Prognostic value of manual versus automatic methods for assessing extents of resection and residual tumor volume in glioblastoma

OBJECTIVE The extent of resection (EOR) and postoperative residual tumor (RT) volume are prognostic factors in glioblastoma. Calculations of EOR and RT rely on accurate tumor segmentations. Raidionics is an open-access software that enables automatic segmentation of preoperative and early postoperative glioblastoma using pretrained deep learning models. The aim of this study was to compare the prognostic value of manually versus automatically assessed volumetric measurements in glioblastoma patients. METHODS Adult patients who underwent resection of histopathologically confirmed glioblastoma were included from 12 different hospitals in Europe and North America. Patient characteristics and survival data were collected as part of local tumor registries or were retrieved from patient medical records. The prognostic value of manually and automatically assessed EOR and RT volume was compared using Cox regression models. RESULTS Both manually and automatically assessed RT volumes were a negative prognostic factor for overall survival (manual vs automatic: HR 1.051, 95% CI 1.034–1.067 [p < 0.001] vs HR 1.019, 95% CI 1.007–1.030 [p = 0.001]). Both manual and automatic EOR models showed that patients with gross-total resection have significantly longer overall survival compared with those with subtotal resection (manual vs automatic: HR 1.580, 95% CI 1.291–1.932 [p < 0.001] vs HR 1.395, 95% CI 1.160–1.679 [p < 0.001]), but no significant prognostic difference of gross-total compared with near-total (90%–99%) resection was found. According to the Akaike information criterion and the Bayesian information criterion, all multivariable Cox regression models showed similar goodness-of-fit. CONCLUSIONS Automatically and manually measured EOR and RT volumes have comparable prognostic properties. Automatic segmentation with Raidionics can be used in future studies in patients with glioblastoma.