD-\(T\), DD-\(RT\), DD-\(LT\), and CD-\(RLT\) models) with their recurrence prediction models, our treatment failure models achieved higher C-indices (0.64, 0.75, 0.76, 0.75, 0.76, and 0.64) than their models. Therefore, the treatment failure prediction performance before CRT in patients with HNSCC may be improved by applying the topology or local binary pattern to conventional radiodosiomics models. However, they reported that the CT radiodosiomics prediction model combined with PET data showed improvement, with a C-index of 0.66. Hence, our model can potentially be improved by applying radiomics features extracted from PET images. Therefore, the incorporation of PET images should be investigated in future studies. Our dataset mixed the cases collected from five independent institutions to ensure generalization performance. [PERSON] et al. reported that the multi-institutional approach significantly improves the generalizability of the models compared to single-center studies [38]. Therefore, our dataset contains inter-institutional variability, which could contribute to the generalizability of the models. Although validation on a completely independent external dataset could not be performed, the inclusion of multi-institutional data in both the training and testing datasets could partially address this concern. However, validation on an independent external dataset would provide stronger evidence of the model's performance. Therefore, we consider the inclusion of additional external datasets an important point for future work to further evaluate and improve the generalizability of the model. [PERSON] et al. reported the utility of adding CT-based radiomics to clinical variables in predicting the outcomes for HNSCC patients treated with CRT [39].