Impact of Intraoperative Magnetic Resonance Imaging and Other Factors on Surgical Outcomes for Newly Diagnosed Grade II Astrocytomas and Oligodendrogliomas: A Multicenter Study
Bibliographic record
Abstract
BACKGROUND: Few studies use large, multi-institutional patient cohorts to examine the role of intraoperative magnetic resonance imaging (iMRI) in the resection of grade II gliomas. OBJECTIVE: To assess the impact of iMRI and other factors on overall survival (OS) and progression-free survival (PFS) for newly diagnosed grade II astrocytomas and oligodendrogliomas. METHODS: Retrospective analyses of a multicenter database assessed the impact of patient-, treatment-, and tumor-related factors on OS and PFS. RESULTS: A total of 232 resections (112 astrocytomas and 120 oligodendrogliomas) were analyzed. Oligodendrogliomas had longer OS (P < .001) and PFS (P = .01) than astrocytomas. Multivariate analyses demonstrated improved OS for gross total resection (GTR) vs subtotal resection (STR; P = .006, hazard ratio [HR]: .23) and near total resection (NTR; P = .02, HR: .64). GTR vs STR (P = .02, HR: .54), GTR vs NTR (P = .04, HR: .49), and iMRI use (P = .02, HR: .54) were associated with longer PFS. Frontal (P = .048, HR: 2.11) and occipital/parietal (P = .003, HR: 3.59) locations were associated with shorter PFS (vs temporal). Kaplan-Meier analyses showed longer OS with increasing extent of surgical resection (EOR) (P = .03) and 1p/19q gene deletions (P = .02). PFS improved with increasing EOR (P = .01), GTR vs NTR (P = .02), and resections above STR (P = .04). Factors influencing adjuvant treatment (35.3% of patients) included age (P = .002, odds ratio [OR]: 1.04) and EOR (P = .003, OR: .39) but not glioma subtype or location. Additional tumor resection after iMRI was performed in 105/159 (66%) iMRI cases, yielding GTR in 54.5% of these instances. CONCLUSION: EOR is a major determinant of OS and PFS for patients with grade II astrocytomas and oligodendrogliomas. Intraoperative MRI may improve EOR and was associated with increased PFS.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".