Mitigating Financial Toxicity in Breast Cancer From Diagnosis to Treatment and Reconstruction

Using hybrid pre-trained models for breast cancer detection

Breast cancer is a prevalent and life-threatening disease that affects women globally. Early detection and access to top-notch treatment are crucial in preventing fatalities from this condition. However, manual breast histopathology image analysis is time-consuming and prone to errors. This study proposed a hybrid deep learning model (CNN+EfficientNetV2B3). The proposed approach utilizes convolutional neural networks (CNNs) for the identification of positive invasive ductal carcinoma (IDC) and negative (non-IDC) tissue using whole slide images (WSIs), which use pre-trained models to classify breast cancer in images, supporting pathologists in making more accurate diagnoses. The proposed model demonstrates outstanding performance with an accuracy of 96.3%, precision of 93.4%, recall of 86.4%, F1-score of 89.7%, Matthew's correlation coefficient (MCC) of 87.6%, the Area Under the Curve (AUC) of a Receiver Operating Characteristic (ROC) curve of 97.5%, and the Area Under the Curve of the Precision-Recall Curve (AUPRC) of 96.8%, which outperforms the accuracy achieved by other models. The proposed model was also tested against MobileNet+DenseNet121, MobileNetV2+EfficientNetV2B0, and other deep learning models, proving more powerful than contemporary machine learning and deep learning approaches.

Financial Toxicity among Patients with Breast Cancer during the COVID-19 Pandemic in the United States

This study reported the prevalence of financial distress (financial toxicity (FT)) and COVID-19-related economic stress in patients with breast cancer (BC). Patients with BC were recruited from the Ciitizen platform, Breastcancer.org, and patient advocacy groups between 30 March and 6 July 2021. FT was assessed with the COmprehensive Score for financial Toxicity (COST) instrument. COVID-19-related economic stress was assessed with the COVID-19 Stress Scale. Among the 669 patients, the mean age was 51.6 years; 9.4% reported a COVID-19 diagnosis. The prevalence rates of mild and moderate/severe FT were 36.8% and 22.4%, respectively. FT was more prevalent in patients with metastatic versus early BC (p < 0.001). The factors associated with FT included income ≤ USD 49,999 (adjusted odds ratio (adj OR) 6.271, p < 0.0001) and USD 50,000-USD 149,999 (adj OR 2.722, p < 0.0001); aged <50 years (adj OR 3.061, p = 0.0012) and 50-64 years (adj OR 3.444, p = 0.0002); living alone (adj OR 1.603, p = 0.0476); and greater depression severity (adj OR 1.155, p < 0.0001). Black patients (adj OR 2.165, p = 0.0133), patients with income ≤ USD 49,999 (adj OR 1.921, p = 0.0432), or greater depression severity (adj OR 1.090, p < 0.0001) were more likely to experience COVID-19-related economic stress. FT was common in patients with BC, particularly metastatic disease, during COVID-19. Multiple factors, especially lower income and greater depression severity were associated with financial difficulties during COVID-19.