Genetic and lifestyle factors for breast cancer risk assessment in Southeast China

Zou, Shuqing; Lin, Yuxiang; Yu, Xingxing; Eriksson, Mikael; Lin, Moufeng; Fu, Freddie H.; Yang, Haomin

doi:10.1002/cam4.6198

Cited by 4 publications

(2 citation statements)

References 53 publications

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“…Every tenth woman in Delhi suffers from obesity as revealed by the NFHS-4 survey. The rise in obesity proportion could be one of the contributing factors to surge in the endometrial cancer incidence [ 23 ]. Among 28 PBCRs Delhi holds fourth position in endometrial cancer and all other top three PBCRs, exceeding 33% overweight or obese women according to NFHS-4 (2015-2016) [ 23 ].…”

Section: Discussionmentioning

confidence: 99%

“…The other components of cancer-specific risk are well-known cancer-specific risk factors. For instance, the risk of breast cancer could be increased due to factors such as poor lifestyle, higher age at marriage, low parity, and delay in the age of first childbirth [20,21]. A sedentary lifestyle and rise in obesity may contribute to the increased incidence of corpus uteri (endometrial) cancer.…”

Section: Discussionmentioning

confidence: 99%

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Demographical and Epidemiological Contribution to Cancer Incidence in Delhi and Its Trends from 1991-2015

Malhotra,

Manoharan,

Deo

et al. 2024

Asian Pac J Cancer Prev

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Introduction: Cancer incidences are rising worldwide, and India ranked third globally in cancer incidence as of 2020, according to estimates from GLOBOCAN. The three components that contributed to changes in cancer incidence include cancer-related risk factors, population size, and population structure. The present study aim is to derive the contribution of these factors to cancer incidence and to evaluate their trend from 1991 to 2015. Methods: The Data were extracted from the Delhi population-based cancer registry published reports. This longstanding registry covers nearly 100% of the Delhi population. The secular trends of cancer incidence from 1991-2015 were assessed for all sites combined as well as top-five cancer sites among males and females. Joinpoint regression and Riskdiff software were performed to assess the trend among the components of cancer incidence change. Results: Both males and females exhibited nearly equal age-standardised incidence rates over 25 years. Albeit, an overall trend in age-standardised rate was not significant for both sexes (0.68% for males and -0.16% for females) when considering all cancer sites combined. Lung, prostate, oral, and gallbladder cancer exhibits a significant rising trend in the age-standardised rates in males while in females only breast and endometrial cancer showed a rising trend. The cancer counts surged by 252% in males and 208.5% in females from 1991 to 2015. The population size component contributed a 180% increase in males and a 170% increase in females, respectively. The site-specific risk changes were more than 100% for the prostate, oral, and gallbladder cancers in males and endometrial cancer in females. The population structure (aging) contributed to rising cancer incidence varying from 35% to 60% in both genders. Conclusion: A significant contribution to new cancer cases was observed due to a demographical shift in both population size and structure, in addition to plausible cancer-specific risk factors. This transformation could surge a potential burden on the Delhi healthcare system. Persistent endeavours are essential to expand and enhance the existing cancer care infrastructure to meet the rising demand driven by aging and population growth. Implementing a stringent population policy can help to mitigate the impact of population growth on cancer incidence.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Demographical and Epidemiological Contribution to Cancer Incidence in Delhi and Its Trends from 1991-2015

Malhotra,

Manoharan,

Deo

et al. 2024

Asian Pac J Cancer Prev

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Genetic and lifestyle factors for breast cancer risk assessment in Southeast China

Zou

Lin

et al. 2023

Cancer Medicine

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BackgroundDespite the rising incidence and mortality of breast cancer among women in China, there are currently few predictive models for breast cancer in the Chinese population and with low accuracy. This study aimed to identify major genetic and life‐style risk factors in a Chinese population for potential application in risk assessment models.MethodsA case–control study in southeast China was conducted including 1321 breast cancer patients and 2045 controls during 2013–2016, in which the data were randomly divided into a training set and a test set on a 7:3 scale. The association between genetic and life‐style factors and breast cancer was examined using logistic regression models. Using AUC curves, we also compared the performance of the logistic model to machine learning models, namely LASSO regression model and support vector machine (SVM), and the scores calculated from CKB, Gail and Tyrer–Cuzick models in the test set.ResultsAmong all factors considered, the best model was achieved when polygenetic risk score, lifestyle, and reproductive factors were considered jointly in the logistic regression model (AUC = 0.73; 95% CI: 0.70–0.77). The models created in this study performed better than those using scores calculated from the CKB, Gail, and Tyrer–Cuzick models. However, the logistic model and machine learning models did not significantly differ from one another.ConclusionIn summary, we have found genetic and lifestyle risk predictors for breast cancer with moderate discrimination, which might provide reference for breast cancer screening in southeast China. Further population‐based studies are needed to validate the model for future applications in personalized breast cancer screening programs.

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Machine learning techniques for breast cancer diagnosis

Dyomin,

Germashev

2024

Digital Diagnostics

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In recent years, machine learning methods have been attracting increasing attention in the field of diagnostics, especially in the detection of breast cancer. The aim of this review is to analyze current research on the use of machine learning methods in breast cancer diagnosis in three directions: their use in solving auxiliary tasks that arise in modern breast cancer diagnostics, their role in providing an intelligent assessment of the patient's condition for preliminary diagnostic decisions, and their ability to determine breast cancer risk factors. The analysis has shown that the use of machine learning methods in breast cancer diagnosis provides great opportunities for improving accuracy and efficiency, as well as for solving additional tasks. As a result of the literature analysis, features that are used as input data in machine learning methods have been obtained. In the future, the collected information will be used to construct a feature system for breast cancer diagnosis using machine learning methods.

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Genetic and lifestyle factors for breast cancer risk assessment in Southeast China

Cited by 4 publications

References 53 publications

Demographical and Epidemiological Contribution to Cancer Incidence in Delhi and Its Trends from 1991-2015

Demographical and Epidemiological Contribution to Cancer Incidence in Delhi and Its Trends from 1991-2015

Genetic and lifestyle factors for breast cancer risk assessment in Southeast China

Machine learning techniques for breast cancer diagnosis

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