Rojo Domingo Mariluz, Do Deondre D, Conlin Christopher C, Bagrodia Aditya, Barrett Tristan, Baxter Madison T, Cooperberg Matthew, Feng Felix, Hahn Michael E, Harisinghani Mukesh, Hollenberg Gary, Javier-Desloges Juan, Kallis Karoline, Kamran Sophia, Kane Christopher J, Kessler Dimitri, Kuperman Joshua, Lee Kang-Lung, Levine Jonathan, Liss Michael A, Margolis Daniel J A, Matthews Ian, Murphy Paul M, Nakrour Nabih, Ohliger Michael, Ollison Courtney, Osinski Thomas, Pamatmat Anthony James, Pompa Isabella R, Rakow-Penner Rebecca, Roberts Jacob L, Shabaik Ahmed S, Song Yuze, Song David, Tempany Clare M, Trecarten Shaun, Wehrli Natasha, Weinberg Eric P, Woolen Sean, Xu George, Zhong Allison Y, Dale Anders M, Seibert Tyler M
Department of Bioengineering, University of California San Diego, La Jolla, California.
Department of Radiation Medicine, University of California San Diego, La Jolla, California.
J Urol. 2025 Sep;214(3):259-271. doi: 10.1097/JU.0000000000004611. Epub 2025 May 16.
The positive predictive value of the Prostate Imaging Reporting and Data System (PI-RADS) for clinically significant prostate cancer (csPCa, grade group [GG] ≥2) varies widely between radiologists. The restriction spectrum imaging restriction score (RSIrs) is a biophysics-based metric derived from diffusion MRI that could be an objectively interpretable biomarker for csPCa. We aimed to evaluate performance of RSIrs for patient-level detection of csPCa in a large and heterogenous dataset, and to combine RSIrs with clinical and imaging parameters for csPCa detection.
At 7 centers, participants underwent prostate MRI between January 2016 and March 2024. We calculated patient-level csPCa probability based on maximum RSIrs in the prostate and compared patient-level csPCa detection to apparent diffusion coefficient (ADC) and PI-RADS using AUC. We also evaluated csPCa discrimination by GG and combining RSIrs with clinical risk factors through multivariable regression.
Among patients who met the inclusion criteria (n = 1892), probability of csPCa increased with higher RSIrs. Among biopsy-naïve patients (n = 877), AUCs for GG ≥ 2 vs non-csPCa were RSIrs = 0.73 (0.69-0.76), ADC = 0.54 (0.50-0.57), and PI-RADS = 0.75 (0.71-0.78). RSIrs significantly outperformed ADC ( < .01) and was comparable with PI-RADS ( = .31). RSIrs and PI-RADS combined outperformed either alone. The model with RSIrs, PI-RADS, age, and PSA density achieved the best discrimination of csPCa.
RSIrs is an accurate and reliable quantitative biomarker that performs better than conventional ADC and comparably with expert-defined PI-RADS for patient-level detection of csPCa. RSIrs provides objective estimates of probability of csPCa that do not require radiology expertise.
前列腺影像报告和数据系统（PI-RADS）对临床显著前列腺癌（csPCa，分级组[GG]≥2）的阳性预测值在放射科医生之间差异很大。限制性谱成像限制性评分（RSIrs）是一种基于生物物理学的指标，源自扩散磁共振成像（MRI），可能是一种可客观解释的csPCa生物标志物。我们旨在评估RSIrs在一个大型异质性数据集中对患者层面csPCa检测的性能，并将RSIrs与临床和影像参数相结合用于csPCa检测。
在7个中心，参与者于2016年1月至2024年3月期间接受了前列腺MRI检查。我们根据前列腺中的最大RSIrs计算患者层面的csPCa概率，并使用曲线下面积（AUC）将患者层面的csPCa检测与表观扩散系数（ADC）和PI-RADS进行比较。我们还通过多变量回归评估了按GG区分csPCa以及将RSIrs与临床风险因素相结合的情况。
在符合纳入标准的患者（n = 1892）中，csPCa的概率随着RSIrs升高而增加。在未进行活检的患者（n = 877）中，GG≥2与非csPCa的AUC分别为：RSIrs = 0.73（0.69 - 0.76），ADC = 0.54（0.50 - 0.57），PI-RADS = 0.75（0.71 - 0.78）。RSIrs显著优于ADC（P <.01），且与PI-RADS相当（P =.31）。RSIrs和PI-RADS联合使用的表现优于单独使用。包含RSIrs、PI-RADS、年龄和前列腺特异抗原（PSA）密度的模型对csPCa的区分效果最佳。
RSIrs是一种准确可靠的定量生物标志物，在患者层面检测csPCa方面比传统的ADC表现更好，与专家定义的PI-RADS相当。RSIrs提供了csPCa概率的客观估计，无需放射学专业知识。
