Jun 2026· Ceska gynekologie· Vol 91 3, pp.
254-260
· 0 citations
Medicine
TL;DR
Current statistical data for the GAIL model, IBIS and its variants, and the BOADICEA model are presented and their potential applications in routine gynecological outpatient practice are highlighted.
Abstract
The rising incidence of breast cancer represents a serious medical problem. Women diagnosed with breast cancer before the age of 45 - that is, those who were not included in mammography screening programs - constitute a specific patient group. Identification of risk factors for the pathogenesis of breast cancer has led to the development of triage models based on both medical history data and genetic testing, as well as imaging methods; their updated versions are based on a combination of these modalities. The oldest empirical Gail model relies solely on basic medical history. The IBIS model, on the other hand, incorporates an expanded family history, as well as mammographic breast tissue density. The BOADICEA model enables the calculation of lifetime risk and risk of being a carrier of BRCA1/2 and other major mutations (BRCA1/2, TP53, PTEN, CHEK2, ATM). The Pecný model represents a Czech contribution. With new possibilities in genetic testing, the latest versions of these models now include the polygenic risk score (PRS), which increases their predictive value. With the growing integration of artificial intelligence (AI) and deep learning into clinical medicine, we can expect the emergence of new, AI-dependent triage models. A major limitation of the aforementioned models is their restriction to the Caucasian population. Calibration based on statistical data for other populations often does not work. This article will present current statistical data for the GAIL model, IBIS and its variants, and the BOADICEA model. The aim of this article is to present predictive models to the professional community and to highlight their potential applications in routine gynecological outpatient practice.
This study identified several personal characteristics that were more strongly linked to breast cancer occurrence, highlighting the value of integrating clinical and self-reported information to better understand individual risk and support more targeted prevention and screening efforts.
M. Franchini, F. Denoth, Stefania Pieroni et al.· Cancers· 0 citations
Introduction: Breast cancer is the most commonly diagnosed cancer among women globally, with an estimated 2.26 million new cases in 2020. The risk factors of breast cancer include age, genetic mutation and family history, racial and ethnic disparities, Increase in breast tissue density, etc. Early detection through organized screening programs plays a crucial role in improving survival by identifying cancer at stages when treatment is more likely to be curative. However, screening and diagnostic results can sometimes be misinterpreted, leading to false positives, false negatives, unnecessary follow-up tests, or over-diagnosis. Although digital mammography remains the standard, additional imaging methods are now used for high-risk individuals and those with dense breast tissue. Aims and Objectives: To evaluate the diagnostic accuracy, and the clinical, psychological, and diagnostic outcomes following recall after routine breast screening. The studies include a Cohort studies, Observational and Case-Control studies, Diagnostic accuracy and Screening Programs evaluations. Participants includes women aged 40 and older undergoing routing breast screening and were called back for additional assessment. Studies were conducted in several countries including Norway, Spain, etc. Ethical approval was not required for the studies as the participants clinical information was anonymized across all selected studies. Methodology: Data were collected using a computerized search strategy as the principle source of information from European Journal of Public Health, Journal of Breast Imaging, PubMed, etc. Results: Findings from the Randomized and Population-based Cohort study suggested that the screen-detected breast cancers indicated over-diagnosis rate of 14-39% with limited quality evidence. Data from the Observational studies estimated the 10-years cumulative probability of false-positive results with Film and Digital Mammography of 49% with limited quality evidence. Findings from the Cohorts studies showed that those women called back for further evaluation reported higher levels of anxiety, distress and pains with a moderate quality evidence. The harm of radiation exposure from mammography screening is based on only three modeling studies with moderate quality evidence.
Ugochukwu Maluze· British Journal of Healthcar...· 0 citations
Objectives: Women with a history of breast cancer are at increased risk of developing subsequent breast cancer, including ipsilateral recurrence and contralateral new primary breast cancer. This study evaluated the discriminatory performance of a mammogram-based artificial intelligence (AI) risk model for predicting subsequent breast cancer within one year after a negative screening mammogram. Methods: This enriched retrospective case–control study included women with a prior history of breast cancer who underwent screening digital breast tomosynthesis between January 2018 and December 2023 at three affiliated academic breast imaging centers. Digital breast tomosynthesis examinations classified as BI-RADS 1 or 2 were retrospectively analyzed using the ProFound AI® Risk model version 1.0 to estimate 1-year breast cancer risk. Patients were classified according to whether they developed subsequent breast cancer within one year of the index screening examination. Model discrimination was evaluated using receiver operating characteristic analysis. Sensitivity, specificity, positive predictive value, and negative predictive value were calculated at an exploratory cutoff selected by maximizing the Youden index. Results: The study included 96 women (mean age, 65.3 ± 8.7 years), of whom 32 developed subsequent breast cancer within one year, and 64 did not. The mean AI risk score was significantly higher in the subsequent breast cancer group than in the control group (1.18 ± 0.59 vs. 0.49 ± 0.41; p < 0.001). The AI model demonstrated an AUC of 0.824 (95% CI: 0.728–0.921). At an exploratory cutoff of 0.39, sensitivity was 81.3%, specificity was 76.6%, PPV was 63.4%, and NPV was 89.1%. In separate exploratory analyses, the AUC was 0.790 (95% CI: 0.641–0.939) for ipsilateral recurrence and 0.860 (95% CI: 0.752–0.974) for contralateral new primary breast cancer. AI risk scores were not significantly correlated with tumor size or age at subsequent breast cancer diagnosis. Conclusions: In this enriched retrospective case–control study, higher mammogram-based AI risk scores were associated with subsequent breast cancer within one year after a negative screening examination. The model demonstrated discriminatory performance for both ipsilateral recurrence and contralateral new primary breast cancer; however, these analyses were exploratory. Because the cohort was enriched for subsequent breast cancer events, the reported predictive values are specific to the study sample and should not be extrapolated to routine surveillance populations. Larger prospective cohorts are needed to validate discrimination, calibration, and clinical utility.
