Development of an MRI‐Based Comprehensive Model Fusing Clinical, Radiomics and Deep Learning Models for Preoperative Histological Stratification in Intracranial Solitary Fibrous Tumor

Abstract

BackgroundAccurate preoperative histological stratification (HS) of intracranial solitary fibrous tumors (ISFTs) can help predict patient outcomes and develop personalized treatment plans. However, the role of a comprehensive model based on clinical, radiomics and deep learning (CRDL) features in preoperative HS of ISFT remains unclear.PurposeTo investigate the feasibility of a CRDL model based on magnetic resonance imaging (MRI) in preoperative HS in ISFT.Study TypeRetrospective.PopulationThree hundred and ninety‐eight patients from Beijing Tiantan Hospital, Capital Medical University (primary training cohort) and 49 patients from Lanzhou University Second Hospital (external validation cohort) with ISFT based on histopathological findings (237 World Health Organization [WHO] tumor grade 1 or 2, and 210 WHO tumor grade 3).Field Strength/Sequence3.0 T/T1‐weighted imaging (T1) by using spin echo sequence, T2‐weighted imaging (T2) by using fast spin echo sequence, and T1‐weighted contrast‐enhanced imaging (T1C) by using two‐dimensional fast spin echo sequence.AssessmentArea under the receiver operating characteristic curve (AUC) was used to assess the performance of the CRDL model and a clinical model (CM) in preoperative HS in the external validation cohort. The decision curve analysis (DCA) was used to evaluate the clinical net benefit provided by the CRDL model.Statistical TestsCohen's kappa, intra‐/inter‐class correlation coefficients (ICCs), Chi‐square test, Fisher's exact test, Student's t‐test, AUC, DCA, calibration curves, DeLong test. A P value <0.05 was considered statistically significant.ResultsThe CRDL model had significantly better discrimination ability than the CM (AUC [95% confidence interval, CI]: 0.895 [0.807–0.912] vs. 0.810 [0.745–0.874], respectively) in the external validation cohort. The CRDL model can provide a clinical net benefit for preoperative HS at a threshold probability >20%.Data ConclusionThe proposed CRDL model holds promise for preoperative HS in ISFT, which is important for predicting patient outcomes and developing personalized treatment plans.Level of Evidence3Technical EfficacyStage 2