Le Dinh Tuan, Lee Seungeun, Park Hyemin, Lee Sungwon, Choi Hyeondeok, Chun Keum San, Jung Joon-Yong
Visual Analysis and Learning for Improved Diagnostics (VALID) Lab, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul, 06591, Republic of Korea.
Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul, 06591, Republic of Korea.
Sci Rep. 2025 Jul 1;15(1):20389. doi: 10.1038/s41598-025-07128-w.
Classifying chondroid tumors is an essential step for effective treatment planning. Recently, with the advances in computer-aided diagnosis and the increasing availability of medical imaging data, automated tumor classification using deep learning shows promise in assisting clinical decision-making. In this study, we propose a hybrid approach that integrates deep learning and radiomics for chondroid tumor classification. First, we performed tumor segmentation using the nnUNetv2 framework, which provided three-dimensional (3D) delineation of tumor regions of interest (ROIs). From these ROIs, we extracted a set of radiomics features and deep learning-derived features. After feature selection, we identified 15 radiomics and 15 deep features to build classification models. We developed 5 machine learning classifiers including Random Forest, XGBoost, Gradient Boosting, LightGBM, and CatBoost for the classification models. The approach integrating features from radiomics, ROI-originated deep learning features, and clinical variables yielded the best overall classification results. Among the classifiers, CatBoost classifier achieved the highest accuracy of 0.90 (95% CI 0.90-0.93), a weighted kappa of 0.85, and an AUC of 0.91. These findings highlight the potential of integrating 3D U-Net-assisted segmentation with radiomics and deep learning features to improve classification of chondroid tumors.
对软骨样肿瘤进行分类是有效治疗方案规划的关键步骤。近年来，随着计算机辅助诊断技术的进步以及医学影像数据的日益丰富，利用深度学习进行自动肿瘤分类在辅助临床决策方面显示出了潜力。在本研究中，我们提出了一种将深度学习与放射组学相结合的混合方法用于软骨样肿瘤分类。首先，我们使用nnUNetv2框架进行肿瘤分割，该框架提供了肿瘤感兴趣区域（ROI）的三维（3D）轮廓。从这些ROI中，我们提取了一组放射组学特征和深度学习衍生特征。经过特征选择后，我们确定了15个放射组学特征和15个深度特征来构建分类模型。我们为分类模型开发了5种机器学习分类器，包括随机森林、XGBoost、梯度提升、LightGBM和CatBoost。整合放射组学特征、源自ROI的深度学习特征和临床变量的方法产生了最佳的总体分类结果。在这些分类器中，CatBoost分类器的准确率最高，为0.90（95%CI 0.90 - 0.93），加权kappa为0.85，AUC为0.91。这些发现突出了将3D U-Net辅助分割与放射组学和深度学习特征相结合以改善软骨样肿瘤分类的潜力。
