Breast cancer incidence and mortality in women in China: temporal trends and projections to 2030

Lei, Shaoyuan; Zheng, Rongshou; Zhang, Siwei; Chen, Ru; Wang, Shaoming; Sun, Kexin; Zeng, Hongmei; Wei, Wenqiang; He, Jie

doi:10.20892/j.issn.2095-3941.2020.0523

Cited by 141 publications

(81 citation statements)

References 35 publications

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“…The National Central Cancer Registry of China, for instance, was established in 2002, and the number of local registries increased to 501 by August 2018, which covered 387 million population [ 31 ]. Published studies estimated the breast cancer burden in China based on the high-quality parts of total registries, which could only involve about 10% of the total population [ 31 , 32 ]. Besides, it was worth noting that the individuals in high socioeconomic regions, such as Western Europe and High-income North America, were significantly less physically active than the global average levels, which might be the reason for the heaviest burden occurring in these higher SDI regions.…”

Section: Discussionmentioning

confidence: 99%

The global, regional, and national disease burden of breast cancer attributable to low physical activity from 1990 to 2019: an analysis of the Global Burden of Disease Study 2019

Yin

Zhang

et al. 2022

Int J Behav Nutr Phys Act

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Background To assess the spatiotemporal variation in female breast cancer attributable to low physical activity (LPA) at a global scale from 1990 to 2019, which is essential to promote physical activity, as well as prevent and control breast cancer. Methods The number of deaths and disability-adjusted life years (DALYs), and the corresponding age-standardized rates (ASMR and ASDR) of LPA-related breast cancer in 204 countries and territories from 1990 to 2019 were retrieved from the Global Burden of Disease Study 2019 to measure the related breast cancer burden by age and region. The estimated annual percentage change (EAPC) was calculated to quantify the secular trend in breast cancer burden rates. Results From 1990 to 2019, globally, both breast cancer deaths and DALYs attributable to LPA nearly doubled, although the corresponding ASMR and ASDR decreased slightly, with EAPC of -0.46 (95% confidence interval: -0.52, -0.40) and -0.44 (95% confidence interval: -0.49, -0.39), respectively. The LPA-related breast cancer burden varied considerably across the world, with the highest-burden rates in Oceania, Tropical Latin America and Caribbean, and the fastest growth in North Africa and Middle East. The ASMR and ASDR showed a logarithmic association with the Socio-demographic Index, and a temporally upward trend in most of 204 countries regardless of the Socio-demographic Index or the ASMR in 1990. Conclusions Despite a decline in LPA-related breast cancer burden achieved in many countries during the last 3 decades like Bermuda, Myanmar, USA and China, an increase still occurred in most of 204 countries and territories, such as Solomon Islands, Equatorial Guinea, Japan and India. The findings can bring greater awareness to the importance of promoting physical activity for the local government to control the attributable breast cancer burden.

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Section: Discussionmentioning

confidence: 99%

The global, regional, and national disease burden of breast cancer attributable to low physical activity from 1990 to 2019: an analysis of the Global Burden of Disease Study 2019

Yin

Zhang

et al. 2022

Int J Behav Nutr Phys Act

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Section: Introductionmentioning

confidence: 99%

“…In China, approximately 303,600 new cases of breast cancer and 70,400 breast cancer-related deaths occurred in 2015, and the incidence and mortality of this disease continue to increase [1][2][3]. There is no sign that this trend will stop by 2030, particularly in rural areas [3]. The goal of early diagnosing breast cancer is to reduce mortality and provide patients with the most appropriate and least aggressive treatment options [4].…”

Section: Introductionmentioning

confidence: 99%

Deep learning based on ultrasound images assists breast lesion diagnosis in China: a multicenter diagnostic study

