T2 Hypointensity Aids in the Diagnosis of Intracranial Metastatic Adenocarcinoma
Bibliographic record
Abstract
BACKGROUND: The T2 hypointensity has been suggested to be associated with intracranial metastatic adenocarcinomas (IMA). The purpose of our study was to determine the association of T2 hypointensity with IMA. METHODS: All patients with pathologically confirmed metastatic brain tumors who had a magnetic resonance imaging (MRI) at our institution in the last 10 years were retrospectively assessed. Qualitative assessment of the lesions on MRI was done by two separate readers who were blinded to the pathological diagnosis. For qualitative assessment, the T2 hypointensity in the lesion was compared with the contralateral normal appearing white matter. Odds ratio, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated. RESULTS: Of 107 patients with intracranial metastasis, only 73 (40 females; 33 males; mean age 61 years) had MRI available for review. Of these, only 46 (25 females; 21 males; mean age 61 years) had pathologically proven IMA. T2 hypointensity was seen in 20% of IMA. The odds ratio of T2 hypointensity in IMA was 3 compared to nonadenocarcinomas but was not statistically significant (p = 0.16). Intralesional hemorrhage was seen in 20. When controlled for hemorrhage, the odds ratio for T2 hypointensity in IMA was 4.7. The specificity, sensitivity, PPV, and NPV for T2 hypointensity to diagnose IMA were 92%, 19%, 81%, and 40%, respectively. CONCLUSION: T2 hypointensity was seen only in 20% of IMA with an odds ratio of 4.7. T2 hypointensity showed a high specificity and PPV for diagnosis of IMA.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".