Magnetic Resonance Spectroscopy and Single-Photon Emission Computed Tomography in the Evaluation of Cerebral Tumors: A Case Report
Bibliographic record
Abstract
In their daily clinical practice, physicians have to confront diagnostic dilemmas which cannot be resolved by the application of only one imaging technique. In this case report, we present a 66-year-old woman who was admitted to our institution for the surgical resection of a recently diagnosed brain tumor. The patient had a history of epileptic seizures and was hospitalized in the past for anti-phospholipid syndrome related to a non-Hodgkin lymphoma in remission. Magnetic resonance imaging (MRI) examination revealed an enhancing right parasagittal lesion with significant edema suggestive of a high grade glioma. Advanced MRI techniques including proton magnetic resonance spectroscopy ( 1 H-MRS) showed findings compatible of glioma. An additional examination was performed as part of a protocol that we are routinely performing in our institution for all brain tumors including not only the gold standard advanced MRI techniques but also single-photon emission computed tomography (SPECT) with technetium-99m (Tc99m). Brain SPECT indicated the presence of a meningioma which was verified by the histopathology of the resected specimen. In conclusion, a multimodality approach for the pre-surgical assessment of brain tumors has significant advantages not only for the diagnosis but also for the evaluation of intracranial tumors histology. J Clin Med Res. 2017;9(1):74-78 doi: https://doi.org/10.14740/jocmr2775w
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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) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".