• Linsha Yang, Tao Zheng, Yanchao Dong, Zhanqiu Wang, Defeng Liu, Juan Du, Shuo Wu, Qinglei Shi, and Lanxiang Liu.
• Department of Magnetic Resonance Imaging, Qinhuangdao Municipal No. 1 Hospital, Qinhuangdao, China.
• J Magn Reson Imaging. 2021 Apr 1; 53 (4): 1054-1065.

BackgroundTreatment regimens and prognoses of gastrointestinal stromal tumors (GIST) are quite different for tumors in different risk categories. Accurate preoperative grading of tumors is important for avoiding under- or overtreatment.PurposeTo develop and validate an MRI texture-based model to predict the mitotic index and its risk classification.Study TypeRetrospective.PopulationNinety-one patients with histologically-confirmed GIST; 64 patients in a training cohort, and 27 patients in a test cohort.Field Strength/SequenceT2 -weighted imaging (T2 WI), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced three-dimensional volumetric interpolated breath-hold examination (3D-VIBE) at 1.5T.AssessmentGIST images were manually segmented by two independent radiologists using ITK-SNAP software and MRI features were extracted using Pyradiomics. Two pathologists reviewed the tissue specimens of the tumors to identify the mitotic index and risk classification in consensus.Statistical TestsThe least absolute shrinkage and selection operator (LASSO) regression method was used to select texture features. A logistic regression model was established based on the radiomic score (radscore), tumor location, and maximum diameter to predict tumor classification and develop a nomogram. Receiver operator characteristic (ROC) curves were used to evaluate the ability of the nomogram to distinguish between two tumors with different risk classifications, and a calibration curve was used to evaluate the consistency between the predicted risk and the actual risk.ResultsThe texture signature achieved high efficacy in predicting the mitotic index area under the curve ([AUC], 0.906; 95% confidence interval [CI]: 0.813, 0.961). A nomogram for prediction of the risk classification of GIST, which incorporated this texture signature together with maximum tumor diameter and location, allowed good discrimination in the training cohort (AUC, 0.878; 95% CI: 0.769, 0.960) and the validation cohort (AUC, 0.903; 95% CI: 0.732, 0.922).Data ConclusionThe texture-based model can be used to predict GIST mitotic index and risk classification preoperatively.Level Of Evidence2.Technical Efficacy Stage3.© 2020 International Society for Magnetic Resonance in Medicine.

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