Accuracy of central neuro-imaging review of DIPG compared with histopathology in the International DIPG Registry
Bibliographic record
Abstract
BACKGROUND: Diffuse intrinsic pontine glioma (DIPG) remains a clinico-radiologic diagnosis without routine tissue acquisition. Reliable imaging distinction between DIPG and other pontine tumors with potentially more favorable prognoses and treatment considerations is essential. METHODS: Cases submitted to the International DIPG registry (IDIPGR) with histopathologic and/or radiologic data were analyzed. Central imaging review was performed on diagnostic brain MRIs (if available) by two neuro-radiologists. Imaging features suggestive of alternative diagnoses included nonpontine origin, <50% pontine involvement, focally exophytic morphology, sharply defined margins, and/or marked diffusion restriction throughout. RESULTS: Among 286 patients with pathology from biopsy and/or autopsy, 23 (8%) had histologic diagnoses inconsistent with DIPG, most commonly nondiffuse low-grade gliomas and embryonal tumors. Among 569 patients with centrally-reviewed diagnostic MRIs, 40 (7%) were classified as non-DIPG, alternative diagnosis suspected. The combined analysis included 151 patients with both histopathology and centrally-reviewed MRI. Of 77 patients with imaging classified as characteristic of DIPG, 76 (99%) had histopathologic diagnoses consistent with DIPG (infiltrating grade II-IV gliomas). Of 57 patients classified as likely DIPG with some unusual imaging features, 55 (96%) had histopathologic diagnoses consistent with DIPG. Of 17 patients with imaging features suggestive of an alternative diagnosis, eight (47%) had histopathologic diagnoses inconsistent with DIPG (remaining patients were excluded due to nonpontine tumor origin). Association between central neuro-imaging review impression and histopathology was significant (p < 0.001), and central neuro-imaging impression was prognostic of overall survival. CONCLUSIONS: The accuracy and important role of central neuro-imaging review in confirming the diagnosis of DIPG is demonstrated.
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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 | 0.006 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".