ArticlePloS one2017
Progressive disease in glioblastoma: Benefits and limitations of semi-automated volumetry.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 19 citations in OpenAlex.
- Automated longitudinal treatment response assessment of brain tumors: A systematic review.Neuro-oncology · 2025Pooled it
- Prognostic and predictive impact of abnormal signal volume evolution early after chemoradiotherapy in glioblastoma.Journal of neuro-oncology · 2023Trial
- Implication of tumor morphology and MRI characteristics on the accuracy of automated versus human segmentation of GBM areas.Scientific reports · 2025Article
- Preoperative nTMS analysis: a sensitive tool to detect imminent motor deficits in brain tumor patients.Acta neurochirurgica · 2024Review
- Ellipsoid calculations versus manual tumor delineations for glioblastoma tumor volume evaluation.Scientific reports · 2022Article
- Modeling Healthy Anatomy with Artificial Intelligence for Unsupervised Anomaly Detection in Brain MRI.Radiology. Artificial intelligence · 2021Article
- Article
- Comparison of Radiographic Approaches to Assess Treatment Response in Pituitary Adenomas: Is RECIST or RANO Good Enough?Journal of the Endocrine Society · 2019Article
- Brain Tumor-Enhancement Visualization and Morphometric Assessment: A Comparison of MPRAGE, SPACE, and VIBE MRI Techniques.AJNR. American journal of neuroradiology · 2019Article
- Divide and Conquer: Stratifying Training Data by Tumor Grade Improves Deep Learning-Based Brain Tumor Segmentation.Frontiers in neuroscience · 2019Article
- Clinical Evaluation of a Multiparametric Deep Learning Model for Glioblastoma Segmentation Using Heterogeneous Magnetic Resonance Imaging Data From Clinical Routine.Investigative radiology · 2018Article
- Earliest radiological progression in glioblastoma by multidisciplinary consensus review.Journal of neuro-oncology · 2018Article
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Authors and funding
12 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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