Analytical Validation of MyProstateScore 2.0

<b>Background/Objectives</b>: Prostate cancer (PCa) is a leading cause of cancer-related deaths among men, with early detection playing a crucial role in improving outcomes. MyProstateScore 2.0 (MPS2), a novel urinary biomarker test, predicts clinically significant PCa to reduce invasive biopsy procedures. This study evaluates the analytical performance of MPS2 using both a post-digital rectal exam (DRE) and non-DRE urine samples. <b>Methods</b>: We assessed the reproducibility, precision, and detection limits of the eighteen MPS2 analytes. Analytical parameters including the linear range, upper and lower limits of quantification (ULOQ and LLOQ), and interference from substances commonly present in urine were evaluated. The reproducibility of the MPS2 scores was evaluated across post-DRE and non-DRE clinical urine samples. <b>Results</b>: MPS2 analytes demonstrated high linearity (R<sup>2</sup> ≥ 0.975) across defined quantification ranges, with PCR efficiencies of 97-105%. The limits of detection (LOD) ranged from 40 to 160 copies/reaction, while the ULOQ was determined to be 10<sup>6</sup>-10<sup>7</sup> copies/reaction for each analyte. Precision studies showed intra-run, inter-run, and inter-instrument standard deviations ≤0.5 Crt. Among the 12 potential interfering substances, only whole blood affected the performance of MPS2. The reproducibility of the MPS2 scores was consistent across post-DRE and non-DRE urine samples, meeting the acceptance criteria. <b>Conclusions</b>: The analytical validation confirms that MPS2 is robust and reliable in detecting biomarkers for clinically significant PCa. These findings, coupled with previous clinical validations, support the clinical use of MPS2 as a non-invasive diagnostic tool.

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Publication Details

Journal
Diagnostics
Published
2025-04-03
DOI
https://doi.org/10.3390/diagnostics15070923
Citations
1
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
1.36
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article

Analytical Validation of MyProstateScore 2.0

Jacob I. Meyers, Tabea M. Schatz, Cameron J. Seitz, Rachel Botbyl et al.
1 citations
Diagnostics
Radiomics and Machine Learning in Medical Imaging
1.36
article

Analytical Validation of MyProstateScore 2.0

Jacob I. Meyers, Tabea M. Schatz, Cameron J. Seitz, Rachel Botbyl, Bradley S. Moore, Bill G. Crafts, Spencer Heaton, John R. Kitchen
article en
1 citations

Abstract

<b>Background/Objectives</b>: Prostate cancer (PCa) is a leading cause of cancer-related deaths among men, with early detection playing a crucial role in improving outcomes. MyProstateScore 2.0 (MPS2), a novel urinary biomarker test, predicts clinically significant PCa to reduce invasive biopsy procedures. This study evaluates the analytical performance of MPS2 using both a post-digital rectal exam (DRE) and non-DRE urine samples. <b>Methods</b>: We assessed the reproducibility, precision, and detection limits of the eighteen MPS2 analytes. Analytical parameters including the linear range, upper and lower limits of quantification (ULOQ and LLOQ), and interference from substances commonly present in urine were evaluated. The reproducibility of the MPS2 scores was evaluated across post-DRE and non-DRE clinical urine samples. <b>Results</b>: MPS2 analytes demonstrated high linearity (R<sup>2</sup> ≥ 0.975) across defined quantification ranges, with PCR efficiencies of 97-105%. The limits of detection (LOD) ranged from 40 to 160 copies/reaction, while the ULOQ was determined to be 10<sup>6</sup>-10<sup>7</sup> copies/reaction for each analyte. Precision studies showed intra-run, inter-run, and inter-instrument standard deviations ≤0.5 Crt. Among the 12 potential interfering substances, only whole blood affected the performance of MPS2. The reproducibility of the MPS2 scores was consistent across post-DRE and non-DRE urine samples, meeting the acceptance criteria. <b>Conclusions</b>: The analytical validation confirms that MPS2 is robust and reliable in detecting biomarkers for clinically significant PCa. These findings, coupled with previous clinical validations, support the clinical use of MPS2 as a non-invasive diagnostic tool.

DiagnosticsVol. 15(7)
Ann Arbor Center for Independent Living (US)
Openalex Percentile: Top 22%
Radiomics and Machine Learning in Medical Imaging
1.36
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