Development and Validation of a Deep Learning Algorithm to Automatic Detection of Pituitary Microadenoma From MRI.
Development and Validation of a Deep Learning Algorithm to Automatic Detection of Pituitary Microadenoma From MRI.
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开发和验证 MRI 自动检测垂体微腺瘤的深度学习算法
DOI：
10.3389/fmed.2021.758690
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发表时间：
2021
影响因子：
3.9
通讯作者：
Chen Y
中科院分区：
文献类型：
--
作者：
Li Q;Zhu Y;Chen M;Guo R;Hu Q;Lu Y;Deng Z;Deng S;Zhang T;Wen H;Gao R;Nie Y;Li H;Chen J;Shi G;Shen J;Cheung WW;Liu Z;Guo Y;Chen Y
背景资料：垂体微腺瘤体积较小，解剖结构多变，临床症状复杂，个体差异大，仅通过MRI难以诊断。我们开发并验证了一个基于深度学习的系统来诊断来自MRI的PM。方法：共有11，935名不孕症参与者参加了该项目。在应用排除标准后，纳入了1，520名参与者（556名PM患者和964名对照受试者），以进一步分层为3个非重叠队列。用于训练集的数据来自回顾性研究，在验证数据集中，采用前瞻性时间和地理验证集。共有780名参与者用于培训，195名参与者用于测试，545名参与者用于验证诊断性能。脑垂体计算机辅助诊断系统（PM-CAD）由垂体区域检测和脑垂体诊断两部分组成。使用受试者工作特征（ROC）曲线和ROC曲线下面积（AUC）、校准曲线、准确性、灵敏度、特异性、阳性预测值（PPV）、阴性预测值（NPV）和F1评分测量PM-CAD系统的诊断性能。结果：在测试数据集上，垂体微腺瘤计算机辅助诊断系统的诊断准确率为94.36%，AUC评分为98.13%。我们证实了我们的PM-CAD系统的鲁棒性和通用性，在内部数据集和外部数据集的诊断准确率分别为96.50%和92.26%和92.36%，AUC分别为95.5%，94.7%和93.7%。在人机竞争中，我们的PM-CAD系统的诊断性能与具有>10年专业知识的放射科医生相当（诊断准确率为94.0% vs. 95.0%，AUC为95.6% vs. 95.0%）。对于放射科医生的误诊病例，我们的系统显示了100%的准确诊断。设计了一个基于浏览器的软件来辅助PM诊断。结论：这是第一份报告表明，PM-CAD系统是一个可行的工具，用于检测PM。我们的研究结果表明，PM-CAD系统是适用于放射科，特别是在基层医疗机构。
Background: It is often difficult to diagnose pituitary microadenoma (PM) by MRI alone, due to its relatively small size, variable anatomical structure, complex clinical symptoms, and signs among individuals. We develop and validate a deep learning -based system to diagnose PM from MRI. Methods: A total of 11,935 infertility participants were initially recruited for this project. After applying the exclusion criteria, 1,520 participants (556 PM patients and 964 controls subjects) were included for further stratified into 3 non-overlapping cohorts. The data used for the training set were derived from a retrospective study, and in the validation dataset, prospective temporal and geographical validation set were adopted. A total of 780 participants were used for training, 195 participants for testing, and 545 participants were used to validate the diagnosis performance. The PM-computer-aided diagnosis (PM-CAD) system consists of two parts: pituitary region detection and PM diagnosis. The diagnosis performance of the PM-CAD system was measured using the receiver operating characteristics (ROC) curve and area under the ROC curve (AUC), calibration curve, accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1-score. Results: Pituitary microadenoma-computer-aided diagnosis system showed 94.36% diagnostic accuracy and 98.13% AUC score in the testing dataset. We confirm the robustness and generalization of our PM-CAD system, the diagnostic accuracy in the internal dataset was 96.50% and in the external dataset was 92.26 and 92.36%, the AUC was 95.5, 94.7, and 93.7%, respectively. In human-computer competition, the diagnosis performance of our PM-CAD system was comparable to radiologists with >10 years of professional expertise (diagnosis accuracy of 94.0% vs. 95.0%, AUC of 95.6% vs. 95.0%). For the misdiagnosis cases from radiologists, our system showed a 100% accurate diagnosis. A browser-based software was designed to assist the PM diagnosis. Conclusions: This is the first report showing that the PM-CAD system is a viable tool for detecting PM. Our results suggest that the PM-CAD system is applicable to radiology departments, especially in primary health care institutions.
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作者：
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通讯作者：
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