Radiomics may increase the prognostic value for survival in glioblastoma patients when combined with conventional clinical and genetic prognostic models.

OBJECTIVES: To evaluate the additional prognostic value of multiparametric MR-based radiomics in patients with glioblastoma when combined with conventional clinical and genetic prognostic factors. METHODS: In this single-center study, patients diagnosed with glioblastoma between October 2007 and December 2019 were retrospectively screened and grouped into training and test sets with a 7:3 distribution. Segmentations of glioblastoma using multiparametric MRI were performed automatically via a convolutional-neural network. Prognostic factors in the clinical model included age, sex, type of surgery/post-operative treatment, and tumor location; those in the genetic model included statuses of isocitrate dehydrogenase-1 mutation and O-6-methylguanine-DNA-methyltransferase promoter methylation. Univariate and multivariate Cox proportional hazards analyses were performed for overall survival (OS) and progression-free survival (PFS). Integrated time-dependent area under the curve (iAUC) for survival was calculated and compared between prognostic models via the bootstrapping method (performances were validated with prediction error curves). RESULTS: Overall, 120 patients were included (training set, 85; test set, 35). The mean OS and PFS were 25.5 and 18.6 months, respectively. The prognostic performances of multivariate models improved when radiomics was added to the clinical model (iAUC: OS, 0.62 to 0.73; PFS, 0.58 to 0.66), genetic model (iAUC: OS, 0.59 to 0.67; PFS, 0.59 to 0.65), and combined model (iAUC: OS, 0.65 to 0.73; PFS, 0.62 to 0.67). In the test set, the combined model (clinical, genetic, and radiomics) demonstrated robust validation for risk prediction of OS and PFS. CONCLUSIONS: Radiomics increased the prognostic value when combined with conventional clinical and genetic prognostic models for OS and PFS in glioblastoma patients. KEY POINTS: • CNN-based automatic segmentation of glioblastoma on multiparametric MRI was useful in extracting radiomic features. • Patients with glioblastoma with high-risk radiomics scores had poor overall survival (hazards ratio 8.33, p < 0.001) and progression-free survival (hazards ratio 3.76, p < 0.001). • MR-based radiomics improved the survival prediction when combined with clinical and genetic factors (overall and progression-free survival iAUC from 0.65 to 0.73 and 0.62 to 0.67, respectively; both p < 0.001).

Significance of COX-2 and VEGF expression in histopathologic grading and invasiveness of meningiomas.

Meningiomas are slow-growing neoplasms that recur locally. Their morphologic grading does not always correlate with patient outcome. We evaluated the status of several immunohistochemical markers with histopathologic parameters in various grades of meningioma.Eighty-eight meningioma specimens were examined immunohistochemically to determine the status of Ki-67, cyclin D1, epidermal growth factor receptor (EGFR), cyclooxygenase-2 (COX-2), vascular endothelial growth factor (VEGF), and bcl-2. Several clinical and pathological parameters were investigated.Forty-nine Grade I, 33 Grade II, and 6 Grade III meningiomas were observed. VEGF and Ki-67 expression was correlated with higher tumor grade. The association between grade and other immunohistochemical markers expression was not significant. A correlation was observed between COX-2 expression and invasiveness to the brain or adjacent soft tissue. Tumor recurrence was correlated with brain or adjacent soft tissue invasion. We also observed a relationship between VEGF level and COX-2 expression, and they were both correlated with necrosis.Immunohistochemical evaluation of VEGF, COX-2, and Ki-67 expression can provide information regarding the behavior of meningiomas, particularly for cases in which histological grading is not straightforward.