Use of Neuroimaging Techniques in Glioma Patients – Results of an International Survey on behalf of the EORTC Brain Tumor Group
Bibliographic record
Abstract
Ziel/Aim Multimodal imaging offers the potential to provide valuable diagnostic information in brain tumor patients. Considering the increasing number and availability of advanced neuroimaging techniques, selecting and applying the best modality may be therefore difficult. The present survey was carried out to evaluate the preferred use of various neuroimaging applications in patients with glioma. Methodik/Methods An online survey with 31 questions was distributed to 262 centers associated with the EORTC located in 34 countries. Subsequently, this survey was promoted by the EANO and the SNO via social media and newsletters. Ergebnisse/Results A total of 77 responses, predominantly from radiation oncologists (36%) and neurooncologists (31%), were evaluated. Most responses came from both university hospitals and research institutions (68%), mainly from Europe (89%). Almost half of these centers (48%) examined more than 100 glioma patients per year. All institutions had access to MRI, 94% to CT, 67% to PET or PET/CT, and 27% to hybrid PET/MRI. A total of 56% of institutions used the RANO criteria for the evaluation of imaging findings. In addition to structural MRI, most institutions routinely performed advanced MRI, followed by CT, PET, and hybrid PET/CT (74%, 42%, 32%, and 23%, respectively). Regarding PET, 64% of centers used the amino acid tracer O-(2-[ 18 F]fluoroethyl)-L-tyrosine, followed by 2-[ 18 F]-fluoro-2-deoxy-D-glucose (45%). Twenty-five percent of centers performed intraoperative MRI, and 10% intraoperative CT. In addition to structural MRI, the preferred additional imaging methods were perfusion-weighted MRI, diffusion-weighted MRI, amino acid PET, and proton MR spectroscopy (83%, 75%, 60% and 56%, respectively). Schlussfolgerungen/Conclusions The results of this international survey provide insights into the use of neuroimaging techniques in neuro-oncology centers. The availability, use, and assessment of neuroimaging in glioma patients varies from country to country. Our results highlight the importance of global activities towards further standardization of neuroimaging in brain tumor patients, such as the RANO working groups. Publication History Article published online: 14 April 2022 © 2022. Thieme. All rights reserved. Georg Thieme Verlag KG Rüdigerstraße 14, 70469 Stuttgart, Germany
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".