DIPG-46. NON-DIPG PATIENTS ENROLLED IN THE INTERNATIONAL DIPG REGISTRY: HISTOPATHOLOGIC EVALUATION OF CENTRAL NEURO-IMAGING REVIEW
Bibliographic record
Abstract
Abstract INTRODUCTION The role of diagnostic biopsy in diffuse intrinsic pontine glioma (DIPG) remains in question. Distinguishing radiographically between DIPG and other pontine tumors with more favorable prognosis and different therapy is critically important. METHODS Cases submitted to the International DIPG registry with histopathologic data were analyzed. Central imaging review was performed by two neuro-radiologists; all cases with imaging features or histopathology suggestive of alternative diagnoses were re-reviewed. Imaging features suggestive of alternative diagnoses included non-pontine origin, <50% pontine involvement (without typical DIPG pattern on follow-up), focally exophytic morphology, sharply-defined margins, or marked diffusion restriction throughout. RESULTS Among 297 patients with pathology from biopsy and/or autopsy available, 27 (9%) had histologic diagnoses not consistent with DIPG, commonly embryonal tumors (n=9) and pilocytic astrocytomas (n=11). 163 patients had diagnostic MRI available for central neuroimaging review. Among 81 patients classified as characteristic of DIPG, 80 (99%) had histopathology consistent with DIPG (diffuse midline glioma, H3K27M-mutant, glioblastoma, anaplastic astrocytoma, diffuse astrocytoma). Among 63 patients classified as likely DIPG, but with unusual imaging features, 59 (94%) had histopathology consistent with DIPG. 19 patients had imaging features suggestive of another diagnosis, including 13 with non-pontine tumor origin; the remaining 6 all had histopathology not consistent with DIPG. Association between central imaging review and histopathology was significant (p<0.001). CONCLUSIONS The important role and accuracy of central neuroimaging review in diagnosing or excluding DIPG is demonstrated. In patients with pontine tumors for which DIPG is felt unlikely radiographically, biopsy may be considered to guide diagnosis and treatment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".