The Global, Regional, and National Burden and Trends of Breast Cancer From 1990 to 2019: Results From the Global Burden of Disease Study 2019

Xu, Shangbo; Liu, Yiyuan; Zhang, Taofeng; Zheng, Jiehua; Lin, Weixun; Cai, Jiehui; Zou, Juan; Chen, Yaokun; Xie, Yanna; Chen, Yexi; Li, Zhiyang

doi:10.3389/fonc.2021.689562

Cited by 30 publications

(59 citation statements)

References 45 publications

(63 reference statements)

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“…It is important to note, that of the 20.6 million disability-adjusted life years (DALY) due to breast cancer, 20.3 million were among women [ 1 ]. In 2019, breast cancer had the highest mortality rate among females, with more than 688 thousand deaths, and accounted for 15.9% of all cancer-related deaths [ 2 ]. Moreover, the global number of female breast cancer cases increased by 128.3% between 1990 and 2019 [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

“…In 2019, breast cancer had the highest mortality rate among females, with more than 688 thousand deaths, and accounted for 15.9% of all cancer-related deaths [ 2 ]. Moreover, the global number of female breast cancer cases increased by 128.3% between 1990 and 2019 [ 2 ]. There were also large increases in the number of incident cases (377.9%), deaths (203.7%) and DALYs (197.2%) attributable to breast cancer in the Middle East and North Africa (MENA) region over the same period [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

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Burden of female breast cancer in the Middle East and North Africa region, 1990–2019

et al. 2022

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Background There is no region-specific publication investigating the attributable burden of breast cancer, particularly among females. This article reported the burden of female breast cancer in the Middle East and North Africa (MENA) region, and its attributable risk factors between 1990 and 2019, by age, sex, and socio-demographic index (SDI). Methods Publicly available data on the incidence, death and disability-adjusted life years (DALY) were retrieved from the Global Burden of Disease (GBD) 2019 study for the 21 countries and territories in MENA, between 1990 and 2019, along with 95% uncertainty intervals (UIs). The relationship between the burden of female breast cancer, in terms of DALYs, and the SDI were also assessed using Smoothing Spline models. Results In 2019, the regional age-standardised incidence and death rates of female breast cancer were 37.5 and 15.2 per 100,000, which represent a 90.9 and 24.0% increase since 1990, respectively. In addition, in 2019 the regional age-standardised DALY rate was 472.7 per 100,000, which was 19.5% higher than in 1990. In 2019, the death rate increased steadily with advancing age, while the DALY rate increased steeply with age and reached its peak in the 70–74 age group. There was a positive association between SDI and the burden of breast cancer over the period 1990 to 2019. Moreover, in 2019 high fasting plasma glucose (6.9%) contributed to the largest proportion of attributable DALYs for female breast cancer in the MENA region. Conclusions There was a significant increase in the incidence rate of female breast cancer in MENA over the past three decades, although the death and DALY rates were both largely unchanged. Preventive programs targeting the major risk factors should be implemented in the region.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Burden of female breast cancer in the Middle East and North Africa region, 1990–2019

et al. 2022

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“…Global statistics on breast cancer in 2021 showed that the disease remains the most diagnosed cancer among women. The study revealed that between 1990 and 2019, global breast cancer cases rose to 2,002,354 and recorded 700,660 deaths in 2019 1 . The burden associated with new cases and disease mortality is disturbing, as 33% and 81% of cases are in ages 30–49 and 30–59 years, respectively 2 .…”

Section: Introductionmentioning

confidence: 97%

A novel wavelet decomposition and transformation convolutional neural network with data augmentation for breast cancer detection using digital mammogram

