• Qing Zhou, Xiaoai Ke, Caiqiang Xue, Shenglin Li, Xiaoyu Huang, Bin Zhang, and Junlin Zhou.
• Department of Radiology, Lanzhou University Second Hospital, Gansu, China; Second Clinical School, Lanzhou University, Gansu, China; Key Laboratory of Medical Imaging of Gansu Province, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, China.
• World Neurosurg. 2022 Aug 1; 164: e619-e628.

ObjectiveThe 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.MethodsOne 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.ResultsUnivariate 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%.ConclusionsA nomogram prediction model can effectively aid in clinical treatment decisions for patients with newly diagnosed HGG.Copyright © 2022 Elsevier Inc. All rights reserved.

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