ArticlemedRxiv : the preprint server for health sciences2024
Improving Individualized Rhabdomyosarcoma Prognosis Predictions Using Somatic Molecular Biomarkers.
Article in medRxiv : the preprint server for health sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Purpose: Molecular markers, such as Patients and Methods: Clinical variables and somatic mutation data for 20 genes from 641 RMS patients in the United Kingdom and the United States were used to develop three Cox proportional hazard models for predicting event-free survival (EFS). The 'Baseline Clinical' (BC) model included treatment location, age, fusion status, and risk group. The 'Gene Enhanced 2' (GE2) model added Results: The GE6 model demonstrated superior predictive performance, offering 39% more predictive information than the BC model (LR p<0.001) and 15% more than the GE2 model (LR p<0.001). The GE6 model achieved the highest discrimination with a C-index of 0.7087, a Nagalkerke R Conclusion: Incorporating molecular markers into RMS prognosis models improves prognosis predictions. Individualized prognosis predictions may suggest alternative treatment regimens compared to traditional risk-classification schemas. Improved clinical variables and external validation are required prior to implementing these models into clinical practice.
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