Lin

et al. 2022

Insights Imaging

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Background Studies on deep learning (DL)-based models in breast ultrasound (US) remain at the early stage due to a lack of large datasets for training and independent test sets for verification. We aimed to develop a DL model for differentiating benign from malignant breast lesions on US using a large multicenter dataset and explore the model’s ability to assist the radiologists. Methods A total of 14,043 US images from 5012 women were prospectively collected from 32 hospitals. To develop the DL model, the patients from 30 hospitals were randomly divided into a training cohort (n = 4149) and an internal test cohort (n = 466). The remaining 2 hospitals (n = 397) were used as the external test cohorts (ETC). We compared the model with the prospective Breast Imaging Reporting and Data System assessment and five radiologists. We also explored the model’s ability to assist the radiologists using two different methods. Results The model demonstrated excellent diagnostic performance with the ETC, with a high area under the receiver operating characteristic curve (AUC, 0.913), sensitivity (88.84%), specificity (83.77%), and accuracy (86.40%). In the comparison set, the AUC was similar to that of the expert (p = 0.5629) and one experienced radiologist (p = 0.2112) and significantly higher than that of three inexperienced radiologists (p < 0.01). After model assistance, the accuracies and specificities of the radiologists were substantially improved without loss in sensitivities. Conclusions The DL model yielded satisfactory predictions in distinguishing benign from malignant breast lesions. The model showed the potential value in improving the diagnosis of breast lesions by radiologists.

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“…Breast cancer has become the leading cause of global cancer incidence in 2020 (1), and it is the fifth cause of cancer deaths among Chinese women (2). A high degree of heterogeneity can be observed in breast cancer, and genomic instability is regarded as a major driver of tumor heterogeneity (3).…”

Section: Introductionmentioning

confidence: 99%

Using an Improved Residual Network to Identify PIK3CA Mutation Status in Breast Cancer on Ultrasound Image

Shen

Guo

et al. 2022

Front. Oncol.

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BackgroundThe detection of phosphatidylinositol-3 kinase catalytic alpha (PIK3CA) gene mutations in breast cancer is a key step to design personalizing an optimal treatment strategy. Traditional genetic testing methods are invasive and time-consuming. It is urgent to find a non-invasive method to estimate the PIK3CA mutation status. Ultrasound (US), one of the most common methods for breast cancer screening, has the advantages of being non-invasive, fast imaging, and inexpensive. In this study, we propose to develop a deep convolutional neural network (DCNN) to identify PIK3CA mutations in breast cancer based on US images.Materials and MethodsWe retrospectively collected 312 patients with pathologically confirmed breast cancer who underwent genetic testing. All US images (n=800) of breast cancer patients were collected and divided into the training set (n=600) and test set (n=200). A DCNN-Improved Residual Network (ImResNet) was designed to identify the PIK3CA mutations. We also compared the ImResNet model with the original ResNet50 model, classical machine learning models, and other deep learning models.ResultsThe proposed ImResNet model has the ability to identify PIK3CA mutations in breast cancer based on US images. Notably, our ImResNet model outperforms the original ResNet50, DenseNet201, Xception, MobileNetv2, and two machine learning models (SVM and KNN), with an average area under the curve (AUC) of 0.775. Moreover, the overall accuracy, average precision, recall rate, and F1-score of the ImResNet model achieved 74.50%, 74.17%, 73.35%, and 73.76%, respectively. All of these measures were significantly higher than other models.ConclusionThe ImResNet model gives an encouraging performance in predicting PIK3CA mutations based on breast US images, providing a new method for noninvasive gene prediction. In addition, this model could provide the basis for clinical adjustments and precision treatment.

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Breast cancer incidence and mortality in women in China: temporal trends and projections to 2030

Cited by 141 publications

References 35 publications

The global, regional, and national disease burden of breast cancer attributable to low physical activity from 1990 to 2019: an analysis of the Global Burden of Disease Study 2019

The global, regional, and national disease burden of breast cancer attributable to low physical activity from 1990 to 2019: an analysis of the Global Burden of Disease Study 2019

Deep learning based on ultrasound images assists breast lesion diagnosis in China: a multicenter diagnostic study

Using an Improved Residual Network to Identify PIK3CA Mutation Status in Breast Cancer on Ultrasound Image

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