A new near-term breast cancer risk prediction scheme based on the quantitative analysis of ipsilateral view mammograms

Sun, Wenqing; Tseng, Tzu-Liang; Qian, Wei; Saltzstein, Edward C.; Yu, Hui; Zhou, Shi

doi:10.1016/j.cmpb.2017.11.019

Cited by 8 publications

(3 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The study showed that increasing the number of filters in the convolutional layer can significantly improve microcalcification classification accuracy. Sun et al 26 developed and test a new near‐term breast cancer risk prediction scheme based on the quantitative analysis of the ipsilateral view of the negative screening mammograms. The results showed that the AUC of this model for breast cancer diagnosis is 0.737 ± 0.052, indicating that deep learning has great potential in developing a risk prediction model for early breast cancer.…”

Section: Discussionmentioning

confidence: 99%

Application of deep learning in the detection of breast lesions with four different breast densities

Li¹,

Ye²,

Líu³

et al. 2021

Cancer Medicine

View full text Add to dashboard Cite

show abstract

Section: Discussionmentioning

confidence: 99%

Application of deep learning in the detection of breast lesions with four different breast densities

Li¹,

Ye²,

Líu³

et al. 2021

Cancer Medicine

View full text Add to dashboard Cite

show abstract

“…Moreover, it has been shown that there is no apparent increase in the risk of developing a contralateral B-cancer according to the histology of primary cancer [31,32]. A quantitative analysis of homolateral views of mammograms would provide useful information regarding B-cancer risk over the short term [16][17][18]. In France, since post-diagnostic follow-up is usually carried out outside the BCSP, this study cannot predict on the evolution of the radiological abnormality in the contralateral breast after B-cancer diagnosis.…”

Section: Discussionmentioning

confidence: 99%

“…Statistical modelling was already predicted the occurrence of B-cancer or its prognosis from previous radiological ndings. A few studies converged towards a positive association between the risk score calculated on previous mammograms and imagedetected B-cancer at screening mammograms [15][16][17][18]. In addition to the reduced sample size, these studies did not describe the B-cancer risk according to the nding age of each radiological abnormality.…”

Section: Introductionmentioning

confidence: 99%

Breast Cancer Incidence by Age at Finding of Mammographic Abnormality in Women Participating in French Organized Screening Campaigns.

Koïvogui¹,

Balamou

Rymzhanova³

et al. 2021

Preprint

View full text Add to dashboard Cite

Purpose: Using reduced samples, statistical modelling was already predicted the occurrence of Breast-cancer or its prognosis from previous radiological findings. This study aims to predict breast-cancer risk by mammographic abnormalities finding age in the French breast-cancer screening campaign. Methods: The study involved 261,083 women aged 50-74 living in French Departments (Ain, Doubs, Haute-Saône, Jura, Territoire-de-Belfort, Yonne). These women had at least two screening mammograms between Jan-1999 and Dec-2017 of which the first was classified as “normal/benign”. The incidence of mammographic-abnormality (microcalcification, spiculated-mass, obscured-mass, architectural-distortion, asymmetric-density) and the incidence of breast-cancer after abnormality detection were estimated abnormalities finding age, using an actuarial life-table method. Breast-cancer risk was predicted in a Cox multivariate model.Results: The incidence of mammographic-abnormality was 95.4[94.9; 95.9]/1000 person-years. Breast-cancer (6,326 cases) incidence was 3.3[3.0; 3.1]/1000 person-years. That incidence was 5 times higher in women who showed a speculated-mass vs. those who did not (6.9[6.4; 7.4] vs. 1.3[1.2; 1.3]). Whatever the abnormality, the incidence of cancer was higher when it was present in only one breast. Depending on the spiculated-mass finding age, the risk increased by at least 40% between the age groups 55-59years (1.4[1.0; 1.8]) and ≥70years (2.4[1.9; 3.3]).Conclusion: The study showed the increased risk of cancer with the abnormalities finding age and the low risk related to the presence of the same mammographic-abnormality in both breasts compared to the isolated mammographic-abnormality in one of the breasts. This should alert radiologists to the relevance of certain diagnostic procedures in the management of a bilateral mammographic abnormality.

show abstract

A novel approach for breast cancer prediction using optimized ANN classifier based on big data environment

Supriya

Deepa²

2019

Health Care Manag Sci

View full text Add to dashboard Cite

A new near-term breast cancer risk prediction scheme based on the quantitative analysis of ipsilateral view mammograms

Cited by 8 publications

References 38 publications

Application of deep learning in the detection of breast lesions with four different breast densities

Application of deep learning in the detection of breast lesions with four different breast densities

Breast Cancer Incidence by Age at Finding of Mammographic Abnormality in Women Participating in French Organized Screening Campaigns.

A novel approach for breast cancer prediction using optimized ANN classifier based on big data environment

Contact Info

Product

Resources

About