MRI Texture‐Based Models for Predicting Mitotic Index and Risk Classification of Gastrointestinal Stromal Tumors. Issue 4 (9th October 2020)

• Record Type: Journal Article
• Authors: Yang, Linsha; Zheng, Tao; Dong, Yanchao; Wang, Zhanqiu; Liu, Defeng; Du, Juan; Wu, Shuo; Shi, Qinglei; Liu, Lanxiang
• Is Part Of: Journal of magnetic resonance imaging. Volume 53:Issue 4(2021)
• Page Start: 1054
• Page End: 1065
• Publication Date: 2020-10-09
• Subjects: gastrointestinal stromal tumor -- magnetic resonance imaging -- texture analysis -- risk classification -- mitotic index; Magnetic resonance imaging -- Periodicals; 616
• DOI: 10.1002/jmri.27390
• Languages: English
• ISSNs: 1053-1807

Abstract

Abstract : Background: Treatment 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. Purpose: To develop and validate an MRI texture‐based model to predict the mitotic index and its risk classification. Study Type: Retrospective. Population: Ninety‐one patients with histologically‐confirmed GIST; 64 patients in a training cohort, and 27 patients in a test cohort. Field Strength/Sequence: T2 ‐weighted imaging (T2 WI), diffusion‐weighted imaging (DWI), and dynamic contrast‐enhanced three‐dimensional volumetric interpolated breath‐hold examination (3D‐VIBE) at 1.5T. Assessment: GIST 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 Tests: The 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. Results: The 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 Conclusion: The texture‐based model can be used to predict GIST mitotic index and risk classification preoperatively. Level of Evidence: 2. Technical Efficacy Stage: 3