{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,5]],"date-time":"2026-11-05T22:01:52Z","timestamp":1793916112749,"version":"4.8.0"},"reference-count":16,"publisher":"Oxford University Press (OUP)","license":[{"start":{"date-parts":[[2022,6,25]],"date-time":"2022-06-25T00:00:00Z","timestamp":1656115200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>The object in this study is to develop an artificial intelligence-based deep learning algorithm for prediction of time to castration-resistant prostate cancer by combined androgen blockade therapy in metastatic hormone-na\u00efve prostate cancer. We included 180 metastatic hormone-na\u00efve prostate cancer patients who initially received combined androgen blockade. We first evaluated whether time to castration-resistant prostate cancer was a significant prognostic factor. Then, using the patients\u2019 needle-biopsy specimen images, we developed and validated our deep learning algorithm. The results are shown below. First, we confirmed that time to castration-resistant prostate cancer correlated with overall survival (P\u00a0&amp;lt;\u00a00.001). Next, we selected two groups by time to castration-resistant prostate cancer of &amp;gt;24\u00a0months (n\u00a0=\u00a018) and &amp;lt;6\u00a0months (n\u00a0=\u00a06) and developed a deep learning algorithm by artificial intelligence-based machine deep learning. In 16 other metastatic hormone-na\u00efve prostate cancer patients used as an external validation set, we confirmed the prediction accuracy remained significant (P\u00a0&amp;lt;\u00a00.05). In conclusion, our obtained deep learning algorithm has high predictive ability for the effectiveness of combined androgen blockade.<\/jats:p>","DOI":"10.1093\/jjco\/hyac089","type":"journal-article","created":{"date-parts":[[2022,6,24]],"date-time":"2022-06-24T22:30:54Z","timestamp":1656109854000},"source":"Crossref","is-referenced-by-count":11,"title":["Pilot study of an artificial intelligence-based deep learning algorithm to predict time to castration-resistant prostate cancer for metastatic hormone-na\u00efve prostate cancer"],"prefix":"10.1093","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3953-6534","authenticated-orcid":false,"given":"Wataru","family":"Nakata","sequence":"first","affiliation":[{"name":"Department of Urology, Osaka Rosai Hospital , Osaka , Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hideo","family":"Mori","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Pathology, Osaka Rosai Hospital , Osaka , Japan"},{"name":"Department of Urology, Osaka Rosai Hospital , Osaka , Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Goh","family":"Tsujimura","sequence":"additional","affiliation":[{"name":"Department of Urology, Osaka Rosai Hospital , Osaka , Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuichi","family":"Tsujimoto","sequence":"additional","affiliation":[{"name":"Department of Urology, Osaka Rosai Hospital , Osaka , Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Takayoshi","family":"Gotoh","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Pathology, Osaka Rosai Hospital , Osaka , Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Masao","family":"Tsujihata","sequence":"additional","affiliation":[{"name":"Department of Urology, Osaka Rosai Hospital , Osaka , Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2022,6,25]]},"reference":[{"key":"2022062422304040000_ref1","doi-asserted-by":"crossref","first-page":"1080","DOI":"10.1200\/JCO.2017.75.3657","article-title":"Chemohormonal therapy in metastatic hormone-sensitive prostate cancer: long-term survival analysis of the randomized phase III E3805 CHAARTED trial","volume":"36","author":"Kyriakopoulos","year":"2018","journal-title":"J Clin Oncol"},{"key":"2022062422304040000_ref2","doi-asserted-by":"crossref","first-page":"686","DOI":"10.1016\/S1470-2045(19)30082-8","article-title":"Abiraterone acetate plus prednisone in patients with newly diagnosed high-risk metastatic castration-sensitive prostate cancer (LATITUDE): final overall survival analysis of a randomised, double-blind, phase 3 trial","volume":"20","author":"Fizazi","year":"2019","journal-title":"Lancet 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Int"},{"key":"2022062422304040000_ref6","doi-asserted-by":"crossref","first-page":"1536","DOI":"10.1002\/cncr.30503","article-title":"Impact of ethnicity on the outcome of men with metastatic, hormone-sensitive prostate cancer","volume":"123","author":"Bernard","year":"2017","journal-title":"Cancer"},{"key":"2022062422304040000_ref7","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1111\/iju.14135","article-title":"Impact of alternative antiandrogen therapy for Japanese prostate cancer patients in the era of new hormonal therapy","volume":"27","author":"Nakata","year":"2020","journal-title":"Int J Urol"},{"key":"2022062422304040000_ref8","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/S1470-2045(19)30739-9","article-title":"Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study","volume":"21","author":"Bulten","year":"2020","journal-title":"Lancet 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biopsies","volume":"3","author":"Steiner","year":"2020","journal-title":"JAMA Netw Open"},{"key":"2022062422304040000_ref12","article-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan","year":"2014"},{"key":"2022062422304040000_ref13","article-title":"How transferable are features in deep neural networks?","author":"Yosinski","year":"2014"},{"key":"2022062422304040000_ref14","article-title":"Grad-CAM: visual explanations from deep networks via gradient-based localization","author":"Ramprasaath","year":"2016"},{"key":"2022062422304040000_ref15","doi-asserted-by":"crossref","first-page":"5642","DOI":"10.1038\/s41467-019-13647-8","article-title":"Automated acquisition of explainable knowledge from unannotated histopathology images","volume":"10","author":"Yamamoto","year":"2019","journal-title":"Nat 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