随着肾脏病变发生率的增加，治疗前区分良性和恶性病变对于优化治疗至关重要。本研究旨在开发一种机器学习模型，利用从不同感兴趣区域 (ROI) 提取的放射组学特征、肿瘤内生态多样性特征和临床因素对肾脏病变进行分类。 1,795 个肾脏病变的 CT 图像（动脉期），其中三个病变已确诊病理学医院地点按手术日期分为开发组（1184 个，66%）和测试组（611 个，34%）。从动脉期图像的八个 ROI 中提取传统的放射组学特征。瘤内生态多样性特征来自瘤内亚区域。开发了将这些特征与临床因素相结合的组合模型，并将其性能与放射科医生的解释进行了比较。结合瘤内和瘤周放射组学特征以及生态多样性特征，在从 CT 扫描提取的所有特征组合中产生了最高的 AUC 0.929。将临床因素纳入从 CT 图像提取的特征中后，我们的组合模型在整体（AUC = 0.946 vs 0.823，P < 0.001）和小肾脏病变（AUC = 0.935 vs 0.745，P < 0.001）测试中优于放射科医生的解释队列。此外，组合模型表现出良好的一致性，并在超过 60% 的阈值概率上提供了最高的净收益。在整个和小肾脏病变测试队列中，预测风险低于或高于 95% 敏感性和特异性截止值的亚组的 AUC 分别为 0.974 和 0.978。 组合模型结合了瘤内和瘤周放射组学特征、生态多样性特征和临床特征。这些因素在区分良性和恶性肾脏病变方面表现出良好的性能，超过了放射科医生对整个肾脏病变和小肾脏病变的诊断。它有可能使患者免于不必要的侵入性活检/手术，并增强临床决策。© 2024。作者。

With the increasing incidence of renal lesions, pretreatment differentiation between benign and malignant lesions is crucial for optimized management. This study aimed to develop a machine learning model utilizing radiomic features extracted from various regions of interest (ROIs), intratumoral ecological diversity features, and clinical factors to classify renal lesions.CT images (arterial phase) of 1,795 renal lesions with confirmed pathology from three hospital sites were split into development (1184, 66%) and test (611, 34%) cohorts by surgery date. Conventional radiomic features were extracted from eight ROIs of arterial phase images. Intratumoral ecological diversity features were derived from intratumoral subregions. The combined model incorporating these features with clinical factors was developed, and its performance was compared with radiologists' interpretation.Combining intratumoral and peritumoral radiomic features, along with ecological diversity features yielded the highest AUC of 0.929 among all combinations of features extracted from CT scans. After incorporating clinical factors into the features extracted from CT images, our combined model outperformed the interpretation of radiologists in the whole (AUC = 0.946 vs 0.823, P < 0.001) and small renal lesion (AUC = 0.935 vs 0.745, P < 0.001) test cohorts. Furthermore, the combined model exhibited favorable concordance and provided the highest net benefit across threshold probabilities exceeding 60%. In the whole and small renal lesion test cohorts, the AUCs for subgroups with predicted risk below or above 95% sensitivity and specificity cutoffs were 0.974 and 0.978, respectively.The combined model, incorporating intratumoral and peritumoral radiomic features, ecological diversity features, and clinical factors showed good performance for distinguishing benign from malignant renal lesions, surpassing radiologists' diagnoses in both whole and small renal lesions. It has the potential to save patients from unnecessary invasive biopsies/surgeries and to enhance clinical decision-making.© 2024. The Author(s).