S. Ogunlade, A. Dakkak, Amie Leon et al.· Journal of Clinical Medicine· 0 citations
ABSTRACT: Background: Breast cancer is the most frequently diagnosed malignancy in women worldwide, with over 2.3 million new cases annually. Despite advances in treatment, early detection remains the primary determinant of survival. Objective: To systematically evaluate the diagnostic performance of Digital Mammography (DM), Digital Breast Tomosynthesis (DBT), Ultrasound, MRI, Contrast-Enhanced Mammography (CEM), and AI-assisted imaging for breast cancer detection in women aged 30–70 years. Methods: A PRISMA-compliant systematic review of peer-reviewed literature (2010–2024) was conducted. Studies sourced from PubMed/MEDLINE, Cochrane Library, Embase, and Scopus. QUADAS-2 was applied for quality assessment. Outcome measures: cancer detection rate (CDR), sensitivity, specificity, stage at diagnosis, and recall rate. Results: Of 142 included studies (>4.2 million examinations), DBT demonstrated superior CDR (4.0–6.5/1000; 95% CI: 3.7–6.9) over DM (3.4–5.0/1000; 95% CI: 3.1–5.4) with lower recall rates. MRI achieved the highest sensitivity (90–99%) but lowest specificity (72–89%). CEM and AI-assisted imaging showed clinically promising performance, though evidence remains emerging. Conclusion: No single modality is universally optimal. A risk-stratified, personalised screening approach is recommended, with DBT as preferred standard for average-risk populations and MRI for high-risk individuals. Future prospective trials should address equity, AI validation, and access.
Tania Amko, Tongbram Bidyananda Singh, Keleriano et al.· International journal of med...· 0 citations
IMPORTANCE
Patients diagnosed with breast cancer (BCa) are at increased risk of multiple common diseases; however, the spectrum of these diseases and the contribution of inherited genetic susceptibility remain incompletely characterized.
METHODS
We evaluated 15 common diseases and tested their associations with BCa exposure and disease-specific polygenic risk scores (PRS) in the UK Biobank (UKB; N = 254,736). Analyses were performed using cause-specific Cox proportional hazards models within a full-cohort framework, with time-updated BCa status, delayed entry at study recruitment, and age as the underlying time scale.
RESULTS
After recruitment, incident BCa was diagnosed in 11,386 women (4.47%), including 2,742 (24.08%) with metastatic BCa. Patients with BCa had an increased risk of nine diseases spanning cardiovascular, metabolic, and neuropsychiatric domains (P<0.003, Bonferroni-corrected). Elevated risks were generally observed among patients with both early staged and advanced BCa. Inherited susceptibility further stratified disease risk, with the highest risks observed among patients with BCa with elevated disease-specific PRS. For example, compared with women without BCa, the hazard ratio (HR; 95% CI) for osteoporosis was 2.33 (2.15-2.52) among women with any BCa, 2.38 (2.18-2.59) among those with non-metastatic BCa, and 2.12 (1.78-2.54) among those with metastatic BCa; the HR was 4.48 (3.99-5.02) among patients with BCa in the highest quartile of osteoporosis-specific PRS (all P<0.001). In contrast, BCa was not significantly associated with risk of coronary artery disease.
CONCLUSION
BCa and inherited genetic susceptibility jointly contribute to increased risk of multiple common diseases, supporting the integration of genetic risk stratification into survivorship care.
Annabelle Ashworth, Zhu-Qing Shi, Huy Tran et al.· JNCI Cancer Spectrum· 0 citations
Higher breast density was associated with later age at menarche, nulliparity, breastfeeding, history of benign breast disease, alcohol consumption, lower body mass index (BMI) at recruitment and at age 20, and current use of menopausal hormone therapy (MHT).
L. Johns, Martina Brayley, R. Frost et al.· Breast Cancer Research· 0 citations
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