Purpose: The COVID-19 pandemic created significant disruptions in the diagnosis and treatment of breast cancer (BC). Several public health measures were taken with limited evidence on their potential impact. In this observational study, we sought to compare the incidence of BC, treatment patterns, and mortality during 2020 versus 2018 and 2019.
Methods: Using the Surveillance, Epidemiology, and End Results program, we identified 37,834 patients with ductal carcinoma in situ (DCIS) and 199,594 with invasive BC between 2018 and 2020. We assessed age-adjusted incidence rates of DCIS and invasive BC as cases per 100,000, treatment patterns, and mortality in 2020 versus 2018 and 2019.
Results: From 2019 to 2020, the incidence of female DCIS decreased from 36.4 to 31.0, and the incidence of female invasive BC decreased from 184.2 to 166.6. Among females, the relative reductions in incidence from 2019 to 2020 were 14.8% for DCIS, 12.1% for stage I, 5.8% for stage II, 2.6% for stage III, and 1.9% for stage IV. Comparing 2020 to 2018-2019 in invasive BC, we observed significant changes in treatment patterns with decreased use of surgery or radiation and increased use of chemotherapy. The 12-month mortality rates were 4.49%, 4.37%, and 4.57% for 2018, 2019 and 2020, respectively. In the Cox model, there were no significant differences in mortality between patients diagnosed in 2020 versus 2018 or 2019.
Conclusions: During 2020, the incidence of BC decreased significantly. There were reductions in surgery and radiation use, but not in chemotherapy. Although vaccines were largely unavailable and COVID-19 treatments were in development, we saw no differences in 12-month mortality in 2020 versus prior years. The impact on BC-specific outcomes requires further follow-up.
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http://dx.doi.org/10.1007/s10549-024-07562-w | DOI Listing |
Biomed Phys Eng Express
January 2025
School of Engineering and Computing, University of the West of Scotland, University of the West of Scotland - Paisley Campus, Paisley PA1 2BE, UK, City, Paisley, PA1 2BE, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND.
Cancer grade classification is a challenging task identified from the cell structure of healthy and abnormal tissues. The partitioner learns about the malignant cell through the grading and plans the treatment strategy accordingly. A major portion of researchers used DL models for grade classification.
View Article and Find Full Text PDFACS Chem Neurosci
January 2025
Department of Bioengineering and Biotechnology, Birla Institute of Technology Mesra, Ranchi, Jharkhand 835215, India.
Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline, extracellular amyloid-β (Aβ) plaque accumulation, and intracellular neurofibrillary tangles. Recent efforts to find effective therapies have increased interest in natural compounds with multifaceted effects on AD pathology. This study explores natural compounds for their potential to mitigate AD pathology using molecular docking, ADME screening, and assays, with ruscogenin─a steroidal sapogenin from emerging as a promising candidate.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
Department of Psychiatry, Yongin Severance Hospital, Yongin, Republic of Korea.
Background: The COVID-19 pandemic has accelerated the digitalization of modern society, extending digital transformation to daily life and psychological evaluation and treatment. However, the development of competencies and literacy in handling digital technology has not kept pace, resulting in a significant disparity among individuals. Existing measurements of digital literacy were developed before widespread information and communications technology device adoption, mainly focusing on one's perceptions of their proficiency and the utility of device operation.
View Article and Find Full Text PDFJ Am Med Inform Assoc
January 2025
Institute of Data Science, National University of Singapore, 117602, Singapore.
Objectives: This study introduces Smart Imitator (SI), a 2-phase reinforcement learning (RL) solution enhancing personalized treatment policies in healthcare, addressing challenges from imperfect clinician data and complex environments.
Materials And Methods: Smart Imitator's first phase uses adversarial cooperative imitation learning with a novel sample selection schema to categorize clinician policies from optimal to nonoptimal. The second phase creates a parameterized reward function to guide the learning of superior treatment policies through RL.
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