Oyelade

Ezugwu

2022

Sci Rep

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Research in deep learning (DL) has continued to provide significant solutions to the challenges of detecting breast cancer in digital images. Image preprocessing methods and architecture enhancement techniques have been proposed to improve the performance of DL models such as convolutional neural networks (CNNs). For instance, the wavelet decomposition function has been used for image feature extraction in CNNs due to its strong compactness. Additionally, CNN architectures have been optimized to improve the process of feature detection to support the classification process. However, these approaches still lack completeness, as no mechanism exists to discriminate features to be enhanced and features to be eliminated for feature enhancement. More so, no studies have approached the use of wavelet transform to restructure CNN architectures to improve the detection of discriminant features in digital mammography for increased classification accuracy. Therefore, this study addresses these problems through wavelet-CNN-wavelet architecture. The approach presented in this paper combines seam carving and wavelet decomposition algorithms for image preprocessing to find discriminative features. These features are passed as input to a CNN-wavelet structure that uses the new wavelet transformation function proposed in this paper. The CNN-wavelet architecture applied layers of wavelet transform and reduced feature maps to obtain features suggestive of abnormalities that support the classification process. Meanwhile, we synthesized image samples with architectural distortion using a generative adversarial network (GAN) model to argue for their training datasets' insufficiency. Experimentation of the proposed method was carried out using DDSM + CBIS and MIAS datasets. The results obtained showed that the new method improved the classification accuracy and lowered the loss function values. The study's findings demonstrate the usefulness of the wavelet transform function in restructuring CNN architectures for performance enhancement in detecting abnormalities leading to breast cancer in digital mammography.

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“…Breast cancer is a deadliest type of female carcinoma ( Xu et al, 2021 ), the incidence of breast cancer is increasing in the past few years ( Hu et al, 2021 ; Su et al, 2021 ; Tagliamento et al, 2021 ; Xu et al, 2021 ). Although the treatment of breast cancer has achieved great progress ( Li et al, 2021 ; Mouabbi et al, 2021 ), 5-years survival rate for advanced breast cancer is still poor ( Kang et al, 2018 ; Wang et al, 2021a ).…”

Section: Introductionmentioning

confidence: 99%

Identification of Five Cytotoxicity-Related Genes Involved in the Progression of Triple-Negative Breast Cancer

et al. 2022

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Background: Breast cancer is one of the deadly tumors in women, and its incidence continues to increase. This study aimed to identify novel therapeutic molecules using RNA sequencing (RNA-seq) data of breast cancer from our hospital.Methods: 30 pairs of human breast cancer tissue and matched normal tissue were collected and RNA sequenced in our hospital. Differentially expressed genes (DEGs) were calculated with raw data by the R package “edgeR”, and functionally annotated using R package “clusterProfiler”. Tumor-infiltrating immune cells (TIICs) were estimated using a website tool TIMER 2.0. Effects of key genes on therapeutic efficacy were analyzed using RNA-seq data and drug sensitivity data from two databases: the Cancer Cell Line Encyclopedia (CCLE) and the Cancer Therapeutics Response Portal (CTRP).Results: There were 2,953 DEGs between cancerous and matched normal tissue, as well as 975 DEGs between primary breast cancer and metastatic breast cancer. These genes were primarily enriched in PI3K-Akt signaling pathway, calcium signaling pathway, cAMP signaling pathway, and cell cycle. Notably, CD8+ T cell, M0 macrophage, M1 macrophage, regulatory T cell and follicular helper T cell were significantly elevated in cancerous tissue as compared with matched normal tissue. Eventually, we found five genes (GALNTL5, MLIP, HMCN2, LRRN4CL, and DUOX2) were markedly corelated with CD8+ T cell infiltration and cytotoxicity, and associated with therapeutic response.Conclusion: We found five key genes associated with tumor progression, CD8+ T cell and therapeutic efficacy. The findings would provide potential molecular targets for the treatment of breast cancer.

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The Global, Regional, and National Burden and Trends of Breast Cancer From 1990 to 2019: Results From the Global Burden of Disease Study 2019

Cited by 30 publications

References 45 publications

Burden of female breast cancer in the Middle East and North Africa region, 1990–2019

Burden of female breast cancer in the Middle East and North Africa region, 1990–2019

A novel wavelet decomposition and transformation convolutional neural network with data augmentation for breast cancer detection using digital mammogram

Identification of Five Cytotoxicity-Related Genes Involved in the Progression of Triple-Negative Breast Cancer

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