Body Composition as a Predictor of Toxicity in Patients Receiving Anthracycline and Taxane–Based Chemotherapy for Early-Stage Breast Cancer
Bibliographic record
Abstract
Abstract Purpose: Poor body composition metrics (BCM) are associated with inferior cancer outcomes; however, in early breast cancer (EBC), there is a paucity of evidence regarding the impact of BCM on toxicities. This study investigates associations between BCM and treatment-related toxicity in patients with EBC receiving anthracyclines and taxane–based chemotherapy. Experimental Design: Pretreatment computerized tomographic (CT) images were evaluated for skeletal muscle area (SMA), skeletal muscle density (SMD), and fat tissue at the third lumbar vertebrae. Skeletal muscle index (SMI = SMA/height2) and skeletal muscle gauge (SMG = SMI × SMD) were also calculated. Relative risks (RR) are reported for associations between body composition measures and toxicity outcomes, after adjustment for age and body surface area (BSA). Results: BCM were calculated for 151 patients with EBC (median age, 49 years; range, 23–75 years). Fifty patients (33%) developed grade 3/4 toxicity, which was significantly higher in those with low SMI (RR, 1.29; P = 0.002), low SMG (RR, 1.09; P = 0.01), and low lean body mass (RR, 1.48; P = 0.002). Receiver operating characteristic analysis showed the SMG measure to be the best predictor of grade 3/4 toxicity. Dividing SMG into tertiles showed toxicity rates of 46% and 22% for lowest versus highest tertile, respectively (P = 0.005). After adjusting for age and BSA, low SMG (<1,475 units) was significantly associated with hematologic (RR, 2.12; P = 0.02), gastrointestinal grade 3/4 toxicities (RR, 6.49; P = 0.02), and hospitalizations (RR, 1.91; P = 0.05). Conclusions: Poor BCMs are significantly associated with increased treatment-related toxicities. Further studies are needed to investigate how these metrics can be used to more precisely dose chemotherapy to reduce treatment-related toxicity while maintaining efficacy. Clin Cancer Res; 23(14); 3537–43. ©2017 AACR.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".