Development and Evaluation of Deep Learning-based Automated Segmentation of Pituitary Adenoma in Clinical Task
Development and Evaluation of Deep Learning-based Automated Segmentation of Pituitary Adenoma in Clinical Task
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DOI：
10.1210/clinem/dgab371
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发表时间：
2021-06-01
影响因子：
5.8
通讯作者：
Wang, Renzhi
中科院分区：
文献类型：
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作者：
Wang, He;Zhang, Wentai;Wang, Renzhi
背景：垂体腺瘤（PA）的切除方案需要术前观察鞍区。放射组学预测需要高质量的分割。手工描绘既耗时又易变。目的：本工作旨在建立一种鞍区自动分割方法，几种工具来提取与侵袭性相关的特征，并通过预测肿瘤一致性来评估其临床应用价值。方法：纳入在北京协和医院诊断为垂体腺瘤的患者。建立了一种称为gate -shaped U-net （GSU-Net）的深度卷积神经网络，用于自动将销售区划分为8类。从分割结果中提取5个磁共振成像特征，包括肿瘤直径、体积、视交叉高度、Knosp分级系统、颈内动脉接触程度。通过肿瘤一致性的诊断准确性来评价所提出方法的临床应用价值。结果：163例确诊垂体腺瘤患者作为第一组，随机分为训练数据集131例，测试数据集32例。50例确诊肢端肥大症患者作为第二组。垂体腺瘤重要影像切片的Dice系数为0.940。所提出的方法在预测5种与侵入性相关的MRI特征方面达到了80%以上的准确率。自动分割的方法比原始方法性能更好，临床模型和放射组学模型的曲线下面积分别为0.840和0.920。结论：该方法能够自动分割鞍区，提取特征，准确率高。肿瘤一致性预测的突出表现表明该方法在支持神经外科医生在术前判断患者病情、预测预后和其他下游任务方面具有临床实用性。
Context: The resection plan of pituitary adenoma (PA) needs preoperative observation of the sellar region. Radiomics prediction requires high-quality segmentations. Manual delineation is time-consuming and subject to rater variability.Objective: This work aims to create an automated segmentation method for the sellar region, several tools to extract invasiveness-related features, and evaluate their clinical usefulness by predicting the tumor consistency.Methods: Patients included were diagnosed with pituitary adenoma at Peking Union Medical College Hospital. A deep convolutional neural network, called gated-shaped U-net (GSU-Net), was created to automatically segment the sellar region into 8 classes. Five magnetic resonance imaging (MRI) features were extracted from the segmentation results, including tumor diameters, volume, optic chiasma height, Knosp grading system, and degree of internal carotid artery contact. The clinical usefulness of the proposed methods was evaluated by the diagnostic accuracy of the tumor consistency.Results: A total of 163 patients with confirmed pituitary adenoma were included as the first group and were randomly divided into a training data set and test data set (131 and 32 patients, respectively). Fifty patients with confirmed acromegaly were included as the second group. The Dice coefficient of pituitary adenoma in important image slices was 0.940. The proposed methods achieved accuracies of more than 80% for the prediction of 5 invasive-related MRI features. Methods derived from the automatic segmentation showed better performance than original methods and achieved areas under the curve of 0.840 and 0.920 for clinical models and radiomics models, respectively.Conclusion: The proposed methods could automatically segment the sellar region and extract features with high accuracy. The outstanding performance of the prediction of the tumor consistency indicates the methods' clinical usefulness for supporting neurosurgeons in judging patients' conditions, predicting prognosis, and other downstream tasks during the preoperative period.