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A machine learning model to precisely immunohistochemically classify pituitary adenoma subtypes with radiomics based on preoperative magnetic resonance imaging.

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机构： [a]Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, China [b]College of Computer Science, Sichuan University, Chengdu, 610065, Sichuan Province, China [c]The Institute for Industrial Internet Research, Sichuan University, Chengdu, Sichuan Province, China

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关键词： Pituitary adenomas Pituitary transcription factor Radiomics support vector machine

摘要：

The type of pituitary adenoma (PA) cannot be clearly recognized with preoperative magnetic resonance imaging (MRI) but can be classified with immunohistochemical staining after surgery. In this study, a model to precisely immunohistochemically classify the PA subtypes by radiomic features based on preoperative MR images was developed. Two hundred thirty-five pathologically diagnosed PAs, including t-box pituitary transcription factor (Tpit) family tumors (n = 55), pituitary transcription factor 1 (Pit-1) family tumors (n = 110), and steroidogenic factor 1 (SF-1) family tumors (n = 70), were retrospectively studied. T1-weighted, T2-weighted and contrast-enhanced T1-weighted images were obtained from all patients. Through imaging acquisition, feature extraction and radiomic data processing, 18 radiomic features were used to train support vector machine (SVM), k-nearest neighbors (KNN) and Naïve Bayes (NBs) models. Ten-fold cross-validation was applied to evaluate the performance of these models. The SVM model showed high performance (balanced accuracy 0.89, AUC 0.9549) whereas the KNN (balanced accuracy 0.83, AUC 0.9266) and NBs (balanced accuracy 0.80, AUC 0.9324) models displayed low performance based on the T2-weighted images. The performance of the T2-weighted images was better than that of the other two MR sequences. Additionally, significant sensitivity (P = 0.031) and specificity (P = 0.012) differences were observed when classifying the PA subtypes by T2-weighted images. The SVM model was superior to the KNN and NBs models and can potentially precisely immunohistochemically classify PA subtypes with an MR-based radiomic analysis. The developed model exhibited good performance using T2-weighted images and might offer potential guidance to neurosurgeons in clinical decision-making before surgery. Copyright © 2020 Elsevier B.V. All rights reserved.

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出版当年[2020]版：

大类 | 3 区医学

小类 | 3 区核医学

最新[2023]版：

大类 | 3 区医学

小类 | 3 区核医学

第一作者：

第一作者机构： [a]Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, China

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通讯作者：

通讯机构： [a]Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, China [b]College of Computer Science, Sichuan University, Chengdu, 610065, Sichuan Province, China [c]The Institute for Industrial Internet Research, Sichuan University, Chengdu, Sichuan Province, China [*1]No.37, Guoxue Alley, Wuhou District, Chengdu, Sichuan Province, 610041, China. [*2]South Section 1, Yihuan Road, Chengdu, Sichuan, 610065, China.

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