Development and internal validation of a noninvasive predictive model based on iron and fat measurements for...

Pathological risk stratification of prostate cancer (PCa) guides treatment decisions. Preoperative noninvasive assessment of PCa risk stratification holds promise for reducing unnecessary invasive biopsies. Elevated levels of iron and fat, along with metabolic disorders in PCa significantly correlate with tumor proliferation and aggressiveness, yet its predictive value in risk stratification remains unclear. We aimed to noninvasively measure fat content as well as iron deposition of PCa lesions by multiparametric magnetic resonance imaging (mpMRI) and investigate their effectiveness in predicting PCa risk.

We prospectively collected patients suspected of PCa with preoperative MRI from 2019 to 2022, and ultimately included 109 pathologically confirmed PCa patients. The Gleason score (GS) and International Society of Urological Pathology grade group (ISUP GG) were determined by two uropathologists who evaluated independently and reached a consensus. Patients were stratified based on the ISUP GG, with 42 in the pathological low-risk (PL) group (ISUP GG ≤2; 69.9±6.08 years), 67 in the pathological high-risk (PH) group (ISUP GG ≥3; 71.82±5.86 years). We also collected clinical, pathologic, and imaging data from the patients. Based on the variables screened by Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, an improved fusion (IF) model was established and visualized with a nomogram plot. The conventional fusion (CF) model was constructed by removing the non-conventional image variables in the IF model. Model performance was evaluated using 10-fold cross-validation, receiver operating characteristic (ROC) analysis, DeLong test, and decision curve analysis (DCA). P<0.05 was considered statistically significant.

Significant differences were observed in the prostate-specific antigen (PSA), prostate volume (PV), Prostate Imaging Reporting and Data System (PI-RADS) scores, fat fraction (FF), T2*, and average apparent diffusion coefficient (ADC) values of the lesions between the two groups. These variables were selected to construct the IF model. The CF model was developed by removing FF and T2* values. The IF model demonstrated higher accuracy than the CF model [IF model: sensitivity =0.952, specificity =0.761, area under the curve (AUC) =0.920; CF model: sensitivity =0.762, specificity =0.761, AUC =0.819; DeLong test: P=0.002, <0.05].

mpMRI‑derived FF and T2* values were significantly associated with ISUP GG in PCa. Intergrating FF and T2* values with ADC, PI-RADS, PSA and PV may predict pathological risk classification more effectively.