Bang Seokhwan, Wang Heehwan, Bae Hoyoung, Hong Sung-Hoo, Cha Jiook, Choi Moon Hyung
Department of Urology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea.
Interdisciplinary Program in Artificial Intelligence, Seoul National University, Seoul 08826, Republic of Korea.
Diagnostics (Basel). 2025 May 28;15(11):1365. doi: 10.3390/diagnostics15111365.
Differentiating histologic subtypes of fat-poor small renal masses using conventional imaging remains difficult due to their overlapping radiologic characteristics. We aimed to develop a machine learning-based diagnostic model using CT-derived radiomic features to classify the five most common renal tumor subtypes: clear cell RCC (ccRCC), papillary RCC (pRCC), chromophobe RCC (chRCC), angiomyolipoma (AML), and oncocytoma. A total of 499 patients with pathologically confirmed renal tumors who underwent preoperative contrast-enhanced CT and nephrectomy were retrospectively analyzed. We extracted and analyzed radiomic features from 1548 multi-phase CT scans from 499 patients, focusing on fat-poor tumors. Five machine learning classifiers including Linear SVM, Rbf SVM, Random Forest, and XGBoost were involved. Among the models, XGBoost showed the best classification performance, with an average AU-PRC: mean = 0.757, standard error = 0.033 and a renal angiomyolipoma-specific AU-ROC: mean = 0.824, standard error = 0.023. These results outperformed other single-phase CT radiomic feature-based machine learning models trained with 20% of principal components. This study demonstrates the effectiveness of radiomics-based machine learning in classifying renal tumor subtypes and highlights the potential of AI in medical imaging. The findings, particularly the utility of single-phase CT and feature optimization, offer valuable insights for future precision medicine approaches. Such methods may support more personalized diagnosis and treatment planning in renal oncology.
由于乏脂性小肾肿块的放射学特征相互重叠，使用传统成像方法区分其组织学亚型仍然很困难。我们旨在开发一种基于机器学习的诊断模型，利用CT衍生的放射组学特征对五种最常见的肾肿瘤亚型进行分类：透明细胞肾细胞癌（ccRCC）、乳头状肾细胞癌（pRCC）、嫌色细胞肾细胞癌（chRCC）、肾血管平滑肌脂肪瘤（AML）和嗜酸细胞瘤。对499例经病理证实的肾肿瘤患者进行了回顾性分析，这些患者均接受了术前对比增强CT检查和肾切除术。我们从499例患者的1548次多期CT扫描中提取并分析了放射组学特征，重点关注乏脂性肿瘤。涉及了包括线性支持向量机、径向基函数支持向量机、随机森林和XGBoost在内的五种机器学习分类器。在这些模型中，XGBoost表现出最佳的分类性能，平均曲线下面积-精确率曲线（AU-PRC）：均值=0.757，标准误差=0.033，肾血管平滑肌脂肪瘤特异性曲线下面积-受试者工作特征曲线（AU-ROC）：均值=0.824，标准误差=0.023。这些结果优于其他基于单相CT放射组学特征且使用20%主成分训练的机器学习模型。本研究证明了基于放射组学的机器学习在肾肿瘤亚型分类中的有效性，并突出了人工智能在医学成像中的潜力。这些发现，特别是单相CT的实用性和特征优化，为未来的精准医学方法提供了有价值的见解。此类方法可能支持肾肿瘤学中更个性化的诊断和治疗规划。
