Radiological Determinants of Postoperative Meningioma Remnants Progression
Bibliographic record
Abstract
Meningioma resection remains partial in over 25% of cases, with few known predictors of progression of residual disease. The aim of this study is to identify radiological determinants influencing progression of meningioma remnants after surgery. Such predictors may prove useful in identifying patients at elevated risk of recurrence and influence intraoperative decision-making regarding completeness of meningioma resection. This is a retrospective cohort study conducted on postoperative meningioma remnants. Various factors such as residue and initial tumor volume, residue attachment (dura, vessel, etc.), and T1 post-gadolinium and T2 intensity on MRI were assessed. Preoperative and postoperative volumetric data was collected for each lesion using three-dimensional segmentation software by two independent observers. Tumor growth rates were calculated based on this volumetric data. Recurrence was defined as a 25% increase in volume of the residue based on the first postoperative imaging. Sixty-one residues in 59 patients were included in this study. All lesions received a pathological diagnosis of a WHO grade 1 meningioma. Mean preoperative and first postoperative volumes were measured at 30.007 and 2.507 mL, respectively. Fifty-five lesions (90%) demonstrated attachment to the dura mater with no significant differences in recurrence. Altogether 51% of lesions showed attachment to a major blood vessel, 39% of lesions to a nerve, 10% to the brain parenchyma and 30% were localized within interdural space. Attachment to a blood vessel was significantly associated with lower recurrence rates (35 vs. 63%, p = 0.032). Interdural space residues demonstrated higher recurrence rates and lower cumulative survival rates through multivariate models (HR = 2.996, 95% CI: [1.233, 7.276]). Intensity ratios both preoperatively and postoperatively failed to demonstrate any difference in recurrence rates. Although larger preoperative volumes and maximal diameters showed elevated risks of recurrence through univariate analysis, they failed to detect any difference in multivariate models. Both univariate and multivariate models associated higher recurrence rates to extensive resections (low ratios of early postoperative to preoperative volume); however, patient survival remained unaffected. The attachments of meningioma residues to their environment were demonstrated to have a significant impact on their progression. This knowledge might influence intraoperative decision-making regarding the completeness of resection and location of residual disease voluntary left in place in some patients. Publication History Article published online: 05 February 2024 © 2024. Thieme. All rights reserved. Georg Thieme Verlag KG Rüdigerstraße 14, 70469 Stuttgart, Germany
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".