MR Imaging and Clinical Characteristics of Diffuse Glioneuronal Tumor with Oligodendroglioma-like Features and Nuclear Clusters
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Diffuse glioneuronal tumor with oligodendroglioma-like features and nuclear clusters (DGONC) is a new, molecularly defined glioneuronal CNS tumor type. The objective of the present study was to describe MR imaging and clinical characteristics of patients with DGONC. MATERIALS AND METHODS: Preoperative MR images of 9 patients with DGONC (median age at diagnosis, 9.9 years; range, 4.2–21.8 years) were reviewed. RESULTS: All tumors were located superficially in the frontal/temporal lobes and sharply delineated, displaying little mass effect. Near the circle of Willis, the tumors encompassed the arteries. All except one demonstrated characteristics of low-to-intermediate aggressiveness with high-to-intermediate T2WI and ADC signals and bone remodeling. Most tumors (n = 7) showed a homogeneous ground-glass aspect on T2-weighted and FLAIR images. On the basis of the original histopathologic diagnosis, 6 patients received postsurgical chemo-/radiotherapy, 2 were irradiated after surgery, and 1 patient underwent tumor resection only. At a median follow-up of 61 months (range, 10–154 months), 6 patients were alive in a first complete remission and 2 with stable disease 10 and 21 months after diagnosis. The only patient with progressive disease was lost to follow-up. Five-year overall and event-free survival was 100% and 86±13%, respectively. CONCLUSIONS: This case series presents radiomorphologic characteristics highly predictive of DGONC that contrast with the typical aspects of the original histopathologic diagnoses. This presentation underlines the definition of DGONC as a separate entity, from a clinical perspective. Complete resection may be favorable for long-term disease control in patients with DGONC. The efficacy of nonsurgical treatment modalities should be evaluated in larger series.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".