{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T12:26:06Z","timestamp":1784204766865,"version":"3.55.0"},"reference-count":17,"publisher":"Springer Science and Business Media LLC","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,2,11]]},"DOI":"10.7759\/cureus.103403","type":"journal-article","created":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T10:16:12Z","timestamp":1770804972000},"source":"Crossref","is-referenced-by-count":1,"title":["Dosimetric Impact of Artificial Intelligence (AI)-Based Autocontouring Software, OncoStudio, in High-Risk Prostate Cancer Treatment Planning: A Three-Group Comparative Study on the Slice Ranges of Seminal Vesicles"],"prefix":"10.7759","author":[{"given":"Masumi","family":"Kawaguchi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Masataka","family":"Hoshina","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shinji","family":"Sugahara","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kousuke","family":"Fujioka","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Masato","family":"Takanashi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Masaya","family":"Noguchi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ayaka","family":"Oosato","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kouichi","family":"Masuda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yoshiaki","family":"Katada","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kazuhiro","family":"Saito","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2016.09.009","article-title":"Uncertainties in volume delineation in radiation oncology: a systematic review and recommendations for future studies","volume":"121","author":"Vinod SK","year":"2016","unstructured":"Vinod SK, Jameson MG, Min M, Holloway LC. Uncertainties in volume delineation in radiation oncology: a systematic review and recommendations for future studies. Radiother Oncol. 2016, 121:169-79. 10.1016\/j.radonc.2016.09.009","journal-title":"Radiother Oncol"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/s0167-8140(98)00021-8","article-title":"Intra- and inter-observer variability in contouring prostate and seminal vesicles: implications for conformal treatment planning","volume":"47","author":"Fiorino C","year":"1998","unstructured":"Fiorino C, Reni M, Bolognesi A, Cattaneo GM, Calandrino R. Intra- and inter-observer variability in contouring prostate and seminal vesicles: implications for conformal treatment planning. Radiother Oncol. 1998, 47:285-92. 10.1016\/s0167-8140(98)00021-8","journal-title":"Radiother Oncol"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2005.06.034","article-title":"Reduction of observer variation using matched CT-PET for lung cancer delineation: a three-dimensional analysis","volume":"64","author":"Steenbakkers RJ","year":"2006","unstructured":"Steenbakkers RJ, Duppen JC, Fitton I, et al.. Reduction of observer variation using matched CT-PET for lung cancer delineation: a three-dimensional analysis. Int J Radiat Oncol Biol Phys. 2006, 64:435-48. 10.1016\/j.ijrobp.2005.06.034","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1002\/mp.12045","article-title":"Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks","volume":"44","author":"Ibragimov B","year":"2017","unstructured":"Ibragimov B, Xing L. Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks. Med Phys. 2017, 44:547-57. 10.1002\/mp.12045","journal-title":"Med Phys"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1002\/mp.12602","article-title":"Automatic segmentation of the clinical target volume and organs at risk in the planning CT for rectal cancer using deep dilated convolutional neural networks","volume":"44","author":"Men K","year":"2017","unstructured":"Men K, Dai J, Li Y. Automatic segmentation of the clinical target volume and organs at risk in the planning CT for rectal cancer using deep dilated convolutional neural networks. Med Phys. 2017, 44:6377-89. 10.1002\/mp.12602","journal-title":"Med Phys"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2019.10.019","article-title":"Comparing deep learning-based auto-segmentation of organs at risk and clinical target volumes to expert inter-observer variability in radiotherapy planning","volume":"144","author":"Wong J","year":"2020","unstructured":"Wong J, Fong A, McVicar N, et al.. Comparing deep learning-based auto-segmentation of organs at risk and clinical target volumes to expert inter-observer variability in radiotherapy planning. Radiother Oncol. 2020, 144:152-8. 10.1016\/j.radonc.2019.10.019","journal-title":"Radiother Oncol"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2017.11.012","article-title":"Clinical evaluation of atlas and deep learning based automatic contouring for lung cancer","volume":"126","author":"Lustberg T","year":"2018","unstructured":"Lustberg T, van Soest J, Gooding M, et al.. Clinical evaluation of atlas and deep learning based automatic contouring for lung cancer. Radiother Oncol. 2018, 126:312-7. 10.1016\/j.radonc.2017.11.012","journal-title":"Radiother Oncol"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2018.08.015","article-title":"A systematic post-QUANTEC review of tolerance doses for late toxicity after prostate cancer radiation therapy","volume":"102","author":"Olsson CE","year":"2018","unstructured":"Olsson CE, Jackson A, Deasy JO, Thor M. A systematic post-QUANTEC review of tolerance doses for late toxicity after prostate cancer radiation therapy. Int J Radiat Oncol Biol Phys. 2018, 102:1514-32. 