18F-FDG PET/CT versus conventional investigations for cancer screening in autoimmune inflammatory myopathy in the era of novel myopathy classifications
Bibliographic record
Abstract
BACKGROUND: To compare the performance of fluorine-18-fluorodeoxyglucose (F-FDG) PET/computed tomography (CT) and conventional tests for cancer screening in autoimmune inflammatory myopathy (AIM) patients. PATIENTS AND METHODS: We carried out a retrospective cohort study of AIM patients from one academic center in Montreal, Canada, classified using myositis-specific antibodies, who underwent F-FDG PET/CT between April 2005 and February 2018 and were followed up on average 3.5±2.4 years. Patients were excluded if follow-up was insufficient, AIM diagnosis was indeterminate, and/or malignancy was diagnosed before an F-FDG PET/CT scan. Demographic/clinical data, F-FDG PET/CT results, and available conventional screening tests results were retrieved from electronic and paper medical records. RESULTS: 100 F-FDG PET/CT studies in 63 unique patients [31/63 dermatomyositis (DM), 25/63 overlap myositis, 1/63 inclusion body myositis, 1/63 polymyositis, 1/63 orbital myositis and 4/63 unspecified myositis] were evaluated. Three patients, all classified as DM, were diagnosed with cancer during follow-up with conventional cancer screening tests: breast cancer detected by mammography; squamous cell carcinoma of the skin detected by physical examination; and multiple myeloma detected by blood work. F-FDG PET/CT did not detect any malignancy and led to more additional biopsies than conventional screening (8 vs. 5). CONCLUSION: F-FDG PET/CT does not appear to be useful in cancer screening for AIM patients compared with conventional screening and carries potential harms associated with follow-up investigations. The risk of cancer in AIM differs by myositis-specific antibodies-defined subsets and cancer screening is likely to be indicated only in high-risk patients, particularly DM. These results, replicated in larger, multicentered studies, may carry significant consequences for optimal management of AIM and health resource utilization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".