Evaluation of Microscopic Extension in High-grade Glioma Using Macropathology: Determination of Optimal Clinical Target Volume Margins for Radiotherapy

Abstract Introduction: One of the most difficult steps of radiotherapy is to determine the clinical target volume (CTV) based on microscopic extension (ME). In this study, we performed a consecutive macropathologic analysis to assess ME in high-grade glioma (HGG) to determine appropriate CTV margins for radiotherapy.Material and methods: The study included 30 HGG patients with tumors located in non-functional areas, and supra total resection was performed. The ME distance from the edge of the tumor to the microscopic tumor cells surrounding brain tissue was measured. Associations between the extent of ME and clinicopathological characteristics were evaluated by multivariate linear regression (MVLR) analysis. An ME predictive model was developed based on the MVLR model. Meanwhile, in order to validate the feasibility and safety of this model, we prospectively recruited another 30 HGG patients in a 1:1 ratio to receive guideline-based radiotherapy (RT) or model-based RT. The overall response rate (ORR) was evaluated during a follow-up of 14 months.Results: Between June 2017 and July 2019, 652 pathologic slides were analyzed. The mean ME distance was 1.70cm (range, 0.63 to 2.87cm). The MVLR analysis identified that pathologic grade, subventricular zone (SVZ) contact and O6-methylguanine-DNA methyltransferase (MGMT) methylation, isocitrate dehydrogenase (IDH) mutation and 1p/19q co-deletion status were independent variables predicting ME (all P < 0.05). A multivariable prediction model was developed as follows: YME = 0.672 + 0.513XGrade + 0.380XSVZ + 0.439XMGMT + 0.320XIDH + 0.333X1p/19q. The R-square value of goodness of fit was 0.780. In our validation cohort, after a mean follow-up of 7.65months, patients in the model RT group had a higher ORR than those in the guideline RT group (66.7% vs. 20%, P = 0.01).Conclusion: ME was heterogeneously distributed across different grades of gliomas according to the tumor location and molecular marker status, which indicated that CTV delineation should be individualized. The model could predict the ME of HGG, which may help clinicians determine the CTV for individual patients.