Optimizing Detection of Clinically Significant Prostate Cancer through Nomograms incorporating MRI, Clinical Features, and Advanced Serum Biomarkers

Abstract

Purpose To develop nomograms that predict the detection of clinically significant prostate cancer at diagnostic biopsy based on multiparametric prostate MRI (mpMRI), serum biomarkers, and patient clinicodemographic features. Materials and Methods Nomograms were developed from a cohort of biopsy-naïve men presenting to our 11-hospital system with a PSA of 2-20ng/mL who underwent pre-biopsy mpMRI from March 2018-June 2021 (n = 1494). The outcomes were the presence of clinically significant and high-grade prostate cancer (defined as ≥ GG2 [Grade Group 2] and ≥ GG3 prostate cancer, respectively). Using significant variables on multivariable logistic regression, individual nomograms were developed for men with PSA, % free PSA, or prostate health index (PHI) when available. The nomograms were both internally validated and evaluated in an independent cohort of 366 men presenting to our hospital system from July 2021-February 2022. Results 1031 of 1494 men (69%) underwent biopsy after initial evaluation with mpMRI, 493 (47.8%) of whom were found to have ≥ GG2 PCa, and 271 (26.3%) were found to have ≥ GG3 PCa. Age, race, highest PIRADS score, prostate health index (PHI) when available, % free PSA when available, and PSA density were significant predictors of ≥ GG2 and ≥ GG3 PCa on multivariable analysis and were used for nomogram generation. Accuracy of nomograms in both the training cohort and independent cohort were high, with areas under the curves (AUC) of ≥ 0.885 in the training cohort and ≥ 0.896 in the independent validation cohort. In our independent validation cohort, our model for ≥ GG2 prostate cancer with PHI saved 39.1% of biopsies (143/366) while only missing 0.8% of csPCa (1/124) with a biopsy threshold of 20% probability of csPCa. Conclusions Here we developed nomograms combining serum testing and mpMRI to help clinicians risk stratify patients with elevated PSA of 2-20ng/mL who are being considered for biopsy. Our nomograms are available at https://rossnm1.shinyapps.io/MynMRIskCalculator/ to aid with biopsy decisions.

Development and internal validation of a nomogram predicting significant prostate cancer: Is it clinically applicable in low prevalent prostate cancer countries? A multicenter study

“…In comparison to a strategy of having all the men undergo biopsy, using a probability threshold of 30% resulting from our study as a basis for biopsy to detect csPCa would result in a net decrease of roughly five needless biopsies per 100 men without missing any malignancies. Such a figure is lower than percentages reported in other studies; where Miroslav et al, 25 and Siddiqui et al 34…”

“…In comparison to a strategy of having all the men undergo biopsy, using a probability threshold of 30% resulting from our study as a basis for biopsy to detect csPCa would result in a net decrease of roughly five needless biopsies per 100 men without missing any malignancies. Such a figure is lower than percentages reported in other studies; where Miroslav et al, 25 and Siddiqui et al 34 reported that, at a biopsy threshold of 20%, there is a net reduction of 20% and 39.1% unnecessary biopsies, respectively. The reduction in the number of biopsies performed (5%), although statistically significant, does not appear to be clinically significant.…”

Radiomics from multisite MRI and clinical data to predict clinically significant prostate cancer

“…Common features used in existing risk prediction models for prostate cancer (PCa) are predominantly clinical parameters, such as prostate specific antigen (PSA), PSA density (PSAD), age, and digital rectal examination (DRE) findings (1–3). Incorporating magnetic resonance imaging (MRI) findings (PI-RADS (4)) into risk prediction models may reduce the number of biopsies and diagnosed non-clinically significant PCa (ncsPCa), without compromising high diagnosis rates of clinically significant PCa (csPCa) (5–14).…”