{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T03:42:47Z","timestamp":1772163767061,"version":"3.50.1"},"reference-count":29,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2021,5,5]],"date-time":"2021-05-05T00:00:00Z","timestamp":1620172800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>The ability to predict the individual outcomes of clinical trials could support the development of tools for precision medicine and improve the efficiency of clinical-stage drug development. However, there are no published attempts to predict individual outcomes of clinical trials for cancer. We used machine learning (ML) to predict individual responses to a two-year course of bicalutamide, a standard treatment for prostate cancer, based on data from three Phase III clinical trials (n = 3653). We developed models that used a merged dataset from all three studies. The best performing models using merged data from all three studies had an accuracy of 76%. The performance of these models was confirmed by further modeling using a merged dataset from two of the three studies, and a separate study for testing. Together, our results indicate the feasibility of ML-based tools for predicting cancer treatment outcomes, with implications for precision oncology and improving the efficiency of clinical-stage drug development.<\/jats:p>","DOI":"10.3390\/a14050147","type":"journal-article","created":{"date-parts":[[2021,5,5]],"date-time":"2021-05-05T11:06:01Z","timestamp":1620212761000},"page":"147","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Machine Learning Predicts Outcomes of Phase III Clinical Trials for Prostate Cancer"],"prefix":"10.3390","volume":"14","author":[{"given":"Felix D.","family":"Beacher","sequence":"first","affiliation":[{"name":"Cool Clinical Consortium for AI and Clinical Science, 1092 Lausanne, Switzerland"}]},{"given":"Lilianne R.","family":"Mujica-Parodi","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY 11790, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4810-906X","authenticated-orcid":false,"given":"Shreyash","family":"Gupta","sequence":"additional","affiliation":[{"name":"Cool Clinical Consortium for AI and Clinical Science, 1092 Lausanne, Switzerland"}]},{"given":"Leonardo A.","family":"Ancora","sequence":"additional","affiliation":[{"name":"Cool Clinical Consortium for AI and Clinical Science, 1092 Lausanne, Switzerland"},{"name":"Faculty of Medicine, University of Lisbon, 1649-028 Lisbon, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2657","DOI":"10.1016\/j.jacc.2017.03.571","article-title":"Artificial Intelligence in Precision Cardiovascular Medicine","volume":"69","author":"Krittanawong","year":"2017","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1142\/S2339547818300020","article-title":"The growing role of precision and personalized medicine for cancer treatment","volume":"6","author":"Krzyszczyk","year":"2018","journal-title":"Technology"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1002\/bsl.672","article-title":"Cues they use: Clinicians\u2019 endorsement of risk cues in predictions of dangerousness","volume":"24","author":"Odeh","year":"2006","journal-title":"Behav. Sci. Law"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1080\/23808993.2017.1380516","article-title":"The role of artificial intelligence in precision medicine","volume":"2","author":"Mesko","year":"2017","journal-title":"Expert Rev. Precis. Med. Drug Dev."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1038\/s41582-020-0377-8","article-title":"Applications of machine learning to diagnosis and treatment of neurodegenerative diseases","volume":"16","author":"Myszczynska","year":"2020","journal-title":"Nat. Rev. Neurol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1093\/ehjqcco\/qcv005","article-title":"Big biomedical data and cardiovascular disease research: Opportunities and challenges","volume":"1","author":"Denaxas","year":"2015","journal-title":"Eur. Hear. J. Qual. Care Clin. Outcomes"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"S14","DOI":"10.1038\/d41586-020-00847-2","article-title":"Another set of eyes for cancer diagnostics Artificial intelligence\u2019s ability to detect subtle patterns could help physi-cians to identify cancer types and refine risk prediction","volume":"579","author":"Savage","year":"2020","journal-title":"Nature"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"906","DOI":"10.1038\/s41380-018-0106-5","article-title":"Treatment response prediction and individualized identification of first-episode drug-na\u00efve schizophrenia using brain functional connectivity","volume":"25","author":"Cao","year":"2018","journal-title":"Mol. Psychiatry"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2330","DOI":"10.1017\/S003329171800315X","article-title":"A machine learning ensemble to predict treatment outcomes following an Internet intervention for depression","volume":"49","author":"Pearson","year":"2018","journal-title":"Psychol. Med."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"11707","DOI":"10.1038\/s41598-017-11817-6","article-title":"Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models","volume":"7","author":"Yousefi","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"e17653","DOI":"10.1200\/jco.2014.32.15_suppl.e17653","article-title":"Piloting IBM Watson Oncology within Memorial Sloan Kettering\u2019s regional network","volume":"32","author":"Zauderer","year":"2014","journal-title":"J. Clin. Oncol."