Preoperative Prediction Nomogram Based on Integrated Profiling for Glioblastoma Multiforme in Glioma Patients - Wu, Wei; Deng, Zhong; Alafate, Wahafu; Wang, Yichang; Xiang, Jianyang; Zhu, Lizhe; Li, Bolin; Wang, Maode; Wang, Jia (Frontiers in Oncology, 2020)
Introduction: Traditional classification which divided gliomas into glioblastoma multiforme (GBM) and lower grade glioma (LGG) based on pathological morphology has been challenged during the past decade by the improvement of molecular stratification, however, the reproducibility and diagnostic accuracy of glioma classification still remain poor. This study aimed to establish and validate a novel nomogram for preoperative diagnosis of GBM by using integrated data combined with feasible baseline characteristics and preoperative tests. Material and method: The models were established in a primary cohort included 259 glioma patients underwent surgical resection and were pathologically diagnosed from March 2014 to May 2016 in the First Affiliated Hospital of Xi’an Jiaotong University. The preoperative data were used to construct 3 models by the best subset regression, the forward stepwise regression and the least absolute shrinkage and selection operator, furthermore, establish the nomogram among those models. The assessment of nomogram was carried out by the discrimination and calibration in internal cohort and external cohort. Results and discussion: For all 3 models, model 2 contained 8 clinical-related variables, which exhibited the minimum Akaike Information Criterion (173.71) and maximum concordance index (0.894). Compared with the other 2 models, the integrated discrimination index for model 2 was significantly improved, indicating that nomogram obtained from model 2 was the most appropriate model. Likewise, the nomogram showed great calibration and significant clinical benefit according to calibration curves and the decision curve analysis. Conclusion: In conclusion, our study showed a novel preoperative model incorporated clinically relevant variables, imaging features with laboratory data and could be used for preoperative prediction in glioma patients thus provide more reliable evidence for surgical decision-making.