The Impact of Age and Adjuvant Chemotherapy Modifications on Survival Among Black Women With Breast Cancer

Racial Disparities in Breast Cancer Treatments and Adverse Events in the SEER-Medicare Data

Despite lower incidence rates, African American (AA) patients have shorter survival from breast cancer (BC) than white (W) patients. Multiple factors contribute to decreased survival, including screening disparities, later presentation, and access to care. Disparities in adverse events (AEs) may contribute to delayed or incomplete treatment, earlier recurrence, and shortened survival. Here, we analyzed the SEER-Medicare dataset, which captures claims from a variety of venues, in order to determine whether the cancer care venues affect treatment and associated adverse events. We investigated a study population whose claims are included in the Outpatient files, consisting of hospital and healthcare facility venues, and a study population from the National Claims History (NCH) files, consisting of claims from physicians, office practices, and other non-institutional providers. We demonstrated statistically and substantively significant venue-specific differences in treatment rates, drugs administered, and AEs from treatments between AA and W patients. We showed that AA patients in the NCH dataset received lower rates of treatment, but patients in the Outpatient dataset received higher rates of treatment than W patients. The rates of recorded AEs per treatment were higher in the NCH setting than in the Outpatient setting in all patients. AEs were consistently higher in AA patients than in W patients. AA patients had higher comorbidity indices and were younger than W patients, but these variables did not appear to play roles in the AE differences. The frequency of specific anticancer drugs administered in cancer- and venue-specific circumstances and their associated AEs varied between AA and W patients. The higher AE rates were due to slightly higher frequencies in the administration of drugs with higher associated AE rates in AA patients than in W patients. Our investigations demonstrate significant differences in treatment rates and associated AEs between AA and W patients with BC, depending on the venues of care, likely contributing to differences in outcomes.

Racial Differences in Breast Cancer Therapeutic Toxicity: Implications for Practice

Disparities in treatment intensity can contribute to racial disparities in overall breast cancer survival. A natural extension of measuring racial disparities in treatment intensity is consideration of the distribution of treatment toxicities, symptoms, and distress that lead to chemotherapy dose reductions, holds or early termination. There is growing evidence that therapeutic toxicity during early-stage breast cancer treatment may be greater among Black women than White. Important components of symptom management involve the communication of symptoms, the self-care abilities of the patient, the patient's perception of the clinical encounter, and the patient centeredness of the clinical encounter. Racial differences in the symptom reporting, the clinical "reception" and response to symptoms, the prescribed management, and the patient adherence to symptom management requires further investigation. Further research must also consider the structural inequities, as well as institutional and interpersonal racism that contribute to racial differences in cancer symptom burden leading to potential decreases in dose intensity of potentially life-saving early cancer treatment. See related article by Hu et al., p. 167.

Functional and Structural Connectome Features for Machine Learning Chemo-Brain Prediction in Women Treated for Breast Cancer with Chemotherapy

Breast cancer is the leading cancer among women worldwide, and a high number of breast cancer patients are struggling with psychological and cognitive disorders. In this study, we aim to use machine learning models to discriminate between chemo-brain participants and healthy controls (HCs) using connectomes (connectivity matrices) and topological coefficients. Nineteen female post-chemotherapy breast cancer (BC) survivors and 20 female HCs were recruited for this study. Participants in both groups received resting-state functional magnetic resonance imaging (rs-fMRI) and generalized q-sampling imaging (GQI). Logistic regression (LR), decision tree classifier (CART), and xgboost (XGB) were the models we adopted for classification. In connectome analysis, LR achieved an accuracy of 79.49% with the functional connectomes and an accuracy of 71.05% with the structural connectomes. In the topological coefficient analysis, accuracies of 87.18%, 82.05%, and 83.78% were obtained by the functional global efficiency with CART, the functional global efficiency with XGB, and the structural transitivity with CART, respectively. The areas under the curves (AUCs) were 0.93, 0.94, 0.87, 0.88, and 0.84, respectively. Our study showed the discriminating ability of functional connectomes, structural connectomes, and global efficiency. We hope our findings can contribute to an understanding of the chemo brain and the establishment of a clinical system for tracking chemo brain.