PURPOSE: To determine if density of breast is an independent risk factor which will contribute to development of breast cancer.
MATERIALS AND METHODS: A prospective cohort study is carried out in two hospitals targeting adult female patients who presented to the Breast Clinic with symptoms suspicious of breast cancer. Participants recruited were investigated for breast cancer based on their symptoms. Breast density assessed from mammogram was correlated with tissue biopsy results and final diagnosis of benign or malignant breast disease.
RESULTS: Participants with dense breasts showed 29% increased risk of breast cancer when compared to those with almost entirely fatty breasts (odds ratio [OR] 1.29, 95% CI 0.38-4.44, P = .683). Among the postmenopausal women, those with dense breasts were 3.1 times more likely to develop breast cancer compared with those with fatty breasts (OR 3.125, 95% CI 0.72-13.64, P = .13). Moreover, the chance of developing breast cancer increases with age (OR 1.046, 95% CI 1.003-1.090, P
METHODS: This work proposes a convolutional neural network (CNN) model for classifying IDC and metastatic breast cancer. The study utilized a large-scale dataset of microscopic histopathological images to automatically perceive a hierarchical manner of learning and understanding.
RESULTS: It is evident that using machine learning techniques significantly (15%-25%) boost the effectiveness of determining cancer vulnerability, malignancy, and demise. The results demonstrate an excellent performance ensuring an average of 95% accuracy in classifying metastatic cells against benign ones and 89% accuracy was obtained in terms of detecting IDC.
CONCLUSIONS: The results suggest that the proposed model improves classification accuracy. Therefore, it could be applied effectively in classifying IDC and metastatic cancer in comparison to other state-of-the-art models.
METHODS: Utilizing the Nationwide Readmissions Database (2018-2020), we identified patients and divided them into male and female groups. Hospital outcomes and complications were compared among these two groups after propensity score matching to match groups based on comorbidities, producing two comparable cohorts.
RESULTS: We analyzed 2928 patients (1832 males, 62.6%, mean age 60.3 ± 13.7 years; 1096 females, 37.4%, mean age 59.1 ± 13.8 years). After propensity score matching (1:1ratio), 1092 males and females were compared. There were no significant sex differences in early mortality (adjusted odd ratios (aOR): 1.04 [95% CI 0.69-1.57]), 30-day readmissions (aOR: 1.05 [95% CI 0.86-1.30]), or nonhome discharge (aOR: 0.89 [95% CI 0.60-1.31]). Females had higher odds of leukopenia (aOR: 1.26 [95% CI 1.06-1.50]) but lower odds of acute kidney injury (aOR: 0.68 [95% CI 0.52-0.88]).
CONCLUSIONS: No sex differences were found in hospital outcomes, including early mortality, 30-day readmission, and nonhome discharge after CAR T-cell therapy.