## 5 Conclusions  
The authors proposed an automated prediction approach for calculating CTRs of part-solid tumors, which achieved high ICCs of 0.943 and 0.904 between reference and predicted CTRs for the internal and external test datasets, showing its robustness for clinical use. The predicted CTRs showed the prognostic power of both datasets in predicting radiotherapy patients with low-risk and high-risk. Future work will involve investigating the improvement of the DLS model and application in patients treated with the same type of radiotherapy. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app14083275/s1](https://www.mdpi.com/article/10.3390/app14083275/s1)]([https://www.mdpi.com/article/10.3390/app14083275/s1](https://www.mdpi.com/article/10.3390/app14083275/s1)), Figure S1: Architectures of a deep learning segmentation (DLS) model (dense V-Net); Figure S2: Visualization of 6-connectivity; Figure S3: Correlation between reference and predicted maximum diameters of whole tumor regions with the proposed model for an internal test dataset B. PCC: Pearson correlation coefficient, ICC: Intraclass correlation coefficient; Figure S4: Bland-Altman plot of reference and predicted maximum diameters of whole tumor regions with the proposed model for an internal test dataset B; Figure S5: Correlation between reference and predicted maximum diameters of solid components with the proposed model for an internal test dataset B. PCC: Pearson correlation coefficient, ICC: Intraclass correlation coefficient; Figure S6: Bland-Altman plot of reference and predicted maximum diameters of solid components with the proposed model for an internal test dataset B; Figure S7: Correlation between reference and predicted maximum diameters of whole tumor regions with the proposed model for an external test dataset. PCC: Pearson correlation coefficient, ICC: Intraclass correlation coefficient; Figure S8: Bland-Altman plot of reference and predicted maximum diameters of whole tumor regions with the proposed model for an external test dataset; Figure S9: Correlation between reference and predicted maximum diameters of solid components with the proposed model for an external test dataset. PCC: Pearson correlation coefficient, ICC: Intraclass correlation coefficient; Figure S10: Bland-Altman plot of reference and predicted maximum diameters of solid components with the proposed model for an external test dataset; Table S1: DSCs for three types of lung tumors (solid, part-solid, pure GGO) and all types using dense V-Net for internal test dataset A and external test dataset; Table S2: Hyper-parameters for Dense V-Net. Conceptualization: H.A. and Y.T. ; data curation: Y.S., T.Y., Y.C., T.K. and H.A. ; formal analysis: Y.T., H.A. and R.W. ; funding acquisition: H.A. ; investigation: [PERSON], Y.C. and H.A. ; methodology: Y.T., H.A. and R.W. ; project administration: H.A. ; resources: Y.T., H.A., T.Y., Y.S. and H.Y. ; software: Y.T. and Y.C. ; supervision: H.A. ; validation: Y.C., H.A., T.Y., Y.S. and H.Y. ; visualization: Y.C. and H.A. ; writing--original draft: Y.C., H.A. and R.W. All authors have read and agreed to the published version of the manuscript. This study was partially supported by JST SPRING Grant Number JPMJSP2136 and JSPS KAKENHI Grant (Number: JP24K10840). The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board of Kyushu University Hospital (protocol code 2020-472, 2 November 2020). Patient consent was waived due to the retrospective nature of the study. Restrictions apply to the datasets: the datasets presented in this article are not readily available because they belong to Kyushu University Hospital. Requests to access the datasets should be directed to Kyushu University Hospital.