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Development and internal validation of a noninvasive predictive model based on iron and fat measurements for pathologic risk stratification in prostate cancer

   
@article{TAU155656,
	author = {Ziwei Li and Yunshu Zhao and Mengying Zhu and Zhen Tian and Shuting Han and Yonggang Li and Guangzheng Li},
	title = {Development and internal validation of a noninvasive predictive model based on iron and fat measurements for pathologic risk stratification in prostate cancer},
	journal = {Translational Andrology and Urology},
	volume = {15},
	number = {7},
	year = {2026},
	keywords = {},
	abstract = {Background: 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.Methods: 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},
	issn = {2223-4691},	url = {https://tau.amegroups.org/article/view/155656}
}