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Background: Prostate cancer (PC) MRI-based risk calculators are commonlybased on biological (e.g. PSA), MRI markers (e.g. volume), and patient age.Whilst patient age measures the amount of years an individual has existed,biological age (BA) might better reflect the physiology of an individual.However, surrogates from prostate MRI and linkage with clinically significantPC (csPC) remain to be explored. Purpose: To obtain and evaluate Prostate AgeGap (PAG) as an MRI marker tool for csPC risk. Study type: Retrospective.Population: A total of 7243 prostate MRI slices from 468 participants who hadundergone prostate biopsies. A deep learning model was trained on 3223 MRIslices cropped around the gland from 81 low-grade PC (ncsPC, Gleason score <=6)and 131 negative cases and tested on the remaining 256 participants.Assessment: Chronological age was defined as the age of the participant at thetime of the visit and used to train the deep learning model to predict the ageof the patient. Following, we obtained PAG, defined as the model predicted ageminus the patient's chronological age. Multivariate logistic regression modelswere used to estimate the association through odds ratio (OR) and predictivevalue of PAG and compared against PSA levels and PI-RADS>=3. Statistical tests:T-test, Mann-Whitney U test, Permutation test and ROC curve analysis. Results:The multivariate adjusted model showed a significant difference in the odds ofclinically significant PC (csPC, Gleason score >=7) (OR =3.78, 95% confidenceinterval (CI):2.32-6.16, P <.001). PAG showed a better predictive ability whencompared to PI-RADS>=3 and adjusted by other risk factors, including PSAlevels: AUC =0.981 vs AUC =0.704, p<.001. Conclusion: PAG was significantlyassociated with the risk of clinically significant PC and outperformed otherwell-established PC risk factors.

Prostate Age Gap (PAG): An MRI surrogate marker of aging for prostate cancer detection