NIMG-24. RANO CRITERIA DETECTS EARLY PROGRESSION SOONER THAN MODIFIED RANO CRITERIA IN PATIENTS WITH NEWLY DIAGNOSED GLIOBLASTOMA
Bibliographic record
Abstract
Abstract BACKGROUND Accurate response criteria are crucial for determining treatment efficacy. The response assessment in neuro-oncology (RANO) criteria was developed to standardize response assessment in neuro-oncology. Modified RANO (m-RANO) criteria were recently proposed to address some limitations of the initial criteria including the use of a post-radiation baseline and an additional scan to confirm progression. We sought to identify differences in the association of progression-free survival (PFS) and overall survival (OS) using RANO and m-RANO criteria. METHODS We conducted a retrospective review of newly diagnosed glioblastoma (GBM) patients treated at Dana-Farber Cancer Institute from January 2013 until December 2019. Patients with available clinical and imaging data obtained before initiation of treatment, after radiation completion and at intervals of 1 to 3 months were included. MRIs were evaluated by two independent readers, and PD dates determined using RANO and m-RANO criteria. RESULTS 552 patients were included. 97.1% of the tumors were IDH wild-type. MGMT promoter was unmethylated in 51.4%, methylated in 35.1% and undetermined in 8.5%. Median OS among patients was 18.1 months. 72 patients (13%) did not have PD at the end of the study. 83 patients had treatment change while being clinically stable and without a confirmation scan and were excluded from the final analysis. PFS was 8.2 months with RANO and 8.4 months with mRANO. Difference in PD dates between RANO and m-RANO was detected in 76 patients (14%), where PFS was 3.5 months with RANO and 5.1 months with m-RANO. These patients had a worse median OS than those with identical RANO and m-RANO PD dates (15.2 vs. 22.4 months, p< 0.0001). CONCLUSION RANO and m-RANO criteria resulted in identical PFS for most patients. 14% of patients had discordant PD dates and a worse prognosis. These patients progressed early, and their PD was identified sooner with RANO criteria.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".