Zuo Teng, Zheng Yanhua, He Lingfeng, Chen Tao, Zheng Bin, Zheng Song, You Jinghang, Li Xiaoyan, Liu Rong, Bai Junjie, Si Shuxin, Wang Yingying, Zhang Shuyi, Wang Lili, Chen Jianhui
Department of Urology, Fujian Medical University Union Hospital, Fuzhou, China.
Institute for Empirical Social Science Research, Xi'an Jiaotong University, Xi'an, China.
Front Oncol. 2021 Nov 18;11:746750. doi: 10.3389/fonc.2021.746750. eCollection 2021.
This study was conducted in order to design and develop a framework utilizing deep learning (DL) to differentiate papillary renal cell carcinoma (PRCC) from chromophobe renal cell carcinoma (ChRCC) using convolutional neural networks (CNNs) on a small set of computed tomography (CT) images and provide a feasible method that can be applied to light devices.
Training and validation datasets were established based on radiological, clinical, and pathological data exported from the radiology, urology, and pathology departments. As the gold standard, reports were reviewed to determine the pathological subtype. Six CNN-based models were trained and validated to differentiate the two subtypes. A special test dataset generated with six new cases and four cases from The Cancer Imaging Archive (TCIA) was applied to validate the efficiency of the best model and of the manual processing by abdominal radiologists. Objective evaluation indexes [accuracy, sensitivity, specificity, receiver operating characteristic (ROC) curve, and area under the curve (AUC)] were calculated to assess model performance.
The CT image sequences of 70 patients were segmented and validated by two experienced abdominal radiologists. The best model achieved 96.8640% accuracy (99.3794% sensitivity and 94.0271% specificity) in the validation set and 100% (case accuracy) and 93.3333% (image accuracy) in the test set. The manual classification achieved 85% accuracy (100% sensitivity and 70% specificity) in the test set.
This framework demonstrates that DL models could help reliably predict the subtypes of PRCC and ChRCC.
本研究旨在设计并开发一个利用深度学习（DL）的框架，通过卷积神经网络（CNN）在一小批计算机断层扫描（CT）图像上区分乳头状肾细胞癌（PRCC）和嫌色性肾细胞癌（ChRCC），并提供一种可应用于轻型设备的可行方法。
基于从放射科、泌尿科和病理科导出的放射学、临床和病理数据建立训练和验证数据集。作为金标准，对报告进行审查以确定病理亚型。训练并验证了六个基于CNN的模型以区分这两种亚型。使用六个新病例和来自癌症影像存档（TCIA）的四个病例生成的特殊测试数据集来验证最佳模型和腹部放射科医生手动处理的效率。计算客观评估指标[准确率、灵敏度、特异性、受试者工作特征（ROC）曲线和曲线下面积（AUC）]以评估模型性能。
70名患者的CT图像序列由两名经验丰富的腹部放射科医生进行分割和验证。最佳模型在验证集中达到了96.8640%的准确率（灵敏度为99.3794%，特异性为94.0271%），在测试集中达到了100%（病例准确率）和93.3333%（图像准确率）。手动分类在测试集中的准确率为85%（灵敏度为100%，特异性为70%）。
该框架表明DL模型有助于可靠地预测PRCC和ChRCC的亚型。
