Carcinoembryonic Antigen Increase in a Patient with Colon Cancer Who Have Achieved Complete Remission and Negative 18F-FDG PET/CT: Don’t Forget the Thyroid!
Bibliographic record
Abstract
Serum carcinoembryonic antigen (CEA) is a tumor marker especially used to follow a patient with colorectal cancer. However, it is non-specific and could be increased in several cancers and some benign conditions. We report the case of a 70-year-old man followed since 2014 for a left colon adenocarcinoma with the persistence of an increased CEA. There was no evidence of recurrence, but a right lobar thyroid nodule without a significantly increased uptake was incidentally discovered on the CT scan of 18F-fluorodeoxyglucose (18F-FDG) PET/CT. We suspected a medullary thyroid carcinoma (MTC) explaining the persistent elevation of CEA. Plasma calcitonin levels were 47 ng/L (N < 10). Fine needle aspiration cytology found atypia of undetermined significance and the patient was reluctant to undergo surgery without any further exploration. We performed a 18F-fluorodihydroxyphenylalanine (18F-FDOPA) PET/CT preoperatively which revealed a punctiform focus of the right thyroid lobe corresponding to a pT1aN1aMxR0 medullary thyroid carcinoma, histopathologically confirmed. This case highlights that despite the potential usefulness of 18F-FDG PET/CT in case of an unknown source of elevated CEA this imaging may be falsely negative as in the case of MTC and should lead to further explorations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| 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".