Zhang Yaodan, Li Xubin, Zhao Jinkun, Ye Zhaoxiang
Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, State Key Laboratory of Druggability Evaluation and Systematic Translational Medicine, Key Laboratory of Cancer Prevention and Therapy, Huan-Hu-Xi Road, Ti-Yuan-Bei, Hexi District, Tianjin, 300060, PR China.
World J Urol. 2025 Aug 9;43(1):480. doi: 10.1007/s00345-025-05864-2.
To assess the predictive value of quantitative parameters derived from conventional MRI for determining the pathological WHO/ISUP grade in patients with clear cell renal cell carcinoma (ccRCC) before surgery, and to construct a nomogram based on these parameters.
This study analyzed ccRCC patients who underwent preoperative abdominal multi-sequence MRI, dynamic contrast-enhanced MRI, and nephrectomy at our hospital. Patients were pathologically classified into low-grade (WHO/ISUP 1/2) and high-grade (WHO/ISUP 3/4) groups. Information on clinical characteristics and quantitative MR parameters was collected. Multivariate logistic regression analyses were performed to create a nomogram incorporating the quantitative MR parameters with statistical significance to preoperatively predict the pathological grade of ccRCC. The area under the curve (AUC) was used to assess the nomogram's predictive performance.
Binary univariate and multivariate logistic regression analyses identified maximum tumor diameter, ADC value, and corticomedullary enhancement as independent predictors of high-grade ccRCC. The quantitative MRI-based nomogram demonstrated high predictive performance, with an AUC of 0.936 (95% confidence interval [CI]: 0.901-0.972). What's more, we found an ADC value of 1.47 × 10mm/s and a corticomedullary enhancement value of 0.90 were determined to be the optimal cut-off values, yielding the highest Youden index for differentiating between low-grade and high-grade ccRCC. The calibration curve and the Hosmer-Lemeshow test revealed that the predicted probability of the quantitative-MR nomogram had a good fitness (χ2 = 12.542, p = 0.129).
The quantitative MR-based nomogram demonstrated excellent performance in the preoperative prediction of pathological WHO/ISUP grade in ccRCC.
评估常规MRI得出的定量参数对术前确定透明细胞肾细胞癌（ccRCC）患者病理WHO/ISUP分级的预测价值，并基于这些参数构建列线图。
本研究分析了在我院接受术前腹部多序列MRI、动态对比增强MRI及肾切除术的ccRCC患者。患者病理上分为低级别（WHO/ISUP 1/2）和高级别（WHO/ISUP 3/4）组。收集临床特征及定量MR参数信息。进行多因素逻辑回归分析以创建包含具有统计学意义的定量MR参数的列线图，用于术前预测ccRCC的病理分级。曲线下面积（AUC）用于评估列线图的预测性能。
二元单因素和多因素逻辑回归分析确定肿瘤最大直径、表观扩散系数（ADC）值及皮髓质强化为高级别ccRCC的独立预测因素。基于定量MRI的列线图显示出较高的预测性能，AUC为0.936（95%置信区间[CI]：0.901 - 0.972）。此外，我们发现ADC值为1.47×10⁻³mm²/s及皮髓质强化值为0.90被确定为最佳截断值，在区分低级别和高级别ccRCC时产生最高的约登指数。校准曲线和Hosmer-Lemeshow检验显示，定量MR列线图的预测概率具有良好的拟合度（χ² = 12.542，p = 0.129）。
基于定量MR的列线图在术前预测ccRCC的病理WHO/ISUP分级方面表现出色。
