CLRM-07 RANO 2.0: PROPOSAL FOR AN UPDATE TO THE RESPONSE ASSESSMENT IN NEURO-ONCOLOGY (RANO) CRITERIA FOR HIGH- AND LOW-GRADE GLIOMAS IN ADULTS
Bibliographic record
Abstract
Abstract The Response Assessment in Neuro-Oncology (RANO) criteria for high-grade gliomas (RANO-HGG) and low-grade gliomas (RANO-LGG) were developed to improve the reliability of response assessment in glioma trials. Over time some limitations of these criteria were identified, and uncertainty emerged regarding integrating features of the modified RANO (mRANO) or the immunotherapy RANO (iRANO) criteria. Informed by data from a cohort of glioblastoma patients and other evaluations of RANO criteria that allowed evaluation of features of the different criteria, we propose updates to the RANO criteria (RANO 2.0). Based on the 2021 WHO classification of gliomas, we recommend a standard set of criteria for both high and low-grade gliomas, to be used for all trials regardless of the treatment modalities being evaluated. In the newly diagnosed setting, the post-radiotherapy MRI, rather than the post-surgical MRI, will be used as the baseline for future comparison. Since the incidence of pseudoprogression is high in the 12 weeks following radiotherapy, continuation of treatment and confirmation of progression during this period with a repeat MRI, or histopathologic evidence of unequivocal recurrent tumor, is required to define tumor progression. However, confirmation scans are not mandatory after this period nor for recurrent tumors. For treatments with a high likelihood of pseudoprogression, mandatory confirmation of progression with a repeat MRI is an option. The primary measurement remains the maximum cross-sectional area of tumor (2-dimensional) but volumetric measurements are an option. For IDH-wildtype glioblastoma, the non-enhancing disease will no longer be evaluated. In IDH-mutated tumors with a significant non-enhancing component, clinical trials may require evaluating both the enhancing and non-enhancing tumor components for response assessment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".