Prevalence and Significance of Incidental PET/CT Findings of Cancer Detected in Patients Evaluated for Their Primary Hematologic Malignancy: A Systematic Review
Bibliographic record
Abstract
In the evaluation of a patient’s primary hematologic malignancy, positron emission tomography/computed tomography (PET/CT) imaging may incidentally detect a concerning abnormality suggestive of a second concurrent cancer. Despite accounting for nearly 10% of all cancers diagnosed in Canada, there has yet to be a systematic review focused on the prevalence and significance of these incidental PET/CT findings in the context of primary hematologic malignancies. As such, a systematic search strategy was employed on MEDLINE and Embase to document the prevalence and clinical significance of incidental PET/CT findings suggestive of a second concurrent cancer detected in patients evaluated for their primary hematologic malignancy. Thirteen studies published between 2008 and 2022 were reviewed, including conference abstracts (n = 8) and journal articles (n = 5). Clinically significant incidental cancers were detected with a median of 2.4% (range: 1.1–10.3%) in patients with myeloma/plasma cell disorders, compared to a median of 1.5% (range: 0.3–2.8%) in patients with lymphoproliferative diseases. The most common anatomic regions of clinically significant incidental malignancies were identified in the gastrointestinal tract (44.4%), followed by the thyroid gland (22.2%) and lungs (7.9%). In most cases, early detection of incidental cancers led to successful early interventions. PET/CT scans occasionally identify second primary malignancies that require additional attention. These findings may affect the treatment of a patient’s primary hematologic malignancy, and as such, timely coordinated management is important for improved outcomes. This review may inform physicians and administrators of the risk of incidental second malignancies and may highlight a need for enhanced cancer treatment pathways.
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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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.014 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".