Li Xiaoxia, Guo Yi, Huang Shunfa, Wang Funan, Dai Chenchen, Zhou Jianjun, Lin Dengqiang
Department of Radiology, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, 361015, China.
Department of Radiology, Zhongshan Hospital, Fudan University, No 180, Fenglin Road, Xuhui District, Shanghai, 200032, China.
BMC Med Imaging. 2025 May 16;25(1):167. doi: 10.1186/s12880-025-01715-z.
This study aimed to develop and validate a computed tomography (CT)-based intratumoral and peritumoral radiomics nomogram to improve the stratification of postoperative recurrence risk in patients with localized clear cell renal cell carcinoma (ccRCC).
This two-center study included 447 patients with localized ccRCC. Patients from Center A were randomly split into a training set (n = 281) and an internal validation set (IVS) (n = 114) in a 7:3 ratio, while 52 patients from Center B formed the external validation set (EVS). Radiomics features from preoperative CT were obtained from the internal area of tumor (IAT), the internal and peritumoral areas of the tumor at 3 mm (IPAT 3 mm), and 5 mm (IPAT 5 mm). The least absolute shrinkage and selection operator (LASSO) Cox regression was used to construct a radiomics score to develop radiomics model (RM). A clinical model (CM) was also established using significant clinical factors. Furthermore, a fusion model (FM) was developed by integrating independent predictors from both clinical factors and the radiomics score (Radscore) through multivariate Cox proportional hazards regression. Model performance was assessed with Kaplan-Meier curves, time-dependent area under the curve (time-AUC), Harrell's concordance index (C-index), and decision curve analysis (DCA).
Compared to both the IAT model and the IPAT 3 mm model, the IPAT 5 mm radiomics model demonstrated superior predictive performance for tumor recurrence (C-index: 0.924 vs. 0.915-0.923 in the IVS; 0.952 vs. 0.920-0.944 in the EVS). Therefore, the IPAT 5 mm radiomics score was incorporated into the development of the fusion model. The FM exhibited outstanding predictive accuracy, achieving a C-index of 0.938 in the IVS, significantly outperforming the CM (0.889, P = 0.03). Notably, in the EVS, the RM surpassed both the CM and FM (C-index: 0.952 vs. 0.904-0.940, P > 0.05). Furthermore, decision curve analysis indicated that the FM provided the highest net clinical benefit in the IVS, while both the FM and RM demonstrated substantially greater net benefit than the CM in the EVS.
The radiomics model and the fusion model, which integrate both intratumoral and peritumoral features, offer accurate prediction of recurrence risk in patients with localized ccRCC. These models have the potential to aid in personalized treatment planning, optimized surveillance strategies, and treatment strategies for patients with clear cell renal cell carcinoma.
本研究旨在开发并验证一种基于计算机断层扫描（CT）的肿瘤内及肿瘤周围影像组学列线图，以改善局限性透明细胞肾细胞癌（ccRCC）患者术后复发风险的分层。
这项两中心研究纳入了447例局限性ccRCC患者。中心A的患者按7:3的比例随机分为训练集（n = 281）和内部验证集（IVS）（n = 114），而中心B的52例患者组成外部验证集（EVS）。术前CT的影像组学特征取自肿瘤内部区域（IAT）、肿瘤内部及肿瘤周围3 mm处区域（IPAT 3 mm）和5 mm处区域（IPAT 5 mm）。采用最小绝对收缩和选择算子（LASSO）Cox回归构建影像组学评分以开发影像组学模型（RM）。还使用显著临床因素建立了临床模型（CM）。此外，通过多变量Cox比例风险回归整合临床因素和影像组学评分（Radscore）中的独立预测因子，开发了融合模型（FM）。使用Kaplan-Meier曲线、曲线下时间依赖性面积（time-AUC）、Harrell一致性指数（C指数）和决策曲线分析（DCA）评估模型性能。
与IAT模型和IPAT 3 mm模型相比，IPAT 5 mm影像组学模型对肿瘤复发具有更高的预测性能（IVS中C指数：0.924 vs. 0.915 - 0.923；EVS中0.952 vs. 0.920 - 0.944）。因此，将IPAT 5 mm影像组学评分纳入融合模型开发中。FM表现出出色预测准确性，在IVS中C指数达到0.938，显著优于CM（0.889, P = 0.03）。值得注意的是，在EVS中，RM优于CM和FM（C指数：0.952 vs. 0.904 - 0.940, P > 0.05）。此外, 决策曲线分析表明，FM在IVS中提供了最高的净临床效益，而在EVS中FM和RM均显示出比CM更大的净效益。
整合肿瘤内及肿瘤周围特征的影像组学模型和融合模型能够准确预测局限性ccRCC患者的复发风险, 这些模型有可能辅助局限性透明细胞肾细胞癌患者的个性化治疗规划、优化监测策略及治疗策略。
