Abstract
PurposeUnambiguous evaluation of glioblastoma (GB) progression is crucial, both for clinical trials as well as day by day routine management of GB patients. 3D-volumetry in the follow-up of GB provides quantitative data on tumor extent and growth, and therefore has the potential to facilitate objective disease assessment. The present study investigated the utility of absolute changes in volume (delta) or regional, segmentation-based subtractions for detecting disease progression in longitudinal MRI follow-ups.Methods165 high resolution 3-Tesla MRIs of 30 GB patients (23m, mean age 60.2y) were retrospectively included in this single center study. Contrast enhancement (CV) and tumor-related signal alterations in FLAIR images (FV) were semi-automatically segmented. Delta volume (dCV, dFV) and regional subtractions (sCV, sFV) were calculated. Disease progression was classified for every follow-up according to histopathologic results, decisions of the local multidisciplinary CNS tumor board and a consensus rating of the neuro-radiologic report.ResultsA generalized logistic mixed model for disease progression (yes / no) with dCV, dFV, sCV and sFV as input variables revealed that only dCV was significantly associated with prediction of disease progression (P = .005). Delta volume had a better accuracy than regional, segmentation-based subtractions (79% versus 72%) and a higher area under the curve by trend in ROC curves (.83 versus .75).ConclusionAbsolute volume changes of the contrast enhancing tumor part were the most accurate volumetric determinant to detect progressive disease in assessment of GB and outweighed FLAIR changes as well as regional, segmentation-based image subtractions. This parameter might be useful in upcoming objective response criteria for glioblastoma.
Highlights
IntroductionMR imaging plays a central role in response assessment of glioblastoma (GB), both in clinical trials as well as in the daily clinical management of GB patients
Delta volume had a better accuracy than regional, segmentation-based subtractions (79% versus 72%) and a higher area under the curve by trend in ROC curves (.83 versus .75)
MR imaging plays a central role in response assessment of glioblastoma (GB), both in clinical trials as well as in the daily clinical management of GB patients
Summary
MR imaging plays a central role in response assessment of glioblastoma (GB), both in clinical trials as well as in the daily clinical management of GB patients. Three-dimensional volumetric tumor assessment using MR image segmentations might overcome current limitations of uni- and biplanar assessments and offers an elegant way to quantify image information [5,6,8,18]. Manual segmentations are still regarded as the gold standard in many imaging studies but require a human rater which makes them often time consuming and prone to bias [18]. Semi-automated segmentation tools often apply intelligent region-growing algorithms that assist the rater during the delineation, saving time and increasing homogeneity of segmentations [13,14,15,18,20,21]. Automated segmentation techniques offer constant results but still have several limitations in terms of precision or unexpected signal alterations like in postoperative MRI of GB [5,6,16,17,22]
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