OS11.5.A PREOPERATIVE ASSESSMENT OF TUMOR ELOQUENCE AND RESECTABILITY: AN INTERNATIONAL SURVEY
Abstract BACKGROUND Tumor location and its proximity to eloquent brain areas are key factors in glioma surgery decision-making. However, the absence of a consensus definition for eloquent brain areas leads to variability in surgical decision-making. This survey aimed to assess this heterogeneity in defining eloquent brain regions. MATERIAL AND METHODS A survey was distributed among neurosurgeons in the United States, Europe, Latin America, and Australasia between February and November 2023. Respondents rated the eloquence of various brain structures on a Likert scale and reported their use of preoperative techniques. Twelve glioma and glioblastoma cases were presented to assess opinions on tumor location eloquence and preferred surgical approaches. RESULTS 157 neurosurgeons from 25 countries responded to the survey. Two-thirds (68%) agreed on the need for a standardized definition of eloquence, while only 23% applied existing eloquence grading scales. Eloquence ratings varied, with the highest variation reported for the corona radiata, uncinate fasciculus and superior longitudinal fasciculus. In patient cases, variability was observed at four levels of decision-making: 1) degree of eloquence; 2) preferred surgical modality; 3) use of intraoperative mapping; 4) the preferred mapping modality (asleep or awake). CONCLUSION This survey highlights the variability in defining eloquence and its impact on glioma surgery decision-making. This lack of consensus limits the reliability of eloquence as a descriptor of tumor location, affecting patient care and comparability across studies. Future research should focus on the development of an easy-to-use, objective method (based on intraoperative data) for identifying eloquent brain regions preoperatively.
Authors
- Mitchel Stuart Berger (ORCID: https://orcid.org/0000-0003-1983-4892)
- Sandro M. Krieg (ORCID: https://orcid.org/0000-0003-4050-1531)
- Arnaud J.P.E. Vincent (ORCID: https://orcid.org/0000-0003-0865-8272)
- Joost W. Schouten (ORCID: https://orcid.org/0000-0002-9266-2815)
- Philippe Schucht (ORCID: https://orcid.org/0000-0002-8025-3694)
- Jasper K W Gerritsen
- Takashi Maruyama (ORCID: https://orcid.org/0000-0001-7959-8831)
- Shawn L. Hervey‐Jumper (ORCID: https://orcid.org/0000-0003-4699-260X)
- Timothy R. Smith (ORCID: https://orcid.org/0000-0003-1976-0766)
- Steven De Vleeschouwer (ORCID: https://orcid.org/0000-0002-9576-9917)
- Jordina Rincon-Toroella
- Arthur Wagner (ORCID: https://orcid.org/0000-0001-9947-2240)
- Marike L. D. Broekman (ORCID: https://orcid.org/0000-0002-0226-0119)
- Chetan Bettegowda (ORCID: https://orcid.org/0000-0001-9991-7123)
- Jacob Young (ORCID: https://orcid.org/0000-0003-3563-425X)
- Eelke M. Bos (ORCID: https://orcid.org/0000-0003-3647-6740)
- Brian V. Nahed (ORCID: https://orcid.org/0000-0001-8537-106X)
- Christine Jungk (ORCID: https://orcid.org/0009-0004-7911-0268)
- Emma Rammeloo (ORCID: https://orcid.org/0009-0002-8932-2905)
Institutions
- Brigham and Women's Hospital (US)
- Johns Hopkins University (US)
- Tokyo Medical University (JP)
- University of California, San Francisco (US)
- Tokyo City University (JP)
- Heidelberg University (DE)
- University Hospital of Bern (CH)
- Erasmus MC (NL)
- University Hospital Heidelberg (DE)
- Massachusetts General Hospital (US)
- Medisch Centrum Haaglanden (NL)
- Technical University of Munich (DE)
- Erasmus University Rotterdam (NL)
- KU Leuven (BE)
Publication Details
- Journal
- Neuro-Oncology
- Published
- 2025-10-01
- DOI
- https://doi.org/10.1093/neuonc/noaf193.073
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00