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<p>Most evidence and experience with mammography screening originate from Western countries, and the optimal prevention strategy for Taiwanese women remains uncertain. Currently, breast cancer susceptibility in Taiwan is primarily stratified by family history, which contrasts with the trend toward personalized screening. Additionally, the high false-positive and false-negative rates (and the resulting compromised positive predictive value) have hindered the widespread adoption of mammography among the general population. Consequently, there is an unmet need to identify breast cancer risk factors to develop a more efficient and tailored screening strategy that minimizes potential harm. This study aimed to identify breast cancer risk factors by analyzing big data, including National Health Insurance claims data, a screening database, a cancer registry from the Health Promotion Administration, and a death registry from census data. Between 2007 and 2017, 189,465 entries were extracted from the cancer registry, representing 133,546 breast cancer cases. The screening database contained 3,806,128 mammography episodes from 2004 to 2014. We identified subjects who had attended at least one screening session between January 2007 and September 2014, matching cancer cases to the registry. Screening intervals were extended by two years to August 2016. After excluding patients with pre-existing breast malignancies, 3,605,758 screening mammography episodes from 2,191,742 invitees were analyzed, resulting in the identification of 38,815 incident breast cancer cases. Multivariate analyses revealed that risk factors for breast cancer diagnosis included a family history of any cancer (odds ratio [OR]: 1.462), number of sisters with breast cancer (OR: 1.058), years of hormone replacement therapy (OR: 1.006), breast symptoms (OR: 3.843), breast examinations within two years (OR: 1.226), prior breast surgery (OR: 1.044), educational level (OR: 1.04), and breast density (OR: 1.096). Protective factors included menopausal status (OR: 0.935), breastfeeding (OR: 0.908), sonography within two years (OR: 0.899), comparison with prior mammography (OR: 0.775), number of mammography screenings (OR: 0.673), and screening via a mobile van (OR: 0.587). The model demonstrated an area under the receiver operating characteristic curve (AUC) of 0.6766. Among 50,831 breast cancer cases, 47.6% had undergone at least one mammography screening before diagnosis, which was associated with earlier disease stages. Clinically detected breast cancer was an independent risk factor for recurrence-free and overall survival, as well as breast cancer mortality. Big data analysis identified several risk factors for breast cancer development in Taiwanese women and confirmed the efficacy of mammography screening.</p>

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Identifying breast cancer risk factors and evaluating biennial mammography screening efficacy using big data analysis in Taiwan

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  • 出版模式：

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  • 发表日期：

    2025年5月9日

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  • Chi-Cheng Huang,
  • Tzu-Pin Lu,
  • Yu-Jen Wang,
  • Bo-Fang Chen,
  • Hui-Ting Yang,
  • Wei-Pin Chang,
  • Ling-Ming Tseng

摘要

Most evidence and experience with mammography screening originate from Western countries, and the optimal prevention strategy for Taiwanese women remains uncertain. Currently, breast cancer susceptibility in Taiwan is primarily stratified by family history, which contrasts with the trend toward personalized screening. Additionally, the high false-positive and false-negative rates (and the resulting compromised positive predictive value) have hindered the widespread adoption of mammography among the general population. Consequently, there is an unmet need to identify breast cancer risk factors to develop a more efficient and tailored screening strategy that minimizes potential harm. This study aimed to identify breast cancer risk factors by analyzing big data, including National Health Insurance claims data, a screening database, a cancer registry from the Health Promotion Administration, and a death registry from census data. Between 2007 and 2017, 189,465 entries were extracted from the cancer registry, representing 133,546 breast cancer cases. The screening database contained 3,806,128 mammography episodes from 2004 to 2014. We identified subjects who had attended at least one screening session between January 2007 and September 2014, matching cancer cases to the registry. Screening intervals were extended by two years to August 2016. After excluding patients with pre-existing breast malignancies, 3,605,758 screening mammography episodes from 2,191,742 invitees were analyzed, resulting in the identification of 38,815 incident breast cancer cases. Multivariate analyses revealed that risk factors for breast cancer diagnosis included a family history of any cancer (odds ratio [OR]: 1.462), number of sisters with breast cancer (OR: 1.058), years of hormone replacement therapy (OR: 1.006), breast symptoms (OR: 3.843), breast examinations within two years (OR: 1.226), prior breast surgery (OR: 1.044), educational level (OR: 1.04), and breast density (OR: 1.096). Protective factors included menopausal status (OR: 0.935), breastfeeding (OR: 0.908), sonography within two years (OR: 0.899), comparison with prior mammography (OR: 0.775), number of mammography screenings (OR: 0.673), and screening via a mobile van (OR: 0.587). The model demonstrated an area under the receiver operating characteristic curve (AUC) of 0.6766. Among 50,831 breast cancer cases, 47.6% had undergone at least one mammography screening before diagnosis, which was associated with earlier disease stages. Clinically detected breast cancer was an independent risk factor for recurrence-free and overall survival, as well as breast cancer mortality. Big data analysis identified several risk factors for breast cancer development in Taiwanese women and confirmed the efficacy of mammography screening.