TMOD-01. A PROGNOSTIC NOMOGRAM MODEL AND ONLINE SOFTWARE FOR PATIENTS WITH NEWLY DIAGNOSED LOWER-GRADE GLIOMAS

Abstract

Abstract The nomogram represents a statistical model that incorporates multiple risk factors to estimate individualized survival probabilities. In this study, we developed a nomogram which provides an important tool for individulized survival predicition for newly diagnosed low-grade gliomas (LGG). A total number of 582 newly diagnosed LGG patients were included; the median age was 39.93 years and 42% were female. Cox regression analysis showed that younger age at diagnosis, WHO grade II vs. III, the IDHmut-codel vs. the IDHwt, and the IDHmut-non-codel vs. the IDHwt were significantly associated with better prognosis. The adjuvant treatment following surgery showed a trend towards improved survival. Subsequently, the nomogram to estimate 60-, 90-, and 120-month survival probabilities was established. Our data showed that the age at diagnosis was the largest contributor to patient survival, followed by molecular subtype, WHO grade, treatment and gender. The calibration plot showed that the observed and the nomogram predicted OS curves were well-aligned. In addition, we also validated our nomogram for LGG patients who received postsurgical adjuvant therapy through cross-validation and the calibration plot. Finally, we developed a free online tool for this nomogram (softwarewebsite:https://rrlnnomogram.shinyapps.io/LGG_Nom_Asian/). Overall, this model should be a useful tool for counseling patients in clinical practice including treatment decisions, follow-up, and prognosis.