Li Xiaoxia, Lin Jinglai, Qi Hongliang, Dai Chenchen, Guo Yi, Lin Dengqiang, Zhou Jianjun
Department of Radiology, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, 361015, China.
Department of Urology, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, 361015, China.
Insights Imaging. 2024 Jul 12;15(1):175. doi: 10.1186/s13244-024-01739-z.
This study aimed to assess the predictive value of radiomics derived from intratumoral and peritumoral regions and to develop a radiomics nomogram to predict preoperative nuclear grade and overall survival (OS) in patients with clear cell renal cell carcinoma (ccRCC).
The study included 395 patients with ccRCC from our institution. The patients in Center A (anonymous) institution were randomly divided into a training cohort (n = 284) and an internal validation cohort (n = 71). An external validation cohort comprising 40 patients from Center B also was included. Computed tomography (CT) radiomics features were extracted from the internal area of the tumor (IAT) and IAT combined peritumoral areas of the tumor at 3 mm (PAT 3 mm) and 5 mm (PAT 5 mm). Independent predictors from both clinical and radiomics scores (Radscore) were used to construct a radiomics nomogram. Kaplan-Meier analysis with a log-rank test was performed to evaluate the correlation between factors and OS.
The PAT 5-mm radiomics model (RM) exhibited exceptional predictive capability for grading, achieving an area under the curves of 0.80, 0.80, and 0.90 in the training, internal validation, and external validation cohorts. The nomogram and RM gained from the PAT 5-mm region were more clinically useful than the clinical model. The association between OS and predicted nuclear grade derived from the PAT 5-mm Radscore and the nomogram-predicted score was statistically significant (p < 0.05).
The CT-based radiomics and nomograms showed valuable predictive capabilities for the World Health Organization/International Society of Urological Pathology grade and OS in patients with ccRCC.
The intratumoral and peritumoral radiomics are feasible and promising to predict nuclear grade and overall survival in patients with clear cell renal cell carcinoma, which can contribute to the development of personalized preoperative treatment strategies.
The multi-regional radiomics features are associated with clear cell renal cell carcinoma (ccRCC) grading and prognosis. The combination of intratumoral and peritumoral 5 mm regional features demonstrated superior predictive performance for grading. The nomogram and radiomics models have a broad range of clinical applications.
本研究旨在评估肿瘤内及瘤周区域的放射组学特征的预测价值，并构建一个放射组学列线图，以预测透明细胞肾细胞癌（ccRCC）患者的术前核分级和总生存期（OS）。
本研究纳入了来自本机构的395例ccRCC患者。中心A（匿名）机构的患者被随机分为训练队列（n = 284）和内部验证队列（n = 71）。还纳入了由中心B的40例患者组成的外部验证队列。从肿瘤内部区域（IAT）以及肿瘤内部区域联合肿瘤周围3 mm（PAT 3 mm）和5 mm（PAT 5 mm）区域提取计算机断层扫描（CT）放射组学特征。来自临床和放射组学评分（Radscore）的独立预测因子用于构建放射组学列线图。采用Kaplan-Meier分析和对数秩检验来评估各因素与总生存期之间的相关性。
PAT 5 mm放射组学模型（RM）在分级方面表现出卓越的预测能力，在训练队列、内部验证队列和外部验证队列中的曲线下面积分别为0.80、0.80和0.90。从PAT 5 mm区域获得的列线图和RM比临床模型在临床上更有用。PAT 5 mm Radscore预测的核分级以及列线图预测分数与总生存期之间的关联具有统计学意义（p < 0.05）。
基于CT的放射组学和列线图在预测ccRCC患者的世界卫生组织/国际泌尿病理学会分级和总生存期方面显示出有价值的预测能力。
肿瘤内和瘤周放射组学对于预测透明细胞肾细胞癌患者的核分级和总生存期是可行且有前景的，这有助于制定个性化的术前治疗策略。
多区域放射组学特征与透明细胞肾细胞癌（ccRCC）分级及预后相关。肿瘤内和瘤周5 mm区域特征的组合在分级方面表现出卓越的预测性能。列线图和放射组学模型具有广泛的临床应用。
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