A Novel Nomogram for Predicting the Risk of Short-Term Recurrence After Surgery in Glioma Patients

Risk factors, prognostic potency, and longitudinal variation of anxiety and depression in postoperative glioma patients

Objective

Anxiety and depression are common mental disorders in glioma patients. This study aimed to evaluate the risk factors, prognostic role, and longitudinal changes in anxiety and depression in postoperative glioma patients.

Methods

Anxiety and depression were assessed by Hospital Anxiety and Depression Scale at baseline, month (M) 6, M12, M24 and M36 in 270 glioma patients after surgical resection. Furthermore, comprehensive clinic characteristics and treatment-related information were collected.

Results

Gender (female vs. male) (P = 0.014, odds ratio (OR) = 1.974), marital status (single/divorced/widowed vs. married) (P = 0.019, OR = 2.172), Karnofsky performance status (KPS) score (≤70 vs. > 70) (P = 0.002, OR = 2.556), World Health Organization (WHO) classification (high-grade glioma (HGG) vs. low-grade glioma (LGG)) (P = 0.005, OR = 2.155), and postoperative complications (yes vs. not) (P = 0.001, OR = 2.525) were independently related to anxiety occurrence. Marital status (single/divorced/widowed vs. married) (P = 0.034, OR = 2.026), KPS score (≤70 vs. > 70) (P < 0.001, OR = 3.880), WHO classification (HGG vs. LGG) (P = 0.032, OR = 1.810), and postoperative complications (yes vs. not) (P = 0.001, OR = 2.602) were independently related to depression occurrence. Besides, anxiety (P = 0.038) and depression (P = 0.013) were linked with shorter overall survival (OS), and depression was an independent risk factor for worse OS (P = 0.040, hazard ratio = 1.596). More importantly, anxiety and depression remained at a high prevalence during a 3-year follow-up.

Conclusion

Gender, marital status, KPS score, WHO classification, and postoperative complications are risk factors for anxiety and depression; moreover, anxiety and depression are at high prevalence continuously and correlated with worse survival in postoperative glioma patients.

Nomogram for predicting early recurrence in patients with high-grade gliomas

OBJECTIVE

To develop a nomogram to predict early recurrence of high-grade glioma (HGG) based on clinical pathology, genetic factors and MRI parameters.

METHODS

154 patients with HGG were classified into recurrence and non-recurrence groups based on the pathological diagnosis and RANO criteria. Clinical pathology information included age, sex, preoperative Karnofsky performance status (KPS) scores,grade, and cell proliferation index (Ki-67). Gene information included P53, IDH1, MGMT, and TERT expression status. All patients underwent baseline MRIs before treatment, including T1WI, T2WI, T1C, Flair, and DWI examinations. Tumor location, single/multiple tumors, tumor diameter, peritumoral edema, necrotic cyst, hemorrhage, average apparent diffusion coefficient(ADC) value, and minimum ADC values were evaluated. Univariate and multivariate logistic regression analyses were used to determine the predictors of early recurrence and build nomogram.

RESULTS

Univariate analysis showed that the number of tumors (OR, 0.258; 95% 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 non-recurrence 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 MGMT (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%.

CONCLUSION

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