Han Jang Hee, Kim Byung Woo, Kim Taek Min, Ko Ji Yeon, Choi Seung Jae, Kang Minho, Kim Sang Youn, Cho Jeong Yeon, Ku Ja Hyeon, Kwak Cheol, Kim Young-Gon, Jeong Chang Wook
Department of Urology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea.
Department of Urology, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea.
BMC Cancer. 2025 Jan 29;25(1):173. doi: 10.1186/s12885-025-13582-6.
To develop and test the performance of a fully automated system for classifying renal tumor subtypes via deep machine learning for automated segmentation and classification.
The model was developed using computed tomography (CT) images of pathologically proven renal tumors collected from a prospective cohort at a medical center between March 2016 and December 2020. A total of 561 renal tumors were included: 233 clear cell renal cell carcinomas (RCCs), 82 papillary RCCs, 74 chromophobe RCCs, and 172 angiomyolipomas. Renal tumor masks manually drawn on contrast-enhanced CT images were used to develop a 3D U-Net-based deep learning model for fully automated tumor segmentation. After segmentation, the entire classification pipeline, including feature extraction and subtype classification, was conducted without any manual intervention. Both conventional radiological features (Hounsfield units, HUs) and radiomic features extracted from areas predicted by the deep learning models were used to develop an algorithm for classifying renal tumor subtypes via a random forest classifier. The performance of the segmentation model was evaluated using the Dice similarity coefficient, while the classification model was assessed based on accuracy, sensitivity, and specificity.
For tumors larger than 4 cm, the Dice similarity coefficient (DSC) for automated segmentation was 0.83, while for tumors smaller than 4 cm, the DSC was 0.65. The classification accuracy (ACC) for distinguishing RCC subtypes was 0.77 for tumors larger than 4 cm and 0.68 for tumors smaller than 4 cm. Additionally, the accuracy for benign versus malignant classification was 0.85.
Our automatic segmentation and classifier model showed promising results for renal tumor segmentation and classification.
开发并测试一个通过深度机器学习进行自动分割和分类来对肾肿瘤亚型进行分类的全自动系统的性能。
该模型是使用2016年3月至2020年12月期间从一家医疗中心的前瞻性队列中收集的经病理证实的肾肿瘤的计算机断层扫描（CT）图像开发的。共纳入561例肾肿瘤：233例透明细胞肾细胞癌（RCC）、82例乳头状RCC、74例嫌色性RCC和172例肾血管平滑肌脂肪瘤。在增强CT图像上手动绘制的肾肿瘤掩码用于开发基于3D U-Net的深度学习模型以进行全自动肿瘤分割。分割后，整个分类流程，包括特征提取和亚型分类，均在无任何人工干预的情况下进行。传统放射学特征（亨氏单位，HUs）和从深度学习模型预测区域提取的放射组学特征均用于开发一种通过随机森林分类器对肾肿瘤亚型进行分类的算法。分割模型的性能使用骰子相似系数进行评估，而分类模型则根据准确性、敏感性和特异性进行评估。
对于大于4cm的肿瘤，自动分割的骰子相似系数（DSC）为0.83，而对于小于4cm的肿瘤，DSC为0.65。区分RCC亚型的分类准确率（ACC）对于大于4cm的肿瘤为0.77，对于小于4cm的肿瘤为0.68。此外，良性与恶性分类的准确率为0.85。
我们的自动分割和分类器模型在肾肿瘤分割和分类方面显示出了有前景的结果。
