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Abstract

Breast cancer (BC) is the most frequently diagnosed cancer in women worldwide with more than 2 million new cases in 2020.Its incidence and death rates have increased over the last three decades due to the change in risk factor profiles, better cancer registration, and cancer detection. The number of risk factors of BC is significant and includes both the modifiable factors and non-modifiable factors. Currently, about 80% of patients with BC are individuals aged >50.Survival depends on both stage and molecular subtype. Invasive BCs comprise wide spectrum tumors that show a variation concerning their clinical presentation, behavior, and morphology. Based on mRNA gene expression levels, BC can be divided into molecular subtypes (Luminal A, Luminal B, HER2-enriched, and basal-like).The molecular subtypes provide insights into new treatment strategies and patient stratifications that impact the management of BC patients. The eighth edition of TNM classification outlines a new staging system for BC that, in addition to anatomical features, acknowledges biological factors. Treatment of breast cancer is complex and involves a combination of different modalities including surgery, radiotherapy, chemotherapy, hormonal therapy, or biological therapies delivered in diverse sequences. Body fatness is a dynamic exposure throughout life. To provide more insight into the association between body mass index (BMI) and postmenopausal breast cancer, we aimed to examine the age at onset, duration, intensity, and trajectories of body fatness in adulthood in relation to risk of breast cancer subtypes. Based on self-reported anthropometry in the prospective Norwegian Women and Cancer Study, we calculated the age at onset, duration, and intensity of overweight and obesity using linear mixed-effects models. BMI trajectories in adulthood were modeled using group based trajectory modeling. We used Cox proportional hazards models to calculate hazard ratios (HRs) with 95% confidence intervals (Cis) for the associations between BMI exposures and breast cancer subtypes in 148,866 postmenopausal women. Advanced glycation end products (AGEs) are reactive metabolites intrinsically linked with modern dietary patterns. Processed foods, and those high in sugar, protein and fat, often contain high levels of AGEs. Increased AGE levels are associated with increased breast cancer risk, however their significance has been largely overlooked due to a lack of direct cause-and-effect relationship. Immunohistochemistry and immunofluorescence were used to assess cellular proliferation and stromal fibroblast and macrophage recruitment. The Kruskal–Wallis test were used to compare continuous outcomes among groups. Mammary epithelial cell migration and invasion in response to AGE-mediated fibroblast activation was determined in two compartment co-culture models. In vitro experiments were performed in triplicate. The nonparametric Wilcoxon rank sum test was used to compare differences between groups. Deep learning analysis of radiological images has the potential to improve diagnostic accuracy of breast cancer, ultimately leading to better patient outcomes. This paper systematically reviewed the current literature on deep learning detection of breast cancer based on magnetic resonance imaging (MRI). The literature search was performed from 2015 to Dec 31, 2022, using Pubmed. Other database included Semantic Scholar, ACM Digital Library, Google search, Google Scholar, and pre-print depositories (such as Research Square). Articles that were not deep learning (such as texture analysis) were excluded. PRISMA guidelines for reporting were used. We analyzed different deep learning algorithms, methods of analysis, experimental design, MRI image types, types of ground truths, sample sizes, numbers of benign and malignant lesions, and performance in the literature.

Keywords

Breast cancer

Reference

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Photo
Hariom Rajput
Corresponding author

GV2C+4M7, Jhanjrola, Haryana 124105

Photo
Dr. Mahi Rajput
Co-author

GV2C+4M7, Jhanjrola, Haryana 124105

Hariom Rajput*, Dr. Mahi Rajput, Breast Cancer, Int. J. in Pharm. Sci., 2023, Vol 1, Issue 11, 245-259. https://doi.org/10.5281/zenodo.10118747

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