The Epidemiology of Breast Cancer

Arzanova, Evelina; Mayrovitz, Harvey N.

doi:10.36255/exon-publications-breast-cancer-epidemiology

Cited by 17 publications

(12 citation statements)

References 68 publications

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“…In 2020 breast cancer became the single most diagnosed malignancy across the world 1 . It is the 5th greatest cause of mortality and the most common cause of cancer death in women worldwide.…”

Section: Introductionmentioning

confidence: 99%

AI-based density prediction for breast cancer prevention: can we measure mammographic density in just one breast?

Perry,

Romanov,

Howell

et al. 2024

Medical Imaging 2024: Computer-Aided Diagnosis

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Breast density is an important factor in assessing individual breast cancer risk. We aim to identify women at increased risk of developing breast cancer before they enter routine screening, using mammography in combination with known risk factors. This will enable targeting of preventive therapies and personalised screening. To reduce radiation risk, this paper examines whether density measurements in one breast or mammographic view could be used to accurately reflect individual risk.We analysed breast cancer risk using breast density in a 1:3 case-control dataset of mammograms from the Predicting Risk of Cancer at Screening Study (PROCAS). Breast density was measured using pVAS, an AI-based approach. Cancer risk in low and high breast density groups was compared using conditional logistic regression.High breast density was independently associated with increased breast cancer risk. Women in the highest breast density quintile averaged across all views had an Odds Ratio (OR) of 4.16 (95% CI 2.90-5.97) compared to those in the lowest. A similar OR was found in both the left 3.77 (95% CI 2.68-5.31) and right 4.52 (95% CI 3.12-6.55) breasts individually. ORs were also significant for each individual view: right mediolateral oblique (MLO) 4.19 (2.92-6.00), right craniocaudal (CC) 4. ).The ability to predict breast cancer risk due to increased breast density was achieved using one breast and even one mammographic view. This provides the possibility of a pre-screening risk assessment using fewer images and therefore less radiation.

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Section: Introductionmentioning

confidence: 99%

AI-based density prediction for breast cancer prevention: can we measure mammographic density in just one breast?

Perry,

Romanov,

Howell

et al. 2024

Medical Imaging 2024: Computer-Aided Diagnosis

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show abstract

“…The bulk of cancer-related deaths worldwide among women are caused by the most prevalent malignancy in women, breast cancer (1,2). The migration of malignant cells to other organs, particularly the lungs and brain, is the main reason for breast cancer deaths (3).…”

Section: Introductionmentioning

confidence: 99%

Survival, Adhesion and Migration of Metastatic Breast Tumor Cells: Roles of Parvins (PARVA and PARVB)

Dylami,

Farahani,

Kia

et al. 2024

Preprint

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Changes in cell-ECM interaction can lead to the anchorage-independent proliferation, resistance to apoptosis, and improved motility that characterize breast metastatic cells. The parvin, alpha (Parva) and parvin, beta (Parvb) localizes to focal adhesions and play a part in cell attachment, migration, and persistence. The assumption of our work is that parvins contribute to the capacity of tumor cells to spread. Following the extraction of primary (4T1T) and metastatic (4T1B and 4T1L) breast tumor cells from mouse metastatic breast cancer model, MTT assay and scratch test were utilized to determine the vitality and motility of the cells. qPCR was performed to examine the expression of parvins. Tumor cells did not show any difference in cellular viability in 2- or 3-dimensional cell culture. Our research illustrated that metastatic tumor cells have a higher capacity of migration. Molecular analysis revealed that parvins expression was significantly increased in metastatic breast carcinoma cells. These findings revealed new information about a major change in parvins expression in metastatic breast carcinoma cells. A targeted therapeutic approach against breast cancer metastasis could be developed using examination of the genetic characteristics of metastatic tumor cells.

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“…Globally, the prevalence of breast cancer is rising, and in the United States, 364 000 instances will be predicted by 2040. 1 According to Globocan and the data from the Cancer Department of the Ministry of Health of the Republic of Turkey, Turkey’s overall incidence rate climbed from 32.3 per 100 000 in 1998 to 60 per 100 000 in 2018. 2 , 3…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence in Breast Imaging: Opportunities, Challenges, and Legal–Ethical Considerations

Durur Subasi,

Özçelik

2024

The Eurasian Journal of Medicine

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This review explores the transformative impact of artificial intelligence (AI) in breast imaging, driven by a global rise in breast cancer cases. Propelled by deep learning techniques, AI shows promise in refining diagnostic processes, yet adoption rates vary. Its ability to manage extensive datasets and process multidimensional information holds potential for advancing precision medicine in breast cancer research. However, integration faces challenges, from data-related obstacles to ensuring transparency and trust in decision-making. Legal considerations, including the formation of AI teams and intellectual property protection, influence health care’s adoption of AI. Ethical dimensions underscore the need for responsible AI implementation, emphasizing autonomy, well-being, safety, transparency, and accessibility. Establishing a robust legal and ethical framework is crucial for conscientiously deploying AI, ensuring positive impacts on patient safety and treatment efficacy. As nations and organizations aspire to engage in global competition, not merely as consumers, the review highlights the critical importance of developing legal regulations. A comprehensive approach, from AI team formation to end-user processes, is essential for navigating the complex terrain of AI applications in breast imaging. Legal experts play a key role in ensuring compliance, managing risks, and fostering resilient integration. The ultimate goal is a harmonious synergy between technological advancements and ethical considerations, ushering in enhanced breast cancer diagnostics through responsible AI utilization.

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The Epidemiology of Breast Cancer

Cited by 17 publications

References 68 publications

AI-based density prediction for breast cancer prevention: can we measure mammographic density in just one breast?

AI-based density prediction for breast cancer prevention: can we measure mammographic density in just one breast?

Survival, Adhesion and Migration of Metastatic Breast Tumor Cells: Roles of Parvins (PARVA and PARVB)

Artificial Intelligence in Breast Imaging: Opportunities, Challenges, and Legal–Ethical Considerations

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