Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Department of Plastic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
J Clin Endocrinol Metab. 2021 Aug 18;106(9):2535-2546. doi: 10.1210/clinem/dgab371.
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.
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.
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.
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.
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.
垂体腺瘤（PA）的切除术计划需要术前观察鞍区。放射组学预测需要高质量的分割。手动勾画既耗时又受评估者变异性的影响。
本研究旨在创建一种自动分割鞍区的方法，以及几种提取侵袭性相关特征的工具，并通过预测肿瘤一致性来评估其临床应用价值。
纳入的患者均在北京协和医学院医院诊断为垂体腺瘤。创建了一种称为门控形 U 型网络（GSU-Net）的深度卷积神经网络，用于自动将鞍区分割成 8 个类别。从分割结果中提取了 5 个磁共振成像（MRI）特征，包括肿瘤直径、体积、视交叉高度、Knosp 分级系统和颈内动脉接触程度。通过肿瘤一致性的诊断准确性评估所提出方法的临床应用价值。
共纳入 163 例经证实的垂体腺瘤患者作为第一组，并将其随机分为训练数据集和测试数据集（分别为 131 例和 32 例）。纳入 50 例经证实的肢端肥大症患者作为第二组。重要图像切片中垂体腺瘤的 Dice 系数为 0.940。所提出的方法对 5 种侵袭性相关 MRI 特征的预测准确率均超过 80%。自动分割得到的方法比原始方法表现更好，在临床模型和放射组学模型中分别获得了 0.840 和 0.920 的曲线下面积。
所提出的方法可以自动分割鞍区并提取具有高精度的特征。肿瘤一致性预测的出色表现表明，这些方法在支持神经外科医生判断患者病情、预测预后以及在术前进行其他下游任务方面具有临床应用价值。
