Analysis of breast cancer cases according to county-level poverty status in 3.5 million rural women who participated in a breast cancer screening program of Hunan province, China from 2016 to 2018

Analysis of Breast Cancer Screening Results and Influencing Factors of Breast Cancer in Guangdong Province from 2017 to 2021

BACKGROUNDS

Breast cancer screening plays an important role in the early detection, diagnosis and treatment of breast cancer. The aim of this study was to evaluate the screening results and explore the influencing factors of breast cancer detection rate in Guangdong.

METHODS

This cross-sectional study was conducted among 2,024,960 women aged 35-64 in Guangdong Province during 2017-2021. The data about breast cancer screening information were collected from the Guangdong maternal and child health information system. Descriptive statistical analysis was used to explain demographic characteristics and results of breast cancer screening. The generalized linear regression model was applied to analyze the related influencing factors of breast cancer detection rate.

RESULTS

The estimated detection rate of breast cancer in Guangdong Province is 70.32/105, with an early diagnosis rate of 82.06%. After adjusting covariates, those women with older age (45-55 [OR (95% CI) 2.174 (1.872, 2.526)], 55-65 [OR (95% CI) 2.162 (1.760, 2.657)]), education for high school ([OR (95% CI) 1.491 (1.254, 1.773)]) and older age at first birth ([OR (95% CI) 1.632 (1.445, 1.844)]) were more likely to have higher detection rate of breast cancer. No history of surgery or biopsy ([OR (95% CI) 0.527 (0.387, 0.718)]), no history of breast cancer check ([OR (95% CI) 0.873 (0.774, 0.985)]) and no family history of breast cancer ([OR (95% CI) 0.255 (0.151, 0.432)]) women were more likely to screen negative for breast cancer (P < 0.05).

CONCLUSION

The detection rate of breast cancer in screening showed an increasing trend year by year in Guangdong Province. Older age, education for high school and older age at first birth were risk factors for breast cancer detection rate, while no surgery or biopsy history, no family history of breast cancer and no history of breast cancer check were protective factors.

Breast cancer screening and early diagnosis in China: a systematic review and meta-analysis on 10.72 million women

BACKGROUND

The incidence of breast cancer among Chinese women has gradually increased in recent years. This study aims to analyze the situation of breast cancer screening programs in China and compare the cancer detection rates (CDRs), early-stage cancer detection rates (ECDRs), and the proportions of early-stage cancer among different programs.

METHODS

We conducted a systematic review and meta-analysis of studies in multiple literature databases. Studies that were published between January 1, 2010 and June 30, 2023 were retrieved. A random effects model was employed to pool the single group rate, and subgroup analyses were carried out based on screening model, time, process, age, population, and follow-up method.

RESULTS

A total of 35 studies, including 47 databases, satisfied the inclusion criteria. Compared with opportunistic screening, the CDR (1.32‰, 95% CI: 1.10‰-1.56‰) and the ECDR (0.82‰, 95% CI: 0.66‰-0.99‰) were lower for population screening, but the proportion of early-stage breast cancer (80.17%, 95% CI: 71.40%-87.83%) was higher. In subgroup analysis, the CDR of population screening was higher in the urban group (2.28‰, 95% CI: 1.70‰-2.94‰), in the breast ultrasonography (BUS) in parallel with mammography (MAM) group (3.29‰, 95% CI: 2.48‰-4.21‰), and in the second screening follow-up group (2.47‰, 95% CI: 1.64‰-3.47‰), and the proportion of early-stage breast cancer was 85.70% (95% CI: 68.73%-97.29%), 88.18% (95% CI: 84.53%-91.46%), and 90.05% (95% CI: 84.07%-94.95%), respectively.

CONCLUSION

There were significant differences between opportunistic and population screening programs. The results of these population screening studies were influenced by the screening process, age, population, and follow-up method. In the future, China should carry out more high-quality and systematic population-based screening programs to improve screening coverage and service.

Deep weakly-supervised breast tumor segmentation in ultrasound images with explicit anatomical constraints

Breast tumor segmentation is an important step in the diagnostic procedure of physicians and computer-aided diagnosis systems. We propose a two-step deep learning framework for breast tumor segmentation in breast ultrasound (BUS) images which requires only a few manual labels. The first step is breast anatomy decomposition handled by a semi-supervised semantic segmentation technique. The input BUS image is decomposed into four breast anatomical structures, namely fat, mammary gland, muscle and thorax layers. Fat and mammary gland layers are used as constrained region to reduce the search space for breast tumor segmentation. The second step is breast tumor segmentation performed in a weakly-supervised learning scenario where only image-level labels are available. Breast tumors are first recognized by a classification network and then segmented by the proposed class activation mapping and deep level set (CAM-DLS) method. For breast anatomy decomposition, the proposed framework achieves Dice similarity coefficient (DSC) of 83.0 ± 11.8%, 84.3 ± 10.0%, 80.7 ± 15.4% and 91.0 ± 11.4% for fat, mammary gland, muscle and thorax layers, respectively. For breast tumor recognition, the proposed framework achieves sensitivity of 95.8%, precision of 92.4%, specificity of 93.9%, accuracy of 94.8% and F1-score of 0.941. For breast tumor segmentation, the proposed framework achieves DSC of 77.3% and intersection-over-union (IoU) of 66.0%. In conclusion, the proposed framework could efficiently perform breast tumor recognition and segmentation simultaneously in a weakly-supervised setting with anatomical constraints.