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 2016–2018

Abstract

<jats:title>Abstract</jats:title> <jats:p><jats:bold>Purpose</jats:bold> Thus far there has been no study on the breast cancer screening program in China, especially one exploring differences between poor and non-poor areas. Therefore, we aimed to assessment the effectiveness of the population-based breast cancer screening programs, and clinical epidemiological characteristics of breast cancers in poor and non-poor counties in rural areas of Hunan province from 2016–2018.<jats:bold>Methods</jats:bold> 3,151,679 rural women took part in the screening program and 1,169 breast cancer cases was identified. Chi-square and Fisher’s exact tests, and binary logistic regression analysis were used for analyzing the difference in risk factors, clinical examination results, and clinicopathological features of breast cancer patients in poor counties compared with those in non-poor counties in rural areas of Hunan province. <jats:bold>Results</jats:bold> The breast cancer incidence was 37.09/10<jats:sup>5</jats:sup>. Breast cancer incidence was lower in poor counties (29.68/10<jats:sup>5</jats:sup>) than in non-poor counties (43.13/10<jats:sup>5</jats:sup>). There were differences between breast cancers in poor counties and non-poor counties in cyst, margin, internal echo, and blood flow in solid masses in the right breast in ultrasonic examination, lump structure in mammography examination, and clinicopathological staging and grading in pathological examination. Breast cancer in poor counties was more likely to be diagnosed at later stages as determined from the classification in ultrasonic, mammography and pathological examination. Furthermore, indexes of the breast screening program including early detection, incidence, pathological examination, and mammography examination were reduced in poor compared with non-poor counties. Binary logistic regression analysis showed that education, ethnicity, reproductive history and the year 2017 were associated with an increased risk of breast cancer in poor counties (OR &gt;1, P &lt; 0.05). <jats:bold>Conclusions</jats:bold> Women in poor areas were more likely to be diagnosed at a later breast cancer stage than those in non-poor areas, and therefore, women should have better access to diagnostic and clinical services in poor areas in Hunan province to help rectify this situation.</jats:p>

Go beyond image-based benign-malignant classification: AI can identify responsible frames better than physicians in breast ultrasound screening videos

“…The access to fast, reliable ultrasound test in underdeveloped areas is in stark contrast with its counterpart, primarily resulting from the better education, training and opportunities for physicians in developed regions that underdeveloped areas are simply not able to match. 15,16 The seeking of means to reduce the burden of sonographers and empower junior physicians has been long. Automated Breast Ultrasound(ABUS) is a technology that automates image acquisition, hoping to boost repeatability and decrease operator reliance.…”