  Background: Prostate cancer (PC) MRI-based risk calculators are commonly
based 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 significant
PC (csPC) remain to be explored. Purpose: To obtain and evaluate Prostate Age
Gap (PAG) as an MRI marker tool for csPC risk. Study type: Retrospective.
Population: A total of 7243 prostate MRI slices from 468 participants who had
undergone prostate biopsies. A deep learning model was trained on 3223 MRI
slices 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 the
time of the visit and used to train the deep learning model to predict the age
of the patient. Following, we obtained PAG, defined as the model predicted age
minus the patient's chronological age. Multivariate logistic regression models
were used to estimate the association through odds ratio (OR) and predictive
value 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 of
clinically significant PC (csPC, Gleason score >=7) (OR =3.78, 95% confidence
interval (CI):2.32-6.16, P <.001). PAG showed a better predictive ability when
compared to PI-RADS>=3 and adjusted by other risk factors, including PSA
levels: AUC =0.981 vs AUC =0.704, p<.001. Conclusion: PAG was significantly
associated with the risk of clinically significant PC and outperformed other
well-established PC risk factors.

Computer Vision and Pattern Recognition