Abstract P6-13-08: A novel comparative analysis approach to personalize chemotherapy dose in early breast cancer
Bibliographic record
Abstract
Abstract Background: Worldwide, body surface area [BSA] is used to calculate chemotherapy dose. The BSA formula was originally developed in 1916, derived from height and weight, with no consideration of other patient characteristics. Most chemotherapy agents have a narrow therapeutic index and are distributed in lean body mass [LBM], leading to under- or over-dosing and deleterious effects to major organs when body composition is not considered. To date, while experts worldwide acknowledge the limitations and risks of BSA dosing, no practical approach to personalizing chemotherapy dose has been developed. Ideally, body composition would be assessed by tests already routinely performed, avoiding unnecessary radiation exposure, clinic visits, discomfort to the patient, and cost. The majority of patients undergo cardiac imaging prior to chemotherapy. We hypothesized that clinical parameters routinely performed prior to chemotherapy could predict LBM in early breast cancer patients. Method: Early stage breast cancer patients (n = 45) enrolled in the Multidisciplinary Team Intervention in Cardio-Oncology (TITAN) study underwent pre-treatment cardiac MRI, body composition (iDEXA) and laboratory (complete blood cell count and chemistry). Cardiac MRI and iDEXA are considered 'gold standard' imaging modalities, the accuracy of which allow for significantly reduced sample size. Our modeling approach, which is novel in this area, aimed to select the best combination of parameters with the most predictive ability of total lean mass (iDEXA). The parameters included in study are: cardiac MRI metrics (LV mass, cardiac output), and laboratory parameters associated with major organ function (albumin, creatinine, bilirubin). All parameters were tested using univariate, multivariate and subset selection approach. Akaike's Information Criterion (AIC) was used to measure model quality, with lower AIC values indicating closer prediction. Results: The univariate analysis of each parameter independently showed LV mass is most predictive with AIC 857.8, while combination of all parameter in multivariate fashion show improvement in prediction with AIC 851. The subset selection approach shows, Adjusted R2 with 4 parameters had AIC 849.14, Schwartz's information criterion (BIC) with 2 parameters had AIC 849.66 and Mallows' C Selection (Cp) model with 3 parameters had the least AIC 848.71 value (P < 0.001). Conclusion: Our comparative analysis showed that the Cp model with 3 parameters (LV mass, cardiac output and bilirubin) has high prediction ability of LBM. This model will form the basis of a personalized formula for chemotherapy dose calculation. We expect this work to result in optimal cancer-specific outcomes while reducing short and long-term toxicities associated with necessary chemotherapy. Citation Format: Perri MD, Singhal S, Hegadoren K, Norris C, Mackey J, Paterson I, Pituskin E. A novel comparative analysis approach to personalize chemotherapy dose in early breast cancer [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P6-13-08.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".