Development of an effective predictive screening tool for prostate cancer using the ClarityDX machine learning platform

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00064190%3A_____%2F24%3A10001297" target="_blank" >RIV/00064190:_____/24:10001297 - isvavai.cz</a>

Alternative codes found

RIV/00216208:11120/24:43927263

Result on the web

<a href="https://doi.org/10.1038/s41746-024-01167-9" target="_blank" >https://doi.org/10.1038/s41746-024-01167-9</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1038/s41746-024-01167-9" target="_blank" >10.1038/s41746-024-01167-9</a>

Result language

angličtina

Original language name

Development of an effective predictive screening tool for prostate cancer using the ClarityDX machine learning platform

Original language description

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&apos; 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 &gt;= 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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

30217 - Urology and nephrology

Project

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Continuities

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

NPJ DIGITAL MEDICINE

ISSN

2398-6352

e-ISSN

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Volume of the periodical

7

Issue of the periodical within the volume

1

Country of publishing house

DE - GERMANY

Number of pages

11

Pages from-to

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UT code for WoS article

001252128300001

EID of the result in the Scopus database

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