1. Quantifying eloquent locations for glioblastoma surgery using resection probability maps
- Author
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Wim Bouwknegt, Georg Widhalm, Frederik Barkhof, Lorenzo Bello, Domenique M J Müller, Michiel Wagemakers, Shawn L. Hervey-Jumper, W. Peter Vandertop, Wimar A. van den Brink, Marnix G. Witte, Pierre A. Robe, Tommaso Sciortino, Seunggu J. Han, Barbara Kiesel, Marco Conti Nibali, Julia Furtner, Philip C. De Witt Hamer, Marco Rossi, Roelant S Eijgelaar, Hilko Ardon, Martin Visser, Jan C. de Munck, Alfred Kloet, Albert J S Idema, Mitchel S. Berger, Aeilko H. Zwinderman, Neurosurgery, ANS - Neurovascular Disorders, ANS - Systems & Network Neuroscience, CCA - Cancer Treatment and Quality of Life, Epidemiology and Data Science, APH - Methodology, Radiology and nuclear medicine, Amsterdam Neuroscience - Brain Imaging, Amsterdam Neuroscience - Neuroinfection & -inflammation, CCA - Cancer Treatment and quality of life, Amsterdam Neuroscience - Neurovascular Disorders, and Amsterdam Neuroscience - Systems & Network Neuroscience
- Subjects
Male ,Neoplasm, Residual ,Biopsy ,Kaplan-Meier Estimate ,Logistic regression ,Neurosurgical Procedures ,0302 clinical medicine ,glioma ,Medicine ,neurosurgery ,BRAIN ,Brain Mapping ,medicine.diagnostic_test ,Brain Neoplasms ,General Medicine ,Middle Aged ,extent of resection ,GLIOMAS ,Treatment Outcome ,030220 oncology & carcinogenesis ,oncology ,Female ,NEWLY-DIAGNOSED GLIOBLASTOMA ,Adult ,medicine.medical_specialty ,WHITE-MATTER TRACTS ,residual volume ,Extent of resection ,Resection ,MULTIFORME ,03 medical and health sciences ,Glioma ,Humans ,Karnofsky Performance Status ,Grading (tumors) ,Aged ,Probability ,Receiver operating characteristic ,business.industry ,EXTENT ,medicine.disease ,Survival Analysis ,Surgery ,ROC Curve ,PREDICTS SURVIVAL ,PATTERNS ,reproducibility of results ,Glioblastoma ,business ,030217 neurology & neurosurgery ,RESPONSE ASSESSMENT - Abstract
OBJECTIVE Decisions in glioblastoma surgery are often guided by presumed eloquence of the tumor location. The authors introduce the “expected residual tumor volume” (eRV) and the “expected resectability index” (eRI) based on previous decisions aggregated in resection probability maps. The diagnostic accuracy of eRV and eRI to predict biopsy decisions, resectability, functional outcome, and survival was determined. METHODS Consecutive patients with first-time glioblastoma surgery in 2012–2013 were included from 12 hospitals. The eRV was calculated from the preoperative MR images of each patient using a resection probability map, and the eRI was derived from the tumor volume. As reference, Sawaya’s tumor location eloquence grades (EGs) were classified. Resectability was measured as observed extent of resection (EOR) and residual volume, and functional outcome as change in Karnofsky Performance Scale score. Receiver operating characteristic curves and multivariable logistic regression were applied. RESULTS Of 915 patients, 674 (74%) underwent a resection with a median EOR of 97%, functional improvement in 71 (8%), functional decline in 78 (9%), and median survival of 12.8 months. The eRI and eRV identified biopsies and EORs of at least 80%, 90%, or 98% better than EG. The eRV and eRI predicted observed residual volumes under 10, 5, and 1 ml better than EG. The eRV, eRI, and EG had low diagnostic accuracy for functional outcome changes. Higher eRV and lower eRI were strongly associated with shorter survival, independent of known prognostic factors. CONCLUSIONS The eRV and eRI predict biopsy decisions, resectability, and survival better than eloquence grading and may be useful preoperative indices to support surgical decisions.
- Published
- 2021
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