Machine Learning-Based Multiparametric Magnetic Resonance Imaging Radiomic Model for Discrimination of Pathological Subtypes of Craniopharyngioma.

Abstract

Preoperative, noninvasive discrimination of the craniopharyngioma subtypes is important because it influences the treatment strategy. To develop a radiomic model based on multiparametric magnetic resonance imaging for noninvasive discrimination of pathological subtypes of craniopharyngioma. Retrospective. A total of 164 patients from two medical centers were enrolled in this study. Patients from the first medical center were divided into a training cohort (N=99) and an internal validation cohort (N=33). Patients from the second medical center were used as the external independent validation cohort (N=32). Axial T1 -weighted (T1 -w), T2 -weighted (T2 -w), contrast-enhanced T1 -weighted (CET1 -w) on 3.0 T or 1.5 T magnetic resonance scanners. Pathological subtypes (squamous papillary craniopharyngioma and adamantinomatous craniopharyngioma) were confirmed by surgery and hematoxylin and eosin staining. Optimal radiomic feature selection was performed by SelectKBest, the least absolute shrinkage and selection operator algorithm, and support vector machine (SVM) with a recursive feature elimination algorithm. Models based on each sequence or combinations of sequences were built using a SVM classifier and used to differentiate pathological subtypes of craniopharyngioma in the training cohort, internal validation, and external validation cohorts. The area under the receiver operating characteristic curve (AUC) was used to assess the diagnostic performance of the radiomic models. Seven texture features, three from T1 -w, two from T2 -w, and two from CET1 -w, were selected and used to construct the radiomic model. The AUC values of the radiomic model were 0.899, 0.810, and 0.920 in the training cohort, internal and external validation cohorts, respectively. The AUC values of the clinicoradiological model were 0.677, 0.655, and 0.671 in the training cohort, internal and external validation cohorts, respectively. The model based on radiomic features from T1 -w, T2 -w, and CET1 -w has a high discriminatory ability for pathological subtypes of craniopharyngioma. 4 TECHNICAL EFFICACY: 2.