The B-Raf protein is a key signaling molecule in the mitogen activated protein kinase (MAPK) signaling pathway and has been implicated in the pathogenesis of a variety of cancers. An important V600E mutation has been identified and can cause constitutive B-Raf activation. Recent studies have evaluated a variety of small molecule inhibitors targeting B-Raf, including PLX4032/vemurafenib, dabrafenib, LGX818, GDC0879, XL281, ARQ736, PLX3603 (RO5212054), and RAF265. Therapeutic resistance has been identified and various mechanisms described. This review also discussed the current understanding of B-Raf signaling mechanism, methods of mutation detection, treatment strategies as well as potential methods of overcoming therapeutic resistance.

BACKGROUND: Although World Health Organization (WHO) grade I meningiomas are considered “benign” tumors, an elevated Ki-67 is one crucial factor that has been shown to influence tumor behavior and clinical outcomes. The ability to preoperatively discern Ki-67 would confer the ability to guide surgical strategy. OBJECTIVE: In this study, we develop a machine learning (ML) algorithm using radiomic feature analysis to predict Ki-67 in WHO grade I meningiomas. METHODS: A retrospective analysis was performed for a cohort of 306 patients who underwent surgical resection of WHO grade I meningiomas. Preoperative magnetic resonance imaging was used to perform radiomic feature extraction followed by ML modeling using least absolute shrinkage and selection operator wrapped with support vector machine through nested cross-validation on a discovery cohort (n = 230), to stratify tumors based on Ki-67 <5% and ≥5%. The final model was independently tested on a replication cohort (n = 76). RESULTS: An area under the receiver operating curve (AUC) of 0.84 (95% CI: 0.78-0.90) with a sensitivity of 84.1% and specificity of 73.3% was achieved in the discovery cohort. When this model was applied to the replication cohort, a similar high performance was achieved, with an AUC of 0.83 (95% CI: 0.73-0.94), sensitivity and specificity of 82.6% and 85.5%, respectively. The model demonstrated similar efficacy when applied to skull base and nonskull base tumors. CONCLUSION: Our proposed radiomic feature analysis can be used to stratify WHO grade I meningiomas based on Ki-67 with excellent accuracy and can be applied to skull base and nonskull base tumors with similar performance achieved.