Development of an effective predictive screening tool for prostate cancer using the ClarityDX machine learning platform.
In: NPJ Digital Medicine, Jg. 7 (2024-06-20), Heft 1, S. 1-11
Online
academicJournal
Zugriff:
The current prostate cancer (PCa) screen test, prostate-specific antigen (PSA), has a high sensitivity for PCa but low specificity for high-risk, clinically significant PCa (csPCa), resulting in overdiagnosis and overtreatment of non-csPCa. Early identification of csPCa while avoiding unnecessary biopsies in men with non-csPCa is challenging. We built an optimized machine learning platform (ClarityDX) and showed its utility in generating models predicting csPCa. Integrating the ClarityDX platform with blood-based biomarkers for clinically significant PCa and clinical biomarker data from a 3448-patient cohort, we developed a test to stratify patients' risk of csPCa; called ClarityDX Prostate. When predicting high risk cancer in the validation cohort, ClarityDX Prostate showed 95% sensitivity, 35% specificity, 54% positive predictive value, and 91% negative predictive value, at a ≥ 25% threshold. Using ClarityDX Prostate at this threshold could avoid up to 35% of unnecessary prostate biopsies. ClarityDX Prostate showed higher accuracy for predicting the risk of csPCa than PSA alone and the tested model-based risk calculators. Using this test as a reflex test in men with elevated PSA levels may help patients and their healthcare providers decide if a prostate biopsy is necessary. [ABSTRACT FROM AUTHOR]
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Development of an effective predictive screening tool for prostate cancer using the ClarityDX machine learning platform.
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Autor/in / Beteiligte Person: | Hyndman, M. Eric ; Paproski, Robert J. ; Kinnaird, Adam ; Fairey, Adrian ; Marks, Leonard ; Pavlovich, Christian P. ; Fletcher, Sean A. ; Zachoval, Roman ; Adamcova, Vanda ; Stejskal, Jiri ; Aprikian, Armen ; Wallis, Christopher J. D. ; Pink, Desmond ; Vasquez, Catalina ; Beatty, Perrin H. ; Lewis, John D. |
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Zeitschrift: | NPJ Digital Medicine, Jg. 7 (2024-06-20), Heft 1, S. 1-11 |
Veröffentlichung: | 2024 |
Medientyp: | academicJournal |
ISSN: | 2398-6352 (print) |
DOI: | 10.1038/s41746-024-01167-9 |
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