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

医学 磁共振成像 颅咽管瘤 放射科 病态的 核磁共振 病理 计算机科学 物理
作者
Zhou‐San Huang,Xiang Xiao,Xiaodan Li,Hai‐Zhu Mo,Wenle He,Yao‐Hong Deng,Li‐Jun Lu,Yuan‐Kui Wu,Hao Liu
出处
期刊:Journal of Magnetic Resonance Imaging [Wiley]
卷期号:54 (5): 1541-1550 被引量:27
标识
DOI:10.1002/jmri.27761
摘要

BACKGROUND: Preoperative, noninvasive discrimination of the craniopharyngioma subtypes is important because it influences the treatment strategy. PURPOSE: To develop a radiomic model based on multiparametric magnetic resonance imaging for noninvasive discrimination of pathological subtypes of craniopharyngioma. STUDY TYPE: Retrospective. POPULATION: 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). FIELD STRENGTH/SEQUENCE: -w) on 3.0 T or 1.5 T magnetic resonance scanners. ASSESSMENT: 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. STATISTICAL TESTS: The area under the receiver operating characteristic curve (AUC) was used to assess the diagnostic performance of the radiomic models. RESULTS: -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. DATA CONCLUSION: -w has a high discriminatory ability for pathological subtypes of craniopharyngioma. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY: 2.
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