Analysis of Secondary Leukemia and Myelodysplastic Syndrome After Chemotherapy for Solid Organ Tumors Using the Food and Drug Administration Adverse Event Reporting System (FAERS)
Bibliographic record
Abstract
PURPOSE: As the prognosis of cancer patients deteriorates, secondary carcinogenesis after chemotherapy, especially secondary hematological malignancies, becomes a serious problem. However, information on the frequency and time of onset of secondary hematological malignancies and the risk of hematological malignancy with different drugs is scarce. This study aimed to evaluate the incidence of leukemia and myelodysplastic syndrome in patients with solid tumors, including breast, colon, gastric, pancreatic, small cell lung, non-small cell lung, esophageal, ovarian, cervical, and endometrial cancers. METHODS: Using the United States Food and Drug Administration Adverse Event Reporting System, we analyzed the reporting rates, reporting odds ratios, and the reporting onset times of secondary leukemia and myelodysplastic syndrome for each drug used. RESULTS: The leukemia reporting rates were higher in breast, small cell lung, ovarian, and endometrial cancers than in other cancers, and the myelodysplastic syndrome reporting rates were higher in ovarian and endometrial cancers than in other cancers. For each cancer type, the reporting odds ratios of cytocidal anticancer agents, such as taxanes, anthracyclines, alkylating agents, platinum, and topoisomerase inhibitors, were higher than those of other drugs. Alternatively, the reporting odds ratios of molecular targeted drugs and immune checkpoint inhibitors were not higher than those of other drugs. Approximately half of the cases of leukemia and myelodysplastic syndrome were reported within 1 to 4 years after chemotherapy. CONCLUSIONS: Our study clarified the risks of leukemia and myelodysplastic syndrome for several anticancer drugs in patients with solid tumors. Our data may aid in the assessment of the risks of secondary leukemia and myelodysplastic syndrome when medical oncologists, clinical pharmacists, and patients select chemotherapy regimens.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".