These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

238 related articles for article (PubMed ID: 30577031)

  • 1. A novel method of determining breast cancer risk using parenchymal textural analysis of mammography images on an Asian cohort.
    Tan M; Mariapun S; Yip CH; Ng KH; Teo SH
    Phys Med Biol; 2019 Jan; 64(3):035016. PubMed ID: 30577031
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Mammographic density and structural features can individually and jointly contribute to breast cancer risk assessment in mammography screening: a case-control study.
    Winkel RR; von Euler-Chelpin M; Nielsen M; Petersen K; Lillholm M; Nielsen MB; Lynge E; Uldall WY; Vejborg I
    BMC Cancer; 2016 Jul; 16():414. PubMed ID: 27387546
    [TBL] [Abstract][Full Text] [Related]  

  • 3. A novel and fully automated mammographic texture analysis for risk prediction: results from two case-control studies.
    Wang C; Brentnall AR; Cuzick J; Harkness EF; Evans DG; Astley S
    Breast Cancer Res; 2017 Oct; 19(1):114. PubMed ID: 29047382
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Global parenchymal texture features based on histograms of oriented gradients improve cancer development risk estimation from healthy breasts.
    Pérez-Benito FJ; Signol F; Pérez-Cortés JC; Pollán M; Pérez-Gómez B; Salas-Trejo D; Casals M; Martínez I; LLobet R
    Comput Methods Programs Biomed; 2019 Aug; 177():123-132. PubMed ID: 31319940
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Fractal analysis of mammographic parenchymal patterns in breast cancer risk assessment.
    Li H; Giger ML; Olopade OI; Lan L
    Acad Radiol; 2007 May; 14(5):513-21. PubMed ID: 17434064
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Breast Cancer Risk and Mammographic Density Assessed with Semiautomated and Fully Automated Methods and BI-RADS.
    Jeffers AM; Sieh W; Lipson JA; Rothstein JH; McGuire V; Whittemore AS; Rubin DL
    Radiology; 2017 Feb; 282(2):348-355. PubMed ID: 27598536
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Assessment of a Four-View Mammographic Image Feature Based Fusion Model to Predict Near-Term Breast Cancer Risk.
    Tan M; Pu J; Cheng S; Liu H; Zheng B
    Ann Biomed Eng; 2015 Oct; 43(10):2416-28. PubMed ID: 25851469
    [TBL] [Abstract][Full Text] [Related]  

  • 8. The combined effect of mammographic texture and density on breast cancer risk: a cohort study.
    Wanders JOP; van Gils CH; Karssemeijer N; Holland K; Kallenberg M; Peeters PHM; Nielsen M; Lillholm M
    Breast Cancer Res; 2018 May; 20(1):36. PubMed ID: 29720220
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Computerized analysis of mammographic parenchymal patterns on a large clinical dataset of full-field digital mammograms: robustness study with two high-risk datasets.
    Li H; Giger ML; Lan L; Bancroft Brown J; MacMahon A; Mussman M; Olopade OI; Sennett C
    J Digit Imaging; 2012 Oct; 25(5):591-8. PubMed ID: 22246204
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Predicting interval and screen-detected breast cancers from mammographic density defined by different brightness thresholds.
    Nguyen TL; Aung YK; Li S; Trinh NH; Evans CF; Baglietto L; Krishnan K; Dite GS; Stone J; English DR; Song YM; Sung J; Jenkins MA; Southey MC; Giles GG; Hopper JL
    Breast Cancer Res; 2018 Dec; 20(1):152. PubMed ID: 30545395
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Parenchymal texture analysis in digital mammography: A fully automated pipeline for breast cancer risk assessment.
    Zheng Y; Keller BM; Ray S; Wang Y; Conant EF; Gee JC; Kontos D
    Med Phys; 2015 Jul; 42(7):4149-60. PubMed ID: 26133615
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Using Convolutional Neural Networks for Enhanced Capture of Breast Parenchymal Complexity Patterns Associated with Breast Cancer Risk.
    Gastounioti A; Oustimov A; Hsieh MK; Pantalone L; Conant EF; Kontos D
    Acad Radiol; 2018 Aug; 25(8):977-984. PubMed ID: 29395798
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Adjusting for BMI in analyses of volumetric mammographic density and breast cancer risk.
    Hudson S; Vik Hjerkind K; Vinnicombe S; Allen S; Trewin C; Ursin G; Dos-Santos-Silva I; De Stavola BL
    Breast Cancer Res; 2018 Dec; 20(1):156. PubMed ID: 30594212
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Assessing breast cancer risk within the general screening population: developing a breast cancer risk model to identify higher risk women at mammographic screening.
    Abdolell M; Payne JI; Caines J; Tsuruda K; Barnes PJ; Talbot PJ; Tong O; Brown P; Rivers-Bowerman M; Iles S
    Eur Radiol; 2020 Oct; 30(10):5417-5426. PubMed ID: 32358648
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Exploring the prediction performance for breast cancer risk based on volumetric mammographic density at different thresholds.
    Wang C; Brentnall AR; Cuzick J; Harkness EF; