The evaluation of mammographic breast density, a critical indicator of breast cancer risk, is traditionally performed by radiologists via visual inspection of mammography images, utilizing the Breast Imaging-Reporting and Data System (BI-RADS) breast density categories. However, this method is subject to substantial interobserver variability, leading to inconsistencies and potential inaccuracies in density assessment and subsequent risk estimations. To address this, we present a deep learning-based automatic detection algorithm (DLAD) designed for the automated evaluation of breast density. Our multicentric, multi-reader study leverages a diverse dataset of 122 full-field digital mammography studies (488 images in CC and MLO projections) sourced from three institutions. We invited two experienced radiologists to conduct a retrospective analysis, establishing a ground truth for 72 mammography studies (BI-RADS class A: 18, BI-RADS class B: 43, BI-RADS class C: 7, BI-RADS class D: 4). The efficacy of the DLAD was then compared to the performance of five independent radiologists with varying levels of experience. The DLAD showed robust performance, achieving an accuracy of 0.819 (95% CI: 0.736-0.903), along with an F1 score of 0.798 (0.594-0.905), precision of 0.806 (0.596-0.896), recall of 0.830 (0.650-0.946), and a Cohen's Kappa (κ) of 0.708 (0.562-0.841). The algorithm achieved robust performance that matches and in four cases exceeds that of individual radiologists. The statistical analysis did not reveal a significant difference in accuracy between DLAD and the radiologists, underscoring the model's competitive diagnostic alignment with professional radiologist assessments. These results demonstrate that the deep learning-based automatic detection algorithm can enhance the accuracy and consistency of breast density assessments, offering a reliable tool for improving breast cancer screening outcomes.

• Klíčová slova
• BI-RADS, breast density, computer-aided diagnosis, deep learning, full-field digital mammography, medical image processing,
• Publikační typ
• časopisecké články MeSH

Breast cancer is diagnosed through a patient's Breast Self-Examination (BSE), Clinical Breast Examination (CBE), or para-clinical methods. False negativity of PCM in breast cancer diagnostics leads to a persisting problem associated with breast tumors diagnosed only in advanced stages. As the tumor volume/size at which it becomes invasive is not clear, BSE and CBE play an exceedingly important role in the early diagnosis of breast cancer. The quality and effectiveness of BSE and CBE depend on several factors, among which breast stiffness is the most important one. In this study, the authors present four methods for evaluating breast stiffness pathology during mammography examination based on the outputs obtained during the breast compression process, id est, without exposing the patient to X-Ray radiation. Based on the subjective assessment of breast stiffness by experienced medical examiners, a novel breast stiffness classification was designed, and the best method of its objective measurement was calibrated to fit the scale. Hence, this study provides an objective tool for the identification of patients who, being unable to perform valid BSE, could benefit from an increased frequency of mammography screening. Dum vivimus servimus.

• Klíčová slova
• Breast, Breast pathology, Mammography, Novel, Scale, Stiffness,
• MeSH
• časná detekce nádoru * MeSH
• lidé MeSH
• mamografie MeSH
• nádory prsu * diagnóza   MeSH
• plošný screening metody   MeSH
• samovyšetření prsu MeSH
• Check Tag
• lidé MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

The aim of the present study was to evaluate breast cancer risk in women aged 40-45 years not included in the routine mammographic screening programme in the Czech Republic and to assess the suitability of the screening interval. Our cohort study was conducted using registry data of one mammography centre (Bulovka Hospital in Prague) between 1 January 2008 and 31 December 2017. The risk of breast cancer was evaluated using a positive predictive finding (PPF) corresponding to the Breast Imaging-Reporting and Data System (BI-RADS) scores of 4 and 5. The annual PPF incidence rate achieved 2.25 per 1000 women aged 40-45 years and was not significantly different from that (3.31) of women of 45-50 years of age as demonstrated by an adjusted hazard ratio of 0.75 (95% confidence interval: 0.42-1.33). It was found that a screening interval longer than 3 years increased the chance of PPF occurrence 1.7 times independently of the women's age, signalling a risk of failure of early detection of breast cancer. The same PPF incidence rates both in women aged 40-45 years and in older ones indicates that even younger women should be eligible for enrolment in the routine mammographic screening programme in the Czech Republic.

• Klíčová slova
• breast cancer risk, diagnostic imaging, mammography, mass screening, women’s groups,
• Publikační typ
• časopisecké články MeSH

PURPOSE: Breast ultrasonography (US) presents an alternative to mammography in young asymptomatic individuals and a complementary examination in screening of women with dense breasts. Handheld US is the standard-of-care, yet when used in whole-breast examination, no effort has been devoted to monitoring breast coverage and missed regions, which is the purpose of this study. METHODS: We introduce a computer-aided system assisting radiologists and US technologists in covering the whole breast with minimum alteration to the standard workflow. The proposed system comprises a standard US device, proprietary electromagnetic 3D tracking technology and software that combines US visual and tracking data to estimate a probe trajectory, total time spent in different breast segments, and a map of missed regions. A case study, which involved four radiologists (two junior and two senior) performing whole-breast ultrasound in 75 asymptomatic patients, was conducted to test the importance and relevance of the system. RESULTS: The mean process time per breast was [Formula: see text], with no statistically significant difference between the left and the right sides, and slightly longer examination time of junior radiologists. The process time density shows that central parts of the breast have better coverage compared to the periphery. Within the central part, missed regions of minimum detectable size of [Formula: see text] occur in [Formula: see text] of examinations, and non-negligible [Formula: see text] regions occur in [Formula: see text] of cases. CONCLUSION: The results of the case study indicate that missed regions are present in handheld whole-breast US, which renders the proposed system for tracking the probe position during examination a valuable tool for monitoring coverage.

• Klíčová slova
• Breast, Cancer, Coverage, Screening, Tracking, Ultrasound,
• MeSH
• design vybavení MeSH
• diagnóza počítačová * MeSH
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mamografie metody   MeSH
• nádory prsu diagnostické zobrazování   MeSH
• počítače do ruky MeSH
• počítačové systémy MeSH
• počítačové zpracování obrazu MeSH
• prsy diagnostické zobrazování   MeSH
• reprodukovatelnost výsledků MeSH
• software MeSH
• ultrasonografie prsů metody   MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

We analyzed 3,872 common genetic variants across the ESR1 locus (encoding estrogen receptor α) in 118,816 subjects from three international consortia. We found evidence for at least five independent causal variants, each associated with different phenotype sets, including estrogen receptor (ER(+) or ER(-)) and human ERBB2 (HER2(+) or HER2(-)) tumor subtypes, mammographic density and tumor grade. The best candidate causal variants for ER(-) tumors lie in four separate enhancer elements, and their risk alleles reduce expression of ESR1, RMND1 and CCDC170, whereas the risk alleles of the strongest candidates for the remaining independent causal variant disrupt a silencer element and putatively increase ESR1 and RMND1 expression.

• MeSH
• alfa receptor estrogenů genetika   metabolismus   MeSH
• exprese genu MeSH
• fenotyp MeSH
• genetická predispozice k nemoci MeSH
• genetické asociační studie MeSH
• jednonukleotidový polymorfismus MeSH
• lidé MeSH
• lidské chromozomy, pár 6 genetika   MeSH
• nádory prsu genetika   metabolismus   MeSH
• proteiny buněčného cyklu genetika   metabolismus   MeSH
• regulace genové exprese u nádorů MeSH
• rizikové faktory MeSH
• sekvence nukleotidů MeSH
• transportní proteiny genetika   metabolismus   MeSH
• vazba proteinů MeSH
• Check Tag
• lidé MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• metaanalýza MeSH
• práce podpořená grantem MeSH
• Research Support, N.I.H., Extramural MeSH
• Research Support, U.S. Gov't, Non-P.H.S. MeSH
• Názvy látek
• alfa receptor estrogenů MeSH
• CCDC170 protein, human MeSH Prohlížeč
• ESR1 protein, human MeSH Prohlížeč
• proteiny buněčného cyklu MeSH
• RMND1 protein, human MeSH Prohlížeč
• transportní proteiny MeSH