10.1016\/j.ijrobp.2018.08.015","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1186\/s13014-014-0288-1","article-title":"Optimal contouring of seminal vesicle for definitive radiotherapy of localized prostate cancer: comparison between EORTC prostate cancer radiotherapy guideline, RTOG0815 protocol and actual anatomy","volume":"9","author":"Qi X","year":"2014","unstructured":"Qi X, Gao XS, Asaumi J, et al.. Optimal contouring of seminal vesicle for definitive radiotherapy of localized prostate cancer: comparison between EORTC prostate cancer radiotherapy guideline, RTOG0815 protocol and actual anatomy. Radiat Oncol. 2014, 9:288. 10.1186\/s13014-014-0288-1","journal-title":"Radiat Oncol"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2018.01.014","article-title":"ESTRO ACROP consensus guideline on CT- and MRI-based target volume delineation for primary radiation therapy of localized prostate cancer","volume":"127","author":"Salembier C","year":"2018","unstructured":"Salembier C, Villeirs G, De Bari B, et al.. ESTRO ACROP consensus guideline on CT- and MRI-based target volume delineation for primary radiation therapy of localized prostate cancer. Radiother Oncol. 2018, 127:49-61. 10.1016\/j.radonc.2018.01.014","journal-title":"Radiother Oncol"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/s0360-3016(02)03011-0","article-title":"Treatment of prostate cancer with radiotherapy: should the entire seminal vesicles be included in the clinical target volume?","volume":"54","author":"Kestin LL","year":"2002","unstructured":"Kestin LL, Goldstein NS, Vicini FA, Yan D, Korman HJ, Martinez AA. Treatment of prostate cancer with radiotherapy: should the entire seminal vesicles be included in the clinical target volume?. Int J Radiat Oncol Biol Phys. 2002, 54:686-97. 10.1016\/s0360-3016(02)03011-0","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2009.07.1754","article-title":"Use of normal tissue complication probability models in the clinic","volume":"76","author":"Marks LB","year":"2010","unstructured":"Marks LB, Yorke ED, Jackson A, et al.. Use of normal tissue complication probability models in the clinic. Int J Radiat Oncol Biol Phys. 2010, 76:S10-9. 10.1016\/j.ijrobp.2009.07.1754","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2008.08.002","article-title":"RTOG GU radiation oncology specialists reach consensus on pelvic lymph node volumes for high-risk prostate cancer","volume":"74","author":"Lawton CA","year":"2009","unstructured":"Lawton CA, Michalski J, El-Naqa I, et al.. RTOG GU radiation oncology specialists reach consensus on pelvic lymph node volumes for high-risk prostate cancer. Int J Radiat Oncol Biol Phys. 2009, 74:383-7. 10.1016\/j.ijrobp.2008.08.002","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1955.10501294","article-title":"A multiple comparison procedure for comparing several treatments with a control","volume":"50","author":"Dunnett CW","year":"1955","unstructured":"Dunnett CW. A multiple comparison procedure for comparing several treatments with a control. J Am Stat Assoc. 1955, 50:1096-121. 10.1080\/01621459.1955.10501294","journal-title":"J Am Stat Assoc"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.4324\/9780203771587","article-title":"Statistical Power Analysis for the Behavioral Sciences","author":"Cohen J","year":"1988","unstructured":"Cohen J. Statistical Power Analysis for the Behavioral Sciences. Lawrence Erlbaum Associates, Hillsdale, NJ; 1988. 10.4324\/9780203771587"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.prro.2020.05.013","article-title":"Clinical evaluation of deep learning and atlas-based auto-contouring of bladder and rectum for prostate radiation therapy","volume":"11","author":"Zabel WJ","year":"2021","unstructured":"Zabel WJ, Conway JL, Gladwish A, et al.. Clinical evaluation of deep learning and atlas-based auto-contouring of bladder and rectum for prostate radiation therapy. Pract Radiat Oncol. 2021, 11:e80-9. 10.1016\/j.prro.2020.05.013","journal-title":"Pract Radiat Oncol"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1186\/s13014-022-01985-9","article-title":"Dosimetric impact of deep learning-based CT auto-segmentation on radiation therapy treatment planning for prostate cancer","volume":"17","author":"Kawula M","year":"2022","unstructured":"Kawula M, Purice D, Li M, et al.. Dosimetric impact of deep learning-based CT auto-segmentation on radiation therapy treatment planning for prostate cancer. Radiat Oncol. 2022, 17:21. 10.1186\/s13014-022-01985-9","journal-title":"Radiat Oncol"}],"container-title":["Cureus"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.cureus.com\/articles\/462146-dosimetric-impact-of-artificial-intelligence-ai-based-autocontouring-software-oncostudio-in-high-risk-prostate-cancer-treatment-planning-a-three-group-comparative-study-on-the-slice-ranges-of-seminal-vesicles","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T10:16:13Z","timestamp":1770804973000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cureus.com\/articles\/462146-dosimetric-impact-of-artificial-intelligence-ai-based-autocontouring-software-oncostudio-in-high-risk-prostate-cancer-treatment-planning-a-three-group-comparative-study-on-the-slice-ranges-of-seminal-vesicles"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,11]]},"references-count":17,"URL":"https:\/\/doi.org\/10.7759\/cureus.103403","relation":{},"ISSN":["2168-8184"],"issn-type":[{"value":"2168-8184","type":"print"}],"subject":[],"published":{"date-parts":[[2026,2,11]]}}}