Wang Xueyi, Yang Youchang, Wu Jiaojiao, Tang Xiaoqiang, Wang Yao
Department of Radiology, Wujin Hospital Affiliated with Jiangsu University 2 The Wujin Clinical college of Xuzhou Medical University, Changzhou, China.
Department of Radiology, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Qingdao, China.
Front Oncol. 2025 Jun 6;15:1576956. doi: 10.3389/fonc.2025.1576956. eCollection 2025.
To investigate the value of using imaging histological models to non-invasively assess the risk of metastasis in patients with clear cell renal cell carcinoma (ccRCC).
This study retrospectively enrolled 273 clear cell renal cell carcinoma (ccRCC) patients from three hospitals, with 57 cases allocated as an independent test cohort. High-throughput imaging histomic features (n=2,264) were extracted from triphasic CT (non-enhanced, corticomedullary, and nephrographic phases) using Pyradiomics. Three monophasic radiomics models were developed following dimensionality reduction, with feature contributions quantified via Shapley Additive exPlanations (SHAP) framework to enhance interpretability. A triphasic radiomics model was subsequently established by ensembling phase-specific prediction probabilities. Metastasis risk factors identified through univariate/multivariate logistic regression informed a clinical predictor model. The final combined model integrated triphasic radiomics signatures with clinical parameters, visualized through a nomogram. Diagnostic performance was evaluated via ROC analysis, while calibration curves validated prediction consistency.
In this study, SHAP analysis revealed that radiomics features quantifying intratumoral heterogeneity (e.g., necrosis patterns in medullary-phase CT) synergized with clinical factors (tumor size >3 cm, creatinine levels) to drive predictions. Key biological insights included threshold effects of necrosis volume (linked to hypoxia) and tumor diameter (critical threshold: 3 cm), aligning with known metastatic pathways. The clinical model achieved an area under the ROC curve (AUROC) of 0.752 (95% confidence interval [CI]: 0.679-0.826) in the training dataset and 0.681 (95% CI: 0.529-0.833) in the testing dataset. Among the single-phase radiomics models, the CT_Medullary model demonstrated good prediction performance, with an AUROC of 0.785 (95% CI: 0.645-0.924) in the testing dataset. The three-phased CT model exhibited improved diagnostic performance, with a testing AUROC rising to 0.812 (95% CI: 0.680-0.943). Notably, the combined model integrating clinical and radiomics features yielded the best prediction, achieving a further improvement in testing AUROC to 0.824 (95% CI: 0.704-0.944).
Radiomics technology provides a quantitative, objective method for predicting the risk of metastasis in patients with ccRCC. Nonetheless, the clinical indicators persist as irreplaceable.
探讨使用成像组织学模型对透明细胞肾细胞癌（ccRCC）患者转移风险进行无创评估的价值。
本研究回顾性纳入了来自三家医院的273例透明细胞肾细胞癌（ccRCC）患者，其中57例被分配为独立测试队列。使用Pyradiomics从三相CT（非增强期、皮质髓质期和肾实质期）中提取高通量成像组织组学特征（n = 2264）。在降维后建立了三个单相放射组学模型，并通过Shapley加性解释（SHAP）框架对特征贡献进行量化，以提高可解释性。随后通过整合特定阶段的预测概率建立了三相放射组学模型。通过单因素/多因素逻辑回归确定的转移风险因素为临床预测模型提供了依据。最终的联合模型将三相放射组学特征与临床参数相结合，并通过列线图进行可视化。通过ROC分析评估诊断性能，同时校准曲线验证预测一致性。
在本研究中，SHAP分析表明，量化肿瘤内异质性的放射组学特征（例如髓质期CT中的坏死模式）与临床因素（肿瘤大小>3 cm、肌酐水平）协同作用以推动预测。关键的生物学见解包括坏死体积（与缺氧相关）和肿瘤直径的阈值效应（临界阈值：3 cm），这与已知的转移途径一致。临床模型在训练数据集中的ROC曲线下面积（AUROC）为0.752（95%置信区间[CI]：0.679 - 0.826），在测试数据集中为0.681（95% CI：0.529 - 0.833）。在单相放射组学模型中，CT_髓质期模型表现出良好的预测性能，在测试数据集中的AUROC为0.785（95% CI：0.645 - 0.924）。三相CT模型表现出更好的诊断性能，测试AUROC升至0.812（95% CI：0.680 - 0.943）。值得注意的是，整合临床和放射组学特征的联合模型产生了最佳预测，测试AUROC进一步提高至0.824（95% CI：0.704 - 0.944）。
放射组学技术为预测ccRCC患者的转移风险提供了一种定量、客观的方法。尽管如此，临床指标仍然是不可替代的。
