The relationship between body mass index and breast cancer recurrence/progression and breast cancer-specific death

The aim of this study was to describe the nonlinear association between body mass index (BMI) and breast cancer outcomes and to determine whether BMI improves prediction of outcomes. A cohort of906 breast cancer patients diagnosed at Henry Ford Health System, Detroit (1985-1990) were studied. The median follow-up was 10 years. Multivariate logistic regression was used to model breast cancer recurrence/progression and breast cancer-specific death. Restricted cubic splines were used to model nonlinear effects. Receiver operator characteristic areas under the curves (ROC AUC) were used to evaluate prediction. BMI was nonlinearly associated with recurrence/progression and death (p= 0.0230 and 0.0101). Probability of outcomes increased with increase or decrease ofBMI away from 25. BMI splines were suggestive of improved prediction of death. The ROC AUCs for nested models with and without BMI were 0.8424 and 0.8331 (p= 0.08). I f causally associated, modifying patients BMI towards 25 may improve outcomes.

Associations Between Personal Cancer History and Lung Cancer Risk

The study aim was to investigate the relationship between factors related to personal cancer history and lung cancer risk as well as assess their predictive utility. Characteristics of interest included the number, anatomical site(s), and age of onset of previous cancer(s). Data from the Prostate, Lung, Colorectal and Ovarian Screening (PLCO) Cancer Screening Trial (N = 154,901) and National Lung Screening Trial (N = 53,452) were analysed. Logistic regression models were used to assess the relationships between each variable of interest and 6-year lung cancer risk. Predictive utility was assessed through changes in area-under-the-curve (AUC) after substitution into the PLCOall2014 lung cancer risk prediction model. Previous lung, uterine and oral cancers were strongly and significantly associated with elevated 6-year lung cancer risk after controlling for confounders. None of these refined measures of personal cancer history offered more predictive utility than the simple (yes/no) measure already included in the PLCOall2014 model.

Predicting the Risk of Lung Cancer in Never-smokers

Despite being considered a disease of smokers, approximately 10-15% of lung cancer cases occur in never-smokers. Lung cancer risk prediction models have demonstrated excellent ability to discriminate cases from non-cases, and have been shown to be more efficient at selecting individuals for future screening than current criteria. Existing models have primarily been developed in populations of smokers, thus there was a need to develop an accurate model in never-smokers. This study focused on developing and validating a model using never-smokers from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. Cox regression analysis, with six-year follow-up, was used for model building. Predictors included: age, body mass index, education level, personal history of cancer, family history of lung cancer, previous chest X-ray, and secondhand smoke exposure. This model achieved fair discrimination (optimism corrected c-statistic = 0.6645) and good calibration. This represents an improvement on existing neversmoker models, but is not suitable for individual-level risk prediction.