ArticleAnnals of surgical oncology2020
The Tyrer-Cuzick Model Inaccurately Predicts Invasive Breast Cancer Risk in Women With LCIS.
Article in Annals of surgical oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.
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Who cites it
13 citing papers in PubMed, 2 syntheses or guidelines pooled it, 42 citations in OpenAlex.
- Risk assessment tools for women with breast atypia: A systematic review.American journal of surgery · 2025Pooled it
- Society of Surgical Oncology Breast Disease Site Working Group Statement on Bilateral Risk-Reducing Mastectomy: Indications, Outcomes, and Risks.Annals of surgical oncology · 2025Pooled it
- Article
- Current insights into breast and ovarian cancer risk: a contemporary review.Minerva surgery · 2025Review
- Defining Lifetime Risk Thresholds for Breast Cancer Surgical Prevention.JAMA oncology · 2025Article
- Long term follow-up of women treated for screen detected atypical ductal hyperplasia or lobular neoplasia in a large UK screening centre.BJC reports · 2024Article
- The Impact of a Breast Cancer Risk Assessment on the Decision for Gender-Affirming Chest Masculinization Surgery in Transgender and Gender-Diverse Individuals: A Pilot Single-Arm Educational Intervention Trial.Annals of surgical oncology · 2024Observational
- Predicting breast cancer risk in a racially diverse, community-based sample of potentially high-risk women.Cancer medicine · 2022Article
- Deep Learning vs Traditional Breast Cancer Risk Models to Support Risk-Based Mammography Screening.Journal of the National Cancer Institute · 2022Article
- Identifying women with increased risk of breast cancer and implementing risk-reducing strategies and supplemental imaging.Breast cancer (Tokyo, Japan) · 2022Review
- Artificial intelligence in breast cancer imaging: risk stratification, lesion detection and classification, treatment planning and prognosis-a narrative review.Exploration of targeted anti-tumor therapy · 2022Review
- Generalizability of Polygenic Risk Scores for Breast Cancer Among Women With European, African, and Latinx Ancestry.JAMA network open · 2021Article
- Artificial Intellgence in the Era of Precision Oncological Imaging.Technology in cancer research & treatmentReview
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundThe Tyrer-Cuzick model has been shown to overestimate risk in women with atypical hyperplasia, although its accuracy among women with lobular carcinoma in situ (LCIS) is unknown. We evaluated the accuracy of the Tyrer-Cuzick model for predicting invasive breast cancer (IBC) development among women with LCIS.
methodsWomen with LCIS participating in surveillance from 1987 to 2017 were identified from a prospectively maintained database. Tyrer-Cuzick score (version 7) was calculated near the time of LCIS diagnosis. Patients with prior or concurrent breast cancer, a BRCA mutation, receiving chemoprevention, or with pleomorphic LCIS were excluded. Invasive cancer-free probability was estimated using the Kaplan-Meier method.
resultsA total of 1192 women with a median follow-up of 6 years (interquartile range [IQR] 2.5-9.9) were included. Median age at LCIS diagnosis was 49 years (IQR 45-55), 88% were white; 37% were postmenopausal, 28% had ≥ 1 first-degree family member with breast cancer, and 13% had ≥ 2 second-degree family members with breast cancer. In total, 128 patients developed an IBC; median age at diagnosis was 54 years (IQR 49-61). Five- and 10-year cumulative incidences of invasive cancer were 8% (95% confidence interval [CI] 6-9%) and 14% (95% CI 12-17%), respectively. The median Tyrer-Cuzick 10-year risk score was 20.1 (IQR 17.4-24.3). Discrimination measured by the C-index was 0.493, confirming that the Tyrer-Cuzick model is not well calibrated in this patient population.
conclusionsThe Tyrer-Cuzick model is not accurate and may overpredict IBC risk for women with LCIS, and therefore should not be used for breast cancer risk assessment in this high-risk population.
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