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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".