Role of Three-Dimensional Echocardiography in Breast Cancer: Comparison With Two-Dimensional Echocardiography, Multiple-Gated Acquisition Scans, and Cardiac Magnetic Resonance Imaging
Bibliographic record
Abstract
PURPOSE: In patients with breast cancer, the administration of doxorubicin and trastuzumab is associated with an increased risk of cardiotoxicity. Although multiple-gated acquisition (MUGA) scans and two-dimensional transthoracic echocardiography (TTE) are conventional methods for baseline and serial assessment of left ventricular ejection fraction (LVEF) in these patients, little is known about the use of real-time three-dimensional TTE (RT3D TTE) in this clinical setting. The aim of this study was to assess the accuracy of MUGA, 2D TTE, and RT3D TTE for determining LVEF in comparison to cardiac magnetic resonance imaging (CMR). METHODS: Between 2007 and 2009 inclusive, 50 female patients with human epidermal growth factor receptor 2-positive breast cancer received adjuvant trastuzumab after doxorubicin. Serial MUGA, 2D TTE, RT3D TTE, and CMR were performed at baseline, 6, and 12 months after the initiation of trastuzumab. RESULTS: A comparison of left ventricular end diastolic volume (LVEDV) demonstrated a modest correlation between 2D TTE and CMR (r = 0.64 at baseline; r = 0.69 at 12 months, respectively). A comparison of LVEDV between RT3D TTE and CMR demonstrated a stronger correlation (r = 0.87 at baseline; r = 0.95 at 12 months, respectively). Although 2D TTE demonstrated a weak correlation with CMR for LVEF assessment (r = 0.31 at baseline, r = 0.42 at 12 months, respectively), both RT3D TTE and MUGA showed a strong correlation when compared with CMR (r = 0.91 at baseline; r = 0.90 at 12 months, respectively). CONCLUSION: As compared with conventional MUGA, RT3D TTE is a feasible, accurate, and reproducible alternate imaging modality for the serial monitoring of LVEF in patients with breast cancer.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.000 | 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 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".