National Cancer Center(2022.10.5), "Prostate Cancer", National Cancer Center Cancer Information Services. https://ganjoho.jp/reg_stat/statistics/stat/cancer/20_prostate.html

Dose escalation and quality of life in patients with localized prostate cancer treated with radiotherapy: long-term results of the dutch randomized dose-escalation trial (CKTO 96–10 Trial) - Al-Mamgani, A.; Putten, W. L. J.; Wielen, G. J.; Levendag, P. C.; Incrocci, L. (Int J Radiat Oncol Biol Phys, 2011)

What is the minimal dose for resistance exercise effectiveness in prostate cancer patients? Systematic review and meta-analysis on patient-reported outcomes - Lopez, P.; Taaffe, D. R.; Newton, R. U.; Buffart, L. M.; Galvão, D. A. (Prostate Cancer Prostatic Dis, 2021)

Physical exercise and quality of life in patients with prostate cancer systematic review and meta-analysis - Rendeiro, J. A.; Medeiros, C. A.; Rodrigues, P.; Letícia, B. R.; Santiago, R.; Rocha, B. (Support Care Cancer, 2021)

Takeda K, Takai Y, Narazaki K, Mitsuya M, Umezawa R, Kadoya N, et al. Treatment outcome of high-dose image-guided intensity-modulated radiotherapy using intra-prostate fiducial markers for localized prostate cancer at a single institute in Japan. 2012. Available: www.nccn.com

A systematic review of randomised controlled trials of radiotherapy for localised prostate cancer - Wolff, R. F.; Ryder, S.; Bossi, A.; Briganti, A.; Crook, J.; Henry, A. (Eur J Cancer, 2015)

Five-year outcomes after prostatectomy or radiotherapy for prostate cancer: the prostate cancer outcomes study - Potosky, A. L.; Davis, W. W.; Hoffman, R. M.; Stanford, J. L.; Stephenson, R. A.; Penson, D. F. (J Natl Cancer Inst, 2004)

Dosimetric and radiobiological comparison in head-and-neck radiotherapy using JO-IMRT and 3D-CRT - Tai, D. T.; Oanh, L. T.; Phuong, P. H.; Sulieman, A.; Abolaban, F. A.; Omer, H. (Saudi J Biol Sci, 2022)

Prediction of the benefits from dose-escalated hypofractionated intensity-modulated radiotherapy for prostate cancer - Amer, A. M.; Mott, J.; Mackay, R. I.; Williams, P. C.; Livsey, J.; Logue, J. P. (Int J Radiat Oncol Biol Phys, 2003)

Martin JM, Richardson M, Siva S, Cardoso M, Handmer M, Sidhom M. Review Mechanisms, mitigation, and management of urinary toxicity from prostate radiotherapy. 2022. Available: www.thelancet.com/oncology

Clinical predictors of severe late urinary toxicity after curative intensity-modulated radiation therapy for localized prostate cancer - Takeda, K.; Umezawa, R.; Ishikawa, Y.; Yamamoto, T.; Takahashi, N.; Takeda, K. (J Radiat Res, 2021)

Genitourinary quality-of-life comparison between urethral sparing prostate stereotactic body radiation therapy monotherapy and virtual high-dose-rate brachytherapy boost - Richardson, M.; Sidhom, M.; Keall, P.; Leigh, L.; Ball, H.; Bucci, J. (Int J Radiat Oncol Biol Phys, 2023)

Lower urinary tract symptoms and mortality among Finnish men: the roles of symptom severity and bother - Åkerla, J.; Pesonen, J. S.; Pöyhönen, A.; Koskimäki, J.; Häkkinen, J.; Huhtala, H. (J Urol, 2022)

Artificial intelligence for MR image reconstruction: an overview for clinicians - Lin, D. J.; Johnson, P. M.; Knoll, F.; Lui, Y. W. (J Magn Reson Imaging, 2021)

A deep learning-based radiomics approach to predict head and neck tumor regression for adaptive radiotherapy - Tanaka, S.; Kadoya, N.; Sugai, Y.; Umeda, M.; Ishizawa, M.; Katsuta, Y. (Sci Rep, 2022)

Artificial intelligence in lung cancer: current applications and perspectives - Chassagnon, G.; Margerie-Mellon, C.; Vakalopoulou, M.; Marini, R.; Hoang-Thi, T. N.; Revel, M. P. (Jpn J Radiol, 2023)

Image segmentation using deep learning: a survey - Minaee, S.; Boykov, Y.; Porikli, F.; Plaza, A.; Kehtarnavaz, N.; Terzopoulos, D. (IEEE Trans Pattern Anal Mach Intell, 2022)

Deep learning-based methods for prostate segmentation in magnetic resonance imaging - Comelli, A.; Dahiya, N.; Stefano, A.; Vernuccio, F.; Portoghese, M.; Cutaia, G. (Appl Sci (Switzerland), 2021)

Feasibility evaluation of novel AI-based deep-learning contouring algorithm for radiotherapy - Maduro Bustos, L. A.; Sarkar, A.; Doyle, L. A.; Andreou, K.; Noonan, J.; Nurbagandova, D. (J Appl Clin Med Phys, 2023)

Multi-atlas-based segmentation of prostatic urethra from planning CT imaging to quantify dose distribution in prostate cancer radiotherapy - Acosta, O.; Mylona, E.; Dain, M.; Voisin, C.; Lizee, T.; Rigaud, B. (Radiother Oncol, 2017)

Multi-atlas–based auto-segmentation for prostatic urethra using novel prediction of deformable image registration accuracy - Takagi, H.; Kadoya, N.; Kajikawa, T.; Tanaka, S.; Takayama, Y.; Chiba, T. (Med Phys, 2020)

Development of a 3D CNN-based AI Model for automated segmentation of the prostatic urethra - Belue, M. J.; Harmon, S. A.; Patel, K.; Daryanani, A.; Yilmaz, E. C.; Pinto, P. A. (Acad Radiol, 2022)

Deep learning-based segmentation of prostatic urethra on computed tomography scans for treatment planning - Cubero, L.; García-Elcano, L.; Mylona, E.; Boue-Rafle, A.; Cozzarini, C.; Ubeira Gabellini, M. G. (Phys Imaging Radiat Oncol, 2023)

Evaluation of the accuracy of deformable image registration on MRI with a physical phantom - Wu, R. Y.; Liu, A. Y.; Yang, J.; Williamson, T. D.; Wisdom, P. G.; Bronk, L. (J Appl Clin Med Phys, 2020)

3D Slicer as an image computing platform for the quantitative imaging network - Fedorov, A.; Beichel, R.; Kalpathy-Cramer, J.; Finet, J.; Fillion-Robin, J. C.; Pujol, S. (Magn Reson Imaging, 2012)

Assessment of the impact of deformable registration of diagnostic mri to planning ct on gtv delineation for radiotherapy for oropharyngeal carcinoma in routine clinical practice - Taylor, A.; Sen, M.; Prestwich, R. J. D. (Healthcare (Switzerland), 2018)

Evaluation of performance of pelvic CT-MR deformable image registration using two software programs - Ishida, T.; Kadoya, N.; Tanabe, S.; Ohashi, H.; Nemoto, H.; Dobashi, S. (J Radiat Res, 2021)

Results of a multi-institution deformable registration accuracy study (MIDRAS) - Brock, K. K. (Int J Radiat Oncol Biol Phys, 2010)

The urethral position may shift due to urethral catheter placement in the treatment planning for prostate radiation therapy - Dekura, Y.; Nishioka, K.; Hashimoto, T.; Miyamoto, N.; Suzuki, R.; Yoshimura, T. (Radiat Oncol, 2019)