Correlation Between MRI Findings and Histological Diagnosis of Brainstem Glioma
Bibliographic record
Abstract
OBJECTIVE: In most studies, treatment decisions of brainstem glioma are based solely on MRI features and do not incorporate a histopathological diagnosis. In the current study, we sought to compare MRI characteristics with histopathological findings of bainstem glioma. METHODS: From April 2003 through April 2012, 150 patients were diagnosed with brainstem gliomas by MRI and microsurgically treated in Tiantan Hospital, Beijing, China. All the MRI and histopathological findings of these patients were respectively reviewed. RESULTS: Of the 150 patients, 65 were female and 85 were male, 120 were adults and 30 were children (age < 18 years), 108 were low-grade glioma (72.0%), 35 were high-grade glioma (23.3%). The accuracy of the MRI diagnosis for brainstem glioma was 95.3%. Data analysis of the MRI findings revealed that a focal lesion was associated with a more favorable histopathological diagnosis in intrinsic (P=0.005) and exophytic (P=0.001) brainstem glioma patients. In the intrinsic diffuse type, tumors without enhancement had more favorable pathological findings (P=0.009). CONCLUSIONS: To our knowledge, this is the largest case series of this nature reported in the literature to date. The results of this study suggest that MRI features of brainstem gliomas could predict some pathological features and guide prognosis, choice of biopsy and treatment modalities. The pathology of tumors with a focal appearance on MRI was associated with a prognosis that was significantly better than their diffuse counterparts. For the intrinsic diffuse gliomas, non-enhancing tumors had pathology suggestive of a favorable prognosis.
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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 | 0.005 |
| 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) | 0.002 | 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".