A Nomogram for Predicting Early Recurrence in Patients with High-Grade Gliomas

Objective: The objective of the study was to develop a nomogram to predict early recurrence of high-grade glioma (HGG) based on clinical pathology, genetic factors, and magnetic resonance imaging parameters.

Methods: One hundred fifty-four patients with HGG were classified into recurrence and nonrecurrence groups based on the pathological diagnosis and Response Assessment in Neuro-Oncology criteria. Clinical pathology information included age, sex, preoperative Karnofsky performance status scores, grade, and cell proliferation index (Ki-67). Gene information included P53, isocitrate dehydrogenase 1 (IDH1), O6-methylguanine-DNA methyltransferase, and telomerase reverse transcriptase expression status. All patients underwent baseline magnetic resonance imaging before treatment, including T1-weighted imaging, T2-weighted imaging, contrast-enhanced T1WI, fluid attenuated inversion recovery, and diffusion-weighted imaging examinations. Tumor location, single/multiple tumors, tumor diameter, peritumoral edema, necrotic cyst, hemorrhage, average apparent diffusion coefficient value, and minimum apparent diffusion coefficient values were evaluated. Univariate and multivariate logistic regression analyses were used to determine the predictors of early recurrence and build a nomogram.

Results: Univariate analysis showed that the number of tumors (odds ratio [OR], 0.258; 95% confidence interval [CI]: 0.104, 0.639; P = 0.003) and peritumoral edema (OR, 0.965; 95% CI: 0.942, 0.988; P = 0.003; mean in the recurrence group = 22.04 ± 17.21 mm; mean in the nonrecurrence group = 14.22 ± 12.84 mm) were statistically significantly different in patients with early recurrence. Genetic factors associated with early recurrence included IDH1 (OR, 4.405; 95% CI: 1.874, 10.353; P = 0.001) and O6-methylguanine-DNA methyltransferase (OR, 2.389; 95% CI: 1.234, 4.628; P = 0.010). Multivariate logistic regression analysis revealed that the number of tumors (OR, 0.227; 95% CI: 0.084, 0.616; P = 0.004), peritumoral edema (OR, 0.969; 95% CI: 0.945, 0.993; P = 0.013), and IDH1 (OR, 4.200; 95% CI: 1.602, 10.013; P = 0.004) were independent risk factors for early recurrence. The nomogram showed the highest net benefit when the threshold probability was less than 60%.

Conclusions: A nomogram prediction model can effectively aid in clinical treatment decisions for patients with newly diagnosed HGG.