Does cardiac imaging surveillance strategy influence outcomes in patients with early breast cancer?
Bibliographic record
Abstract
Background: Many patients with breast cancer receive therapies with the potential to cause cardiotoxicity. Echocardiography and multiple-gated acquisition (MUGA) scans are the most used modalities to assess cardiac function during treatment in high-risk patients; however, the optimal imaging strategy and the impact on outcome are unknown. Methods: Consecutive patients with stage 0-3 breast cancer undergoing pre-treatment echocardiography or MUGA were identified from a tertiary care cancer center from 2010-2019. Demographics, medical history, imaging data and clinical events were collected from hospital charts and administrative databases. The primary outcome is a composite of all-cause death or heart failure event. Clinical and imaging predictors of outcome were evaluated on univariable and multivariable analyses. Results: 1028 patients underwent pre-treatment MUGA and 1032 underwent echocardiography. The groups were well matched for most clinical characteristics except patients undergoing MUGA were younger, had more stage 3 breast cancer and more HER2 over-expressing and triple negative cases. Routine follow-up cardiac imaging scan was obtained in 39.3% of patients with MUGA and 38.0% with echocardiography. During a median follow-up of 2448 (1489, 3160) days, there were 194 deaths, including 7 cardiovascular deaths, and 28 heart failure events with no difference in events between the MUGA and echocardiography groups. There were no imaging predictors of the primary composite outcome or cardiac events. Patients without follow-up imaging had similar adjusted risk for the composite outcome compared to those with imaging follow-up, hazard ratio 0.8 (95% confidence interval 0.5,1.3), p=0.457. Conclusion: The selection of pretreatment echocardiography or MUGA did not influence the risk of death or heart failure in patients with early breast cancer. Many patients did not have any follow-up cardiac imaging and did not suffer worse outcomes. Cardiovascular deaths and heart failure event rates were low and the value of long-term cardiac imaging surveillance should be further evaluated.
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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 | 0.001 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".