},{"key":"ref_12","unstructured":"Strickland, E. (2021, March 16). How IBM Watson Overpromised and Underdelivered on A.I. Health Care-IEEE Spectrum. Available online: https:\/\/spectrum.ieee.org\/biomedical\/diagnostics\/how-ibm-watson-overpromised-and-underdelivered-on-ai-health-care."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"E2970","DOI":"10.1073\/pnas.1717139115","article-title":"Predicting cancer outcomes from histology and genomics using convolutional networks","volume":"115","author":"Mobadersany","year":"2018","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1162","DOI":"10.1016\/j.patcog.2008.08.011","article-title":"Exploring feature-based approaches in PET images for predicting cancer treatment outcomes","volume":"42","author":"Grigsby","year":"2009","journal-title":"Pattern Recognit."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/S2215-0366(15)00471-X","article-title":"Cross-trial prediction of treatment outcome in depression: A machine learning approach","volume":"3","author":"Chekroud","year":"2016","journal-title":"Lancet Psychiatry"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.jhealeco.2016.01.012","article-title":"Innovation in the pharmaceutical industry: New estimates of R&D costs","volume":"47","author":"DiMasi","year":"2016","journal-title":"J. Health Econ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1038\/d41573-019-00074-z","article-title":"Trends in clinical success rates and therapeutic focus","volume":"18","author":"Dowden","year":"2019","journal-title":"Nat. Rev. Drug Discov."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1016\/j.tips.2019.05.005","article-title":"Artificial Intelligence for Clinical Trial Design","volume":"40","author":"Harrer","year":"2019","journal-title":"Trends Pharmacol. Sci."},{"key":"ref_19","unstructured":"(2021, May 01). Machine-Learning-Derived Enrichment Markers in Clinical Trials. Available online: https:\/\/isctm.org\/public_access\/Feb2020\/Presentation\/Millis-Presentation.pdf."},{"key":"ref_20","first-page":"3866","article-title":"The worldwide epidemiology of prostate cancer: Perspectives from autopsy studies","volume":"15","author":"Haas","year":"2008","journal-title":"Can. J. Urol."},{"key":"ref_21","first-page":"S3","article-title":"Androgen Deprivation Therapy in the Treatment of Advanced Prostate Cancer","volume":"9","author":"Lepor","year":"2007","journal-title":"Rev. Urol."},{"key":"ref_22","first-page":"833","article-title":"Androgen-Deprivation Therapy in Prostate Cancer and Cardiovascular Risk","volume":"121","author":"Levine","year":"2010","journal-title":"CA Cancer J. Clin."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1074","DOI":"10.1111\/j.1464-410X.2010.09319.x","article-title":"Antiandrogen monotherapy in patients with localized or locally advanced prostate cancer: Final results from the bicalutamide Early Prostate Cancer programme at a median follow-up of 9.7 years","volume":"105","author":"Iversen","year":"2010","journal-title":"BJU Int."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"133","DOI":"10.12954\/PI.14054","article-title":"Correlation and diagnostic performance of the prostate-specific antigen level with the diagnosis, aggressiveness, and bone metastasis of prostate cancer in clinical practice","volume":"2","author":"Lojanapiwat","year":"2014","journal-title":"Prostate Int."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"570","DOI":"10.1093\/jjco\/hyy060","article-title":"Two years of bicalutamide monotherapy in patients with biochemical relapse after radical prostatectomy","volume":"48","author":"Okubo","year":"2018","journal-title":"Jpn. J. Clin. Oncol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1007\/BF01542223","article-title":"The GRISS: A psychometric instrument for the assessment of sexual dysfunction","volume":"15","author":"Rust","year":"1986","journal-title":"Arch. Sex. Behav."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Le, N.Q.K., Do, D.T., Chiu, F.-Y., Yapp, E.K.Y., Yeh, H.-Y., and Chen, C.-Y. (2020). XGBoost Improves Classification of MGMT Promoter Methylation Status in IDH1 Wildtype Glioblastoma. J. Pers. Med., 10.","DOI":"10.3390\/jpm10030128"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1027","DOI":"10.3233\/JAD-190262","article-title":"Optimizing Machine Learning Methods to Improve Predictive Models of Alzheimer\u2019s Disease","volume":"71","author":"Ezzati","year":"2019","journal-title":"J. Alzheimer\u2019s Dis."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1216","DOI":"10.1056\/NEJMp1606181","article-title":"Predicting the Future\u2014Big Data, Machine Learning, and Clinical Medicine","volume":"375","author":"Obermeyer","year":"2016","journal-title":"New Engl. J. Med."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/14\/5\/147\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:57:12Z","timestamp":1760162232000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/14\/5\/147"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,5]]},"references-count":29,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2021,5]]}},"alternative-id":["a14050147"],"URL":"https:\/\/doi.org\/10.3390\/a14050147","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,5]]}}}