Oligodendroglial neoplasms with ganglioglioma-like maturation: a diagnostic pitfall
Bibliographic record
Abstract
Although oligodendroglial neoplasms are traditionally considered purely glial, increasing evidence suggests that they are capable of neuronal or neurocytic differentiation. Nevertheless, ganglioglioma-like foci (GGLF) have not been previously described. Herein, we report seven examples where the primary differential diagnosis was a ganglioglioma with an oligodendroglial component. These five male and two female patients ranged in age from 29 to 63 (median 44) years at initial presentation and neuroimaging features were those of diffuse gliomas in general. At presentation, the glial component was oligodendroglioma in six and oligoastrocytoma in one; one was low-grade and six were anaplastic. A sharp demarcation from adjacent GGLF was common, although some intermingling was always present. The GGLF included enlarged dysmorphic and occasionally binucleate ganglion cells, Nissl substance, expression of neuronal antigens, GFAP-positive astrocytic elements, and low Ki-67 labeling indices. In contrast to classic ganglioglioma, however, cases lacked eosinophilic granular bodies and CD34-positive tumor cells. Scattered bizarre astrocytes were also common and one case had focal neurocytic differentiation. By FISH analysis, five cases showed 1p/19q codeletion. In the four cases with deletions and ample dysmorphic ganglion cells for analysis, the deletions were found in both components. At last follow-up, two patients suffered recurrences, one developed radiation necrosis mimicking recurrence, and one died of disease 7.5 years after initial surgery. We conclude that GGLF represents yet another form of neuronal differentiation in oligodendroglial neoplasms. Recognition of this pattern will prevent a misdiagnosis of ganglioglioma with its potential for under-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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".