Intensive FDG-PET/CT Uptake Suggestive of Malignancy Misleading the Diagnosis of Sclerosing Pneumocytoma
Bibliographic record
Abstract
Introduction: Combined Positron Emission Tomography-Computed Tomography with 18-fluoro-desoxy-glucose (FDG-PET/CT) is highly sensitive in differentiating malignant from benign pulmonary lesions and is part of the current recommended practices for non-invasive lung nodule assessment. However, many solid pulmonary nodules may show misleading miscellaneous features and can be mistakenly diagnosed as malignant lesions. Case Report: Herein we report the case of a passive smoking female patient with multiple comorbidities, who was referred for a solitary pulmonary nodule randomly discovered. Chest imaging showed a middle lobe 16-mm nodule with an intensive uptake (SUVmax 7.6) highly suggestive of malignant origin. The patient underwent middle lobectomy with radical lymphadenectomy because the malignancy was not excluded on frozen section. Definitive pathological examination showed a sclerosing pneumocytoma. Conclusion: FDG-PET/CT is an accurate imaging tool for assessment of solid pulmonary nodules. However, false positive results of some benign lesions have to be kept in mind. Therefore, FDG-PET/CT features have to be interpreted according to the patients background and clinical data, in order to provide the best appropriate management.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".