Limitations of nomogram models in predicting survival outcomes for glioma patients - Xue, Jihao; Liu, Hang; Jiang, Lu; Yin, Qijia; Chen, Ligang; Wang, Ming (Frontiers in Immunology, 2025)
PurposeGlioma represents a prevalent and malignant tumor of the central nervous system (CNS), and it is essential to accurately predict the survival of glioma patients to optimize their subsequent treatment plans. This review outlines the most recent advancements and viewpoints regarding the application of nomograms in glioma prognosis research.DesignWith an emphasis on the precision and external applicability of predictive models, we carried out a comprehensive review of the literature on the application of nomograms in glioma and provided a step-by-step guide for developing and evaluating nomograms.ResultsA summary of thirty-nine articles was produced. The majority of nomogram-building research has used limited patient samples, disregarded the proportional hazards (PH) assumption in Cox regression models, and some of them have failed to incorporate external validation. Furthermore, the predictive capability of nomograms is influenced by the selection of incorporated risk factors. Overall, the current predictive accuracy of nomograms is moderately credible.ConclusionThe development and validation of nomogram models ought to adhere to a standardized set of criteria, thereby augmenting their worth in clinical decision-making and clinician-patient communication. Prior to the clinical application of a nomogram, it is imperative to thoroughly scrutinize its statistical foundation, rigorously evaluate its accuracy, and, whenever feasible, assess its external applicability utilizing multicenter databases.