Abstract
ObjectiveThe aim of this study was to establish a nomogram model for predicting the risk of short-term recurrence in glioma patients.MethodsThe clinical data of recurrent glioma patients were summarized and analyzed in this study. Univariate and multivariate logistic regression analyses were performed to analyze the correlation between clinical data and the risk of short-term recurrence after operation. A nomogram was established based on the multivariate logistic regression model results.ResultsA total of 175 patients with recurrent glioma were enrolled, with 53 patients in the short-term recurrence (STR) group (recurrent time ≤6 months) and 122 patients in the long-term recurrence (LTR) group (recurrent time ≥36 months). Univariate analysis revealed that age at diagnosis, Karnofsky performance scores (KPSs), tumor location, glioma grade, glioma type, extent of resection (EOR), adjuvant chemotherapy (ad-CT), concurrent chemotherapy (co-CT), and isocitrate dehydrogenase (IDH) status were significantly associated with the short-term glioma recurrence. Multivariate analyses revealed that age at diagnosis, KPS, glioma grade, EOR, and IDH were independent risk factors for short-term glioma recurrence. A risk nomogram for the short-term recurrence of glioma was established, with the concordance index (C-index) of 0.971. The findings of calibration and receiver operating characteristic (ROC) curves showed that our nomogram model had good performance and discrimination to estimate short-term recurrence probability.ConclusionThis nomogram model provides reliable information about the risk of short-term glioma recurrence for oncologists and neurosurgeons. This model can predict the short-term recurrence probability and give assistance to decide the interval of follow-up or formulate individualized treatment strategies based on the predicted results. A free online prediction risk tool for this nomogram is provided: https://rj2021.shinyapps.io/Nomogram_ recurrence-risk/.
Highlights
Glioma is the most common primary intracranial tumor, with a high mortality rate and poor outcome [1,2,3]
Surgery consisted of gross total resection (GTR)/STRa in 96 patients (54.9%) and partial resection (PR) in 70 patients (45.1%)
Comparing the characteristics and management data between these two groups, we found that the age at diagnosis of the STR group was lower in the long-term recurrence (LTR) group (56.19 ± 13.36 vs. 42.43 ± 13.91, p < 0.001)
Summary
Glioma is the most common primary intracranial tumor, with a high mortality rate and poor outcome [1,2,3]. Time to recurrence is an independent predictor for survival [11, 12], with short-term recurrence predicting a worse prognosis; a tool that can predict short-term recurrence risk of glioma is important. No nomogram model for predicting the risk of shortterm recurrence in glioma patients has been reported. A risk nomogram for predicting short-term recurrence of glioma is needed urgently, and it is a tool for clinicians to use for predicting the probability of short-term recurrence, developing an individualized management strategy after tumor recurrence, and formulating the interval of follow-up. The aim of this study is to establish and independently validate a nomogram model for estimating individualized short-term recurrence probabilities for glioma patients, which would be readily accessible for clinical use
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
