超声造影放射组学模型术前预测透明细胞肾细胞癌肿瘤分级的探索性研究。

BACKGROUND: This study aims to explore machine learning(ML) methods for non-invasive assessment of WHO/ISUP nuclear grading in clear cell renal cell carcinoma(ccRCC) using contrast-enhanced ultrasound(CEUS) radiomics. METHODS: This retrospective study included 122 patients diagnosed as ccRCC after surgical resection. They were divided into a training set (n = 86) and a testing set(n = 36). CEUS radiographic features were extracted from CEUS images, and XGBoost ML models (US, CP, and MP model) with independent features at different phases were established. Multivariate regression analysis was performed on the characteristics of different radiomics phases to determine the indicators used for developing the prediction model of the combined CEUS model and establishing the XGBoost model. The training set was used to train the above four kinds of radiomics models, which were then tested in the testing set. Radiologists evaluated tumor characteristics, established a CEUS reading model, and compared the diagnostic efficacy of CEUS reading model with independent characteristics and combined CEUS model prediction models. RESULTS: The combined CEUS radiomics model demonstrated the best performance in the training set, with an area under the curve (AUC) of 0.84, accuracy of 0.779, sensitivity of 0.717, specificity of 0.879, positive predictive value (PPV) of 0.905, and negative predictive value (NPV) of0.659. In the testing set, the AUC was 0.811, with an accuracy of 0.784, sensitivity of 0.783, specificity of 0.786, PPV of 0.857, and NPV of 0.688. CONCLUSIONS: The radiomics model based on CEUS exhibits high accuracy in non-invasive prediction of ccRCC. This model can be utilized for non-invasive detection of WHO/ISUP nuclear grading of ccRCC and can serve as an effective tool to assist clinical decision-making processes.

背景：本研究旨在探讨使用对比增强超声（CEUS）放射组学对透明细胞肾细胞癌（ccRCC）进行非侵入性评估的机器学习（ML）方法，以评估世界卫生组织/国际泌尿系统肿瘤学会（WHO/ISUP）核分级。

方法：这是一项回顾性研究，纳入了 122 名经手术切除后诊断为 ccRCC 的患者。他们被分为训练集（n=86）和测试集（n=36）。从 CEUS 图像中提取 CEUS 影像学特征，并建立具有不同相位独立特征的 XGBoost ML 模型（US、CP 和 MP 模型）。对不同放射组学相位的特征进行多变量回归分析，确定用于开发联合 CEUS 模型预测模型和建立 XGBoost 模型的指标。使用训练集对上述四种放射组学模型进行训练，然后在测试集中进行测试。放射科医生评估肿瘤特征，建立 CEUS 阅读模型，并比较 CEUS 阅读模型与独立特征和联合 CEUS 模型预测模型的诊断效能。

结果：联合 CEUS 放射组学模型在训练集表现最佳，曲线下面积（AUC）为 0.84，准确率为 0.779，敏感度为 0.717，特异度为 0.879，阳性预测值（PPV）为 0.905，阴性预测值（NPV）为 0.659。在测试集中，AUC 为 0.811，准确率为 0.784，敏感度为 0.783，特异度为 0.786，PPV 为 0.857，NPV 为 0.688。

结论：CEUS 放射组学模型在 ccRCC 的非侵入性预测中具有较高的准确性。该模型可用于非侵入性检测 ccRCC 的 WHO/ISUP 核分级，可作为辅助临床决策过程的有效工具。