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<jats:bold>Objective:<\/jats:bold>\n                      We aim to develop a non-invasive biomarker test, the simplified MPS2 (sMPS2), which achieves similar state-of-the-art accuracy as MPS2 for predicting high-grade PCa but requires substantially fewer genes than the 18-gene MPS2 to improve its accessibility for routine clinical care.\n                      <jats:bold>Methods:<\/jats:bold>\n                      We grounded the development of sMPS2 in the Predictability, Computability, and Stability (PCS) framework for veridical data science. Under this framework, we stress-tested the development of sMPS2 across various data preprocessing and modeling choices and developed a stability-driven PCS ranking procedure for selecting the most predictive and robust genes for use in sMPS2.\n                      <jats:bold>Results:<\/jats:bold>\n                      The final sMPS2 model consisted of 7 genes and achieved a 0.784 AUROC (95% confidence interval, 0.742\u20130.825) for predicting high-grade PCa on a blinded external validation cohort. This is only 2.3% lower than the 18-gene MPS2, which is similar in magnitude to the 1\u20132% in uncertainty induced by different data preprocessing choices.\n                      <jats:bold>Conclusions:<\/jats:bold>\n                      The 7-gene sMPS2 provides a unique opportunity to expand the reach and adoption of non-invasive PCa screening.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/18758592241308755","type":"journal-article","created":{"date-parts":[[2025,3,20]],"date-time":"2025-03-20T04:52:43Z","timestamp":1742446363000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["A simplified MyProstateScore2.0 for high-grade prostate cancer"],"prefix":"10.1177","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8079-6867","authenticated-orcid":false,"given":"Tiffany M","family":"Tang","sequence":"first","affiliation":[{"name":"Department of Statistics, 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