Clinically significant incidental findings on the unenhanced CT portion of PET/CT studies: frequency in 250 patients.
Bibliographic record
Abstract
UNLABELLED: PET/CT technology is in rapid evolution. It remains unclear if the unenhanced CT portion, performed for attenuation correction and lesion localization, provides additional independent diagnostic information not apparent on PET alone. The objective of the current study was to evaluate the incremental added value and frequency of potentially clinically significant incidental findings from the independent reading of the unenhanced CT portion of PET/CT studies by an expert CT radiologist. METHODS: PET/CT was performed on 250 patients (123 men and 127 women; mean age, 56.5 y) referred for clinical evaluation of known or suspected cancer. Unenhanced CT studies were read without knowledge of findings from PET and PET/CT fused images. Findings from unenhanced CT were considered clinically significant if they were not detected or explained by PET findings and were considered, after examination of all available clinical data, to clearly require additional work-up. Small pulmonary nodules < 7 mm were not considered to require immediate work-up. RESULTS: Unenhanced CT revealed potentially clinically significant incidental findings in 7 patients. Three patients had indeterminate renal lesions, 1 patient had a solid renal mass, 1 patient had sclerotic bone metastases (albeit inactive on PET), 1 patient had liver cirrhosis with portal hypertension, and 1 patient had a 5 cm abdominal aortic aneurysm. These findings were generally not detected on PET. CONCLUSION: Clinically significant findings from the unenhanced CT portion of PET/CT are relatively infrequent (3%) but could be serious enough to warrant major alterations in clinical management. Thus, we believe it is most appropriate for the CT portion to be interpreted by a physician skilled in CT interpretation with special attention to the lesions that PET alone can fail to detect.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".