Cargando…

A novel and fully automated mammographic texture analysis for risk prediction: results from two case-control studies

BACKGROUND: The percentage of mammographic dense tissue (PD) is an important risk factor for breast cancer, and there is some evidence that texture features may further improve predictive ability. However, relatively little work has assessed or validated textural feature algorithms using raw full fi...

Descripción completa

Detalles Bibliográficos
Autores principales:	Wang, Chao, Brentnall, Adam R., Cuzick, Jack, Harkness, Elaine F., Evans, D. Gareth, Astley, Susan
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	BioMed Central 2017
Materias:	Research Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5648465/ https://www.ncbi.nlm.nih.gov/pubmed/29047382 http://dx.doi.org/10.1186/s13058-017-0906-6

Ejemplares similares

Exploring the prediction performance for breast cancer risk based on volumetric mammographic density at different thresholds
por: Wang, Chao, et al.
Publicado: (2018)

Mammographic density change in a cohort of premenopausal women receiving tamoxifen for breast cancer prevention over 5 years
por: Brentnall, Adam R., et al.
Publicado: (2020)

A case–control evaluation of 143 single nucleotide polymorphisms for breast cancer risk stratification with classical factors and mammographic density
por: Brentnall, Adam R., et al.
Publicado: (2019)

External validation of a mammographic texture marker for breast cancer risk in a case–control study
por: Wang, Chao, et al.
Publicado: (2020)

The Relationship between Body Mass Index and Mammographic Density during a Premenopausal Weight Loss Intervention Study
por: Atakpa, Emma C., et al.
Publicado: (2021)

A comparison of five methods of measuring mammographic density: a case-control study
por: Astley, Susan M., et al.
Publicado: (2018)

Breast cancer pathology and stage are better predicted by risk stratification models that include mammographic density and common genetic variants
por: Evans, D. Gareth R., et al.
Publicado: (2019)

Mammographic density adds accuracy to both the Tyrer-Cuzick and Gail breast cancer risk models in a prospective UK screening cohort
por: Brentnall, Adam R., et al.
Publicado: (2015)

Validation of a new fully automated software for 2D digital mammographic breast density evaluation in predicting breast cancer risk
por: Giorgi Rossi, Paolo, et al.
Publicado: (2021)

Fully convolutional network for automated detection and diagnosis of mammographic masses
por: Kulkarni, Sujata, et al.
Publicado: (2023)

Obesity at age 20 and weight gain during adulthood increase risk of total and premature all-cause mortality: findings from women attending breast screening in Manchester
por: Pegington, Mary, et al.
Publicado: (2023)

Development and evaluation of a method to assess breast cancer risk using a longitudinal history of mammographic density: a cohort study
por: Atakpa, Emma C., et al.
Publicado: (2023)

A Case-Control Study to Add Volumetric or Clinical Mammographic Density into the Tyrer-Cuzick Breast Cancer Risk Model
por: Brentnall, Adam R, et al.
Publicado: (2019)

Statistical Evaluation of a Fully Automated Mammographic Breast Density Algorithm
por: Abdolell, Mohamed, et al.
Publicado: (2013)

Breast cancer risk in a screening cohort of Asian and white British/Irish women from Manchester UK
por: Evans, D. Gareth, et al.
Publicado: (2018)

Automated percent mammographic density, mammographic texture variation, and risk of breast cancer: a nested case-control study
por: Warner, Erica T., et al.
Publicado: (2021)

Young adulthood body mass index, adult weight gain and breast cancer risk: the PROCAS Study (United Kingdom)
por: Renehan, Andrew G., et al.
Publicado: (2020)

Computer-assisted mammographic imaging
por: Boggis, Caroline RM, et al.
Publicado: (2000)

The importance of ethnicity: Are breast cancer polygenic risk scores ready for women who are not of White European origin?
por: Evans, D. Gareth, et al.
Publicado: (2021)

The effect of variable labels on deep learning models trained to predict breast density
por: Squires, Steven, et al.
Publicado: (2023)

Mammographic Breast Density Evaluation in Korean Women Using Fully Automated Volumetric Assessment
por: Youn, Inyoung, et al.
Publicado: (2016)

Mammographic Breast Density Assessed with Fully Automated Method and its Risk for Breast Cancer
por: Saikiran, Pendem, et al.
Publicado: (2019)

Use of the concordance index for predictors of censored survival data
por: Brentnall, Adam R, et al.
Publicado: (2016)

Risk Models for Breast Cancer and Their Validation
por: Brentnall, Adam R., et al.
Publicado: (2020)

Mammographic breast density refines Tyrer-Cuzick estimates of breast cancer risk in high-risk women: findings from the placebo arm of the International Breast Cancer Intervention Study I
por: Warwick, Jane, et al.
Publicado: (2014)

Breast cancer risk feedback to women in the UK NHS breast screening population
por: Evans, D Gareth R, et al.
Publicado: (2016)

Is Breast Cancer Risk Associated with Menopausal Hormone Therapy Modified by Current or Early Adulthood BMI or Age of First Pregnancy?
por: Leventea, Eleni, et al.
Publicado: (2021)

Characterizing mammographic images by using generic texture features
por: Häberle, Lothar, et al.
Publicado: (2012)

SNPs for breast cancer risk assessment
por: Cuzick, Jack, et al.
Publicado: (2017)

PB.22. Does mammographic compression force at breast screening influence the likelihood of subsequent screening attendance?
por: Meyer, J, et al.
Publicado: (2014)

Measuring mammographic density: comparing a fully automated volumetric assessment versus European radiologists’ qualitative classification
por: Sartor, Hanna, et al.
Publicado: (2016)

Comparison of fully and semi-automated area-based methods for measuring mammographic density and predicting breast cancer risk
por: Sovio, U, et al.
Publicado: (2014)

Estimating efficacy in trials with selective crossover
por: Brentnall, Adam R., et al.
Publicado: (2017)

A genome-wide association study of mammographic texture variation
por: Liu, Yuxi, et al.
Publicado: (2022)

Textural Classification of Mammographic Parenchymal Patterns with the SONNET Selforganizing Neural Network
por: Howard, Daniel, et al.
Publicado: (2008)

Mammographic texture resemblance generalizes as an independent risk factor for breast cancer
por: Nielsen, Mads, et al.
Publicado: (2014)

Mammographic texture features associated with contralateral breast cancer in the WECARE Study
por: Watt, Gordon P., et al.
Publicado: (2021)

Mammographic texture and risk of breast cancer by tumor type and estrogen receptor status
por: Malkov, Serghei, et al.
Publicado: (2016)

What are the benefits and harms of risk stratified screening as part of the NHS breast screening Programme? Study protocol for a multi-site non-randomised comparison of BC-predict versus usual screening (NCT04359420)
por: French, David P., et al.
Publicado: (2020)

A comparison of methylation levels in HPV18, HPV31 and HPV33 genomes reveals similar associations with cervical precancers()
por: Vasiljević, Nataša, et al.
Publicado: (2014)

Cannot write session to /tmp/vufind_sessions/sess_i6qurteelr1c7q113gom8af6mh