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Van De Steene, J., Linthout, N., De Mey, J., Vinh-Hung, V., Claassens, C., Noppen, M., Bel, A., & Storme, G. (2002). Definition of gross tumor volume in lung cancer: Inter-observer variability. Radiotherapy and Oncology , 62 (1), 37–49. https://doi.org/10.1016/S0167-8140(01)004 53-4 Vinod, S. K., Jameson, M. G., Min, M., & Holloway, L. C. (2016). Uncertainties in volume delineation in radiation oncology: A systematic review and recommendations for future studies. Radiotherapy and Oncology , 121 (2), 169–179. https://doi.org/10.1016/j.radonc.2016.09.00 9 Wang, T.-W., Hong, J.-S., Huang, J.-W., Liao, C.-Y., Lu, C.-F., & Wu, Y.-T. (2024). Systematic review and meta-analysis of deep learning applications in computed tomography lung cancer segmentation. Radiotherapy and Oncology , 197 , 110344. https://doi.org/10.1016/j.radonc.2024.1103 44 Wee, L., Aerts, H. J., Kalendralis, P., & Dekker, A. (2019). Data from NSCLC-Radiomics-Interobserver1 (Version 3) [Dataset]. The Cancer Imaging Archive. https://doi.org/10.7937/TCIA.2019.CWVL PD26 Wendler, T., Kreissl, M. C., Schemmer, B., Rogasch, J. M. M., & De Benetti, F. (2023). Artificial Intelligence-powered automatic volume calculation in medical images – available tools, performance and challenges for nuclear medicine. Nuklearmedizin - NuclearMedicine , 62 (06), 343–353. https://doi.org/10.1055/a-2200-2145 Wesdorp, N. J., Bolhuis, K., Roor, J., Van Waesberghe, J.-H. T. M., Van Dieren, S.,

Van Amerongen, M. J., Chapelle, T., Dejong, C. H. C., Engelbrecht, M. R. W., Gerhards, M. F., Grunhagen, D., Van Gulik, T. M., Hermans, J. J., De Jong, K. P., Klaase, J. M., Liem, M. S. L., Van Lienden, K. P., Molenaar, I. Q., Patijn, G. A., … Kazemier, G. (2021). The Prognostic Value of Total Tumor Volume Response Compared With RECIST1.1 in Patients With Initially Unresectable Colorectal Liver Metastases Undergoing Systemic Treatment. Annals of Surgery Open , 2 (4), e103. https://doi.org/10.1097/AS9.000000000000 0103 Wu, W., Gao, L., Duan, H., Huang, G., Ye, X., & Nie, S. (2020). Segmentation of pulmonary nodules in CT images based on 3D ‐ UNET combined with three ‐ dimensional conditional random field optimization. Medical Physics , 47 (9), 4054–4063. https://doi.org/10.1002/mp.14248 Yoon, S. H., Kim, J., Lee, K. J., Nam, C.-M., Kim, J., Lee, K. H., & Lee, K. W. (2022). Volumetric analysis of pulmonary nodules: Reducing the discrepancy between the diameter-based volume calculation and voxel-counting method. Quantitative Imaging in Medicine and Surgery , 12 (3), 1674–1683. https://doi.org/10.21037/qims-21-485 Yue, Y., Li, N., Shahid, H., Bi, D., Liu, X., Song, S., & Ta, D. (2022). Gross Tumor Volume Definition and Comparative Assessment for Esophageal Squamous Cell Carcinoma From 3D 18F-FDG PET/CT by Deep Learning-Based Method. Frontiers in Oncology , 12 , 799207. https://doi.org/10.3389/fonc.2022.799207

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