Using Histopathology to Assess the Reliability of Intraoperative Magnetic Resonance Imaging in Guiding Additional Brain Tumor Resection: A Multicenter Study
Bibliographic record
Abstract
BACKGROUND: Intraoperative magnetic resonance imaging (iMRI) is a powerful tool for guiding brain tumor resections, provided that it accurately discerns residual tumor. OBJECTIVE: To use histopathology to assess how reliably iMRI may discern additional tumor for a variety of tumor types, independent of the indications for iMRI. METHODS: A multicenter database was used to calculate the odds of additional resection during the same surgical session for grade I to IV gliomas and pituitary adenomas. The reliability of iMRI for identifying residual tumor was assessed using histopathology of tissue resected after iMRI. RESULTS: Gliomas (904/1517 cases, 59.6%) were more likely than pituitary adenomas (176/515, 34.2%) to receive additional resection after iMRI (P < .001), but these tumors were equally likely to have additional tissue sent for histopathology (398/904, 44.4% vs 66/176, 37.5%; P = .11). Tissue samples were available for resections after iMRI for 464 cases, with 415 (89.4%) positive for tumor. Additional resections after iMRI for gliomas (361/398, 90.7%) were more likely to yield additional tumor compared to pituitary adenomas (54/66, 81.8%) (P = .03). There were no significant differences in resection after iMRI yielding histopathologically positive tumor between grade I (58/65 cases, 89.2%; referent), grade II (82/92, 89.1%) (P = .98), grade III (72/81, 88.9%) (P = .95), or grade IV gliomas (149/160, 93.1%) (P = .33). Additional resection for previously resected tumors (122/135 cases, 90.4%) was equally likely to yield histopathologically confirmed tumor compared to newly-diagnosed tumors (293/329, 89.0%) (P = .83). CONCLUSION: Histopathological analysis of tissue resected after use of iMRI for grade I to IV gliomas and pituitary adenomas demonstrates that iMRI is highly reliable for identifying residual tumor.
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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 | 0.018 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".