Development and validation of a radiomics nomogram for preoperative prediction of BRAFV600E mutation status in adult patients with craniopharyngioma

Abstract

Although craniopharyngiomas are rare benign brain tumors primarily managed by surgery, they are often burdened by a poor prognosis due to tumor recurrence and long-term morbidity. In recent years, BRAF^V600E-targeted therapy has been promising, showing potential as an adjuvant or neoadjuvant approach. Therefore, we aim to develop and validate a radiomics nomogram for preoperative prediction of BRAF^V600E mutation in craniopharyngiomas. A total of 398 patients with craniopharyngioma (training cohort: n = 278; validation cohort: n = 120) were retrospectively reviewed. We extracted 851 radiomic features from MRI images and adopted a support vector machine (SVM) classifier to develop a radiomic model. Also, a clinical-radiomics nomogram was constructed based on a multivariable logistic regression analysis. The performance of the nomogram was evaluated by its discrimination, calibration, and clinical utility. The radiomic model using the SVM based on three selected features showed good discrimination in the training and validation cohorts (area under the curve [AUC], 0.941 and 0.945, respectively). A higher Rad-score, smaller tumor volume, and homogenous enhancement were demonstrated as independent predictors of BRAF^V600E mutation in craniopharyngioma. The nomogram incorporating the Rad-score and clinical-radiological factors exhibited AUCs of 0.958 (95% CI, 0.936-0.980) and 0.956 (95% CI, 0.921-0.991) in the training and validation cohorts, respectively, showing good clinical benefit and calibration. The radiomics nomogram could provide an accurate, non-invasive preoperative prediction of BRAF^V600E mutation in craniopharyngioma and may provide potential guidance for the preoperative administration of BRAF ^V600E mutation inhibitors and promote personalized treatment. Further prospective validation is still needed.

Keywords: BRAF V600E mutation; Craniopharyngioma; Neuro-oncology; Nomogram; Radiomics; Target prediction.