Fluid-attenuated Inversion Recovery Ring Sign as a Marker of Dysembryoplastic Neuroepithelial Tumors
Bibliographic record
Abstract
PURPOSE: The aim of our study is to describe the hyperintense ring sign on fluid-attenuated inversion recovery (FLAIR) images in patients with dysembryoplastic neuroepithelial tumors (DNET), to discuss the radiopathologic correlation for this appearance, and to determine its role in preoperative diagnosis of DNETs. MATERIALS AND METHODS: We retrospectively analyzed imaging features in 11 patients with pathological diagnosis of DNET. All patients had undergone surgery for refractory seizures. All had FLAIR imaging sequences performed on a 1.5-T magnetic resonance scanner. Clinical and pathological details in all cases were examined. Twenty-one age matched patients with pathologically confirmed low-grade glioma (n = 11), oligodendroglioma (n = 2), and ganglioglioma (n = 8) in similar locations acted as control cases. Ten patients had follow-up imaging. RESULTS: There were 11 patients with DNET (5 girls and 6 boys). The age of presentation varied from 4 to 18 years (average, 9 years 1 month). Tumors were located in the temporal (n = 5), frontal (n = 4), parietal (n = 1), and occipital (n = 1) lobes. In 9 patients (82% sensitivity), the FLAIR images showed a well-defined hyperintense ring around these tumors, either as a complete or incomplete ring. Among the 21 control cases, the hyperintense ring sign was seen in 2 cases (90% specificity): one with low-grade glioma and one with ganglioglioma. Pathological evaluation of the DNETs suggested the hyperintense ring might correspond to the presence of peripheral loose neuroglial elements. Postoperative imaging showed partial residual ring in 3 patients, all of whom had persistent seizures. One patient had recurrent DNET at second surgery. CONCLUSION: Magnetic resonance imaging findings of DNET are well described. We describe an additional imaging sign, the hyperintense ring sign on FLAIR images, which is distinct and is fairly sensitive and specific for DNET. We believe this sign is a helpful adjuvant to preoperatively diagnose these tumors. The presence of this ring on postoperative imaging may indicate residual or recurrent tumor.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 |
| 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".