Liu Wen-Qi, Wei Yong, Ke Zhi-Bin, Lin Bin, Wu Xiao-Hui, Huang Xu-Yun, Chen Ze-Jia, Chen Jia-Yin, Chen Shao-Hao, Xue Yu-Ting, Lin Fei, Chen Dong-Ning, Zheng Qing-Shui, Xue Xue-Yi, Xu Ning
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.).
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.).
Acad Radiol. 2025 Aug;32(8):4607-4620. doi: 10.1016/j.acra.2024.11.043. Epub 2024 Dec 27.
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).
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.
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 (χ = 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 (χ = 12.871, P = 0.116). Decision curve analysis indicated good clinical utility of the radiomic nomogram.
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.
评估基于磁共振成像（MRI）的前列腺周围脂肪（PPF）和肿瘤病变的影像组学特征对预测前列腺癌（PCa）患者从活检到根治性前列腺切除术（RP）时Gleason评分（GS）升级的价值。
本研究纳入了314例根治性前列腺切除术后病理确诊为前列腺癌的患者。患者按1:1比例随机分为训练队列（n = 157）和验证队列（n = 157）。所有患者术前均行MRI检查，随后行经直肠超声引导下前列腺穿刺活检。从PPF和肿瘤的T2加权成像（T2WI）及表观扩散系数（ADC）序列中提取影像学特征。单因素和多因素逻辑回归分析确定独立的临床危险因素，并通过整合PPF和PCa的影像组学特征建立联合模型。采用受试者操作特征（ROC）曲线、校准和决策曲线分析评估模型性能。
在训练队列中，结合PPF、PCa影像组学特征和临床数据的联合模型对从活检到RP时GS升级的预测效果极佳（AUC = 0.925，95%CI 0.872 - 0.979）。Hosmer-Lemeshow检验证实模型拟合良好（χ = 9.316，P = 0.316）。该列线图在验证队列中得到验证；显示出良好的准确性（AUC = 0.937，95%CI，0.891 - 0.983）且校准良好（χ = 12.871，P = 0.116）。决策曲线分析表明影像组学列线图具有良好的临床实用性。
结合PPF、PCa和临床数据的联合模型在预测PCa患者从活检到RP时GS升级方面表现出色。这为GS升级风险分层提供了一种新颖且可靠的非侵入性工具。
