Cost-Sensitive Learning for Recurrence Prediction of Breast Cancer

Breast cancer is one of the top cancer-death causes and specifically accounts for 10.4% of all cancer incidences among women. The prediction of breast cancer recurrence has been a challenging research problem for many researchers. Data mining techniques have recently received considerable attention, especially when used for the construction of prognosis models from survival data. However, existing data mining techniques may not be effective to handle censored data. Censored instances are often discarded when applying classification techniques to prognosis. In this paper, we propose a cost-sensitive learning approach to involve the censored data in prognostic assessment with better recurrence prediction capability. The proposed approach employs an outcome inference mechanism to infer the possible probabilistic outcome of each censored instance and adopt the cost-proportionate rejection sampling and a committee machine strategy to take into account these instances with probabilistic outcomes during the classification model learning process. We empirically evaluate the effectiveness of our proposed approach for breast cancer recurrence prediction and include a censored-data-discarding method (i.e., building the recurrence prediction model by only using uncensored data) and the Kaplan-Meier method (a common prognosis method) as performance benchmarks. Overall, our evaluation results suggest that the proposed approach outperforms its benchmark techniques, measured by precision, recall and F1 score

The association between tyrosine kinase inhibitors and fatal arrhythmia in patients with non-small cell lung cancer in Taiwan

ObjectiveAs a standard therapy, tyrosine kinase inhibitors (TKIs) improved survival in patients with non-small cell lung cancer (NSCLC) and epidermal growth factor receptor (EGFR) mutation. However, treatment-related cardiotoxicity, particularly arrhythmia, cannot be ignored. With the prevalence of EGFR mutations in Asian populations, the risk of arrhythmia among patients with NSCLC remains unclear.MethodsUsing data from the Taiwanese National Health Insurance Research Database and National Cancer Registry, we identified patients with NSCLC from 2001 to 2014. Using Cox proportional hazards models, we analyzed outcomes of death and arrhythmia, including ventricular arrhythmia (VA), sudden cardiac death (SCD), and atrial fibrillation (AF). The follow-up duration was three years.ResultsIn total, 3876 patients with NSCLC treated with TKIs were matched to 3876 patients treated with platinum analogues. After adjusting for age, sex, comorbidities, and anticancer and cardiovascular therapies, patients receiving TKIs had a significantly lower risk of death (adjusted HR: 0.767; CI: 0.729–0.807, p < 0.001) than those receiving platinum analogues. Given that approximately 80% of the studied population reached the endpoint of mortality, we also adjusted for mortality as a competing risk. Notably, we observed significantly increased risks of both VA (adjusted sHR: 2.328; CI: 1.592–3.404, p < 0.001) and SCD (adjusted sHR: 1.316; CI: 1.041–1.663, p = 0.022) among TKI users compared with platinum analogue users. Conversely, the risk of AF was similar between the two groups. In the subgroup analysis, the increasing risk of VA/SCD persisted regardless of sex and most cardiovascular comorbidities.ConclusionsCollectively, we highlighted a higher risk of VA/SCD in TKI users than in patients receiving platinum analogues. Further research is needed to validate these findings