Radiomics of Periprostatic Fat and Tumor Lesion Based on MRI Predicts the Pathological Upgrading of Prostate Cancer from Biopsy to Radical Prostatectomy

Wen-Qi Liu; Yong Wei; Zhi-Bin Ke; Bin Lin; Xiao-Hui Wu; Xu-Yun Huang; Ze-Jia Chen; Jia-Yin Chen; Shao-Hao Chen; Yu-Ting Xue; Fei Lin; Dong-Ning Chen; Qing-Shui Zheng; Xue-Yi Xue; Ning Xu

doi:10.1016/j.acra.2024.11.043

Radiomics of Periprostatic Fat and Tumor Lesion Based on MRI Predicts the Pathological Upgrading of Prostate Cancer from Biopsy to Radical Prostatectomy

Acad Radiol. 2024 Dec 26:S1076-6332(24)00890-0. doi: 10.1016/j.acra.2024.11.043. Online ahead of print.

Authors

Affiliations

¹ Department of Urology, Urology Research Institute, the First Affiliated Hospital, Fujian Medical University, Fuzhou 35005, China (W-Q.L., Y.W., Z-B.K., B.L., X-H.W., X-Y.H., Z-J.C., J-Y.C., S-H.C., Y-T.X., F.L., D-N.C., Q-S.Z., X-Y.X., N.X.); Department of Urology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou 350212, China (W-Q.L., Y.W., Z-B.K., B.L., X-H.W., X-Y.H., Z-J.C., J-Y.C., S-H.C., Y-T.X., F.L., D-N.C., Q-S.Z., X-Y.X., N.X.).
² Department of Urology, Urology Research Institute, the First Affiliated Hospital, Fujian Medical University, Fuzhou 35005, China (W-Q.L., Y.W., Z-B.K., B.L., X-H.W., X-Y.H., Z-J.C., J-Y.C., S-H.C., Y-T.X., F.L., D-N.C., Q-S.Z., X-Y.X., N.X.); Department of Urology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou 350212, China (W-Q.L., Y.W., Z-B.K., B.L., X-H.W., X-Y.H., Z-J.C., J-Y.C., S-H.C., Y-T.X., F.L., D-N.C., Q-S.Z., X-Y.X., N.X.); Fujian Key Laboratory of Precision Medicine for Cancer, the First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China (X-Y.X., N.X.).
³ Department of Urology, Urology Research Institute, the First Affiliated Hospital, Fujian Medical University, Fuzhou 35005, China (W-Q.L., Y.W., Z-B.K., B.L., X-H.W., X-Y.H., Z-J.C., J-Y.C., S-H.C., Y-T.X., F.L., D-N.C., Q-S.Z., X-Y.X., N.X.); Department of Urology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou 350212, China (W-Q.L., Y.W., Z-B.K., B.L., X-H.W., X-Y.H., Z-J.C., J-Y.C., S-H.C., Y-T.X., F.L., D-N.C., Q-S.Z., X-Y.X., N.X.); Fujian Key Laboratory of Precision Medicine for Cancer, the First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China (X-Y.X., N.X.). Electronic address: drxun@fjmu.edu.cn.

PMID: 39730248
DOI: 10.1016/j.acra.2024.11.043

Abstract

Rationale and objectives: To assess the predictive value of MRI-based radiomics of periprostatic fat (PPF) and tumor lesions for predicting Gleason score (GS) upgrading from biopsy to radical prostatectomy (RP) in prostate cancer (PCa).

Methods: A total of 314 patients with pathologically confirmed prostate cancer (PCa) after radical prostatectomy (RP) were included in the study. The patients were randomly assigned to the training cohort (n = 157) and the validating cohort (n = 157) in a 1:1 ratio. All had pre-surgery MRI followed by transrectal ultrasound-guided prostate biopsy. Radiological features were extracted from T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) sequences for PPF and tumors. Univariate and multivariate logistic regression identified independent clinical risk factors, and a combined model was established by integrating radiomic features of PPF and PCa. Model performance was assessed using receiver operating characteristic (ROC) curves, calibration, and decision curve analysis.

Results: The combined model, incorporating radiomic features of PPF, PCa, and clinical data, predicted GS upgrading from biopsy to RP excellently (AUC=0.925, 95%CI0.872-0.979) in the training cohort. The Hosmer-Lemeshow test confirmed model fit (χ² = 9.316, P = 0.316). The nomogram was validated in the validating cohort; it showed good accuracy (AUC= 0.937, 95% CI, 0.891-0.983) and was well calibrated (χ² = 12.871, P = 0.116). Decision curve analysis indicated good clinical utility of the radiomic nomogram.

Conclusion: The combined model incorporating PPF, PCa, and clinical data showed excellent performance in predicting GS upgrading from biopsy to RP in PCa patients. This offers a novel and reliable noninvasive tool for GS upgrading risk stratification.

Keywords: Gleason score (GS); Pathological upgrading; Periprostatic fat (PPF); Radiomics.