Alimu Parehe, Fang Chen, Han Yingnan, Dai Jun, Xie Chunmei, Wang Jiyong, Mao Yongxin, Chen Yunmeng, Yao Lu, Lv Chuanfeng, Xu Danfeng, Xie Guotong, Sun Fukang
Department of Urology, Ruijin Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai, China.
Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinses Medicine, Shanghai, China.
Quant Imaging Med Surg. 2023 Apr 1;13(4):2675-2687. doi: 10.21037/qims-22-539. Epub 2023 Mar 22.
Functional adrenal tumors (FATs) are mainly diagnosed by biochemical analysis. Traditional imaging tests have limitations and cannot be used alone to diagnose FATs. In this study, we aimed to establish an artificially intelligent diagnostic model based on computed tomography (CT) images to distinguish different types of FATs.
A cohort study of 375 patients diagnosed with hyperaldosteronism (HA), Cushing's syndrome (CS), and pheochromocytoma in our center between March 2015 and June 2020 was conducted. Retrospectively, patients were randomly divided into three data sets: the training set (270 cases), the testing set (60 cases), and the retrospective trial set (45 cases). An artificially intelligent diagnostic model based on CT images was established by transferring data from the training set into the deep learning network. The testing set was then used to evaluate the accuracy of the model compared to that of physicians' judgments. The retrospective trial set was used to evaluate the quantification and distinction performance.
The deep learning model achieved an average area under the receiver operating characteristic (ROC) curve (AUC) of 0.915, and the AUCs in all three FAT types were greater than 0.882. The AUC of the model tested on the retrospective dataset reached above 0.849. In the quantitative evaluation of tumor lesion area recognition, the diagnostic model also obtained a segmentation Dice coefficient of 0.69. With the help of the proposed model, clinicians reached 92.5% accuracy in distinguishing FATs, compared to 80.6% accuracy when using only their judgment (P<0.05).
The result of our study shows that the diagnostic model based on a deep learning network can distinguish and quantify three common FAT types based on texture features of contrast-enhanced CT images. The model can quantify and distinguish functional tumors without any endocrine tests and can assist clinicians in the diagnostic procedure.
功能性肾上腺肿瘤（FATs）主要通过生化分析进行诊断。传统的影像学检查存在局限性，不能单独用于诊断FATs。在本研究中，我们旨在建立一种基于计算机断层扫描（CT）图像的人工智能诊断模型，以区分不同类型的FATs。
对2015年3月至2020年6月在我们中心诊断为原发性醛固酮增多症（HA）、库欣综合征（CS）和嗜铬细胞瘤的375例患者进行队列研究。回顾性地将患者随机分为三个数据集：训练集（270例）、测试集（60例）和回顾性试验集（45例）。通过将训练集的数据传输到深度学习网络中，建立基于CT图像的人工智能诊断模型。然后使用测试集评估该模型与医生判断相比的准确性。回顾性试验集用于评估定量和区分性能。
深度学习模型的平均受试者操作特征曲线（ROC）下面积（AUC）为0.915，所有三种FAT类型的AUC均大于0.882。在回顾性数据集中测试的模型的AUC达到0.849以上。在肿瘤病变面积识别的定量评估中，诊断模型的分割Dice系数也达到了0.69。在该模型的帮助下，临床医生区分FATs的准确率达到92.5%，而仅靠他们自己判断时的准确率为80.6%（P<0.05）。
我们的研究结果表明，基于深度学习网络的诊断模型可以根据增强CT图像的纹理特征区分和量化三种常见的FAT类型。该模型无需任何内分泌检查即可对功能性肿瘤进行量化和区分，并可在诊断过程中协助临床医生。
