Department of Neurosurgery, Jiangsu University Affiliated People's Hospital, Zhenjiang, 212002, Jiangsu, China.
Department of Neurosurgery, Zhenjiang Clinical Medical College of Nanjing Medical University, Zhenjiang, 212002, Jiangsu, China.
Pituitary. 2020 Jun;23(3):246-252. doi: 10.1007/s11102-020-01032-4.
This study was designed to develop a computer-aided diagnosis (CAD) system based on a convolutional neural network (CNN) to diagnose patients with pituitary tumors.
We included adult patients clinically diagnosed with pituitary adenoma (pituitary adenoma group), or adult individuals without pituitary adenoma (control group). After pre-processing, all the MRI data were randomly divided into training or testing datasets in a ratio of 8:2 to create or evaluate the CNN model. Multiple CNNs with the same structure were applied for different types of MR images respectively, and a comprehensive diagnosis was performed based on the classification results of different types of MR images using an equal-weighted majority voting strategy. Finally, we assessed the diagnostic performance of the CAD system by accuracy, sensitivity, specificity, positive predictive value, and F1 score.
We enrolled 149 participants with 796 MR images and adopted the data augmentation technology to create 7960 new images. The proposed CAD method showed remarkable diagnostic performance with an overall accuracy of 91.02%, sensitivity of 92.27%, specificity of 75.70%, positive predictive value of 93.45%, and F1-score of 92.67% in separate MRI type. In the comprehensive diagnosis, the CAD achieved better performance with accuracy, sensitivity, and specificity of 96.97%, 94.44%, and 100%, respectively.
The CAD system could accurately diagnose patients with pituitary tumors based on MR images. Further, we will improve this CAD system by augmenting the amount of dataset and evaluate its performance by external dataset.
本研究旨在开发一种基于卷积神经网络（CNN）的计算机辅助诊断（CAD）系统，用于诊断垂体肿瘤患者。
我们纳入了临床诊断为垂体腺瘤的成年患者（垂体腺瘤组）或无垂体腺瘤的成年个体（对照组）。对 MRI 数据进行预处理后，将所有 MRI 数据随机分为训练或测试数据集，比例为 8:2，以创建或评估 CNN 模型。对不同类型的 MRI 图像应用相同结构的多个 CNN，并采用等权重多数投票策略基于不同类型的 MRI 图像的分类结果进行综合诊断。最后，通过准确性、敏感性、特异性、阳性预测值和 F1 评分评估 CAD 系统的诊断性能。
我们共纳入了 149 名参与者，共 796 张 MRI 图像，并采用数据增强技术生成了 7960 张新图像。所提出的 CAD 方法具有显著的诊断性能，在单独的 MRI 类型中总体准确率为 91.02%，敏感性为 92.27%，特异性为 75.70%，阳性预测值为 93.45%，F1 得分为 92.67%。在综合诊断中，CAD 的准确率、敏感性和特异性分别为 96.97%、94.44%和 100%，性能更好。
基于 MRI 图像，CAD 系统可准确诊断垂体肿瘤患者。此外，我们将通过增加数据集的数量来改进此 CAD 系统，并通过外部数据集来评估其性能。
