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. 2020 Dec 18;10:590083. doi: 10.3389/fonc.2020.590083

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Copyright © 2020 Zhang, Ko, Chen, Chang, Chen, Lim, Tsui and Su

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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Flowchart of the analytical process for prediction of progression/recurrence (P/R) in non-functioning pituitary macroadenomas (NFPAs). The NFPA (red outline) is segmented on coronal contrast-enhanced (CE) T1WI and then mapped to the coronal T2WI. On each set of images, a total of 107 imaging features, including 32 first order features and 75 textural features, are extracted. The most important three features are selected by sequential feature selection and support vector machine (SVM) classifiers to build the prediction model. A 10-fold cross validation method is applied to test the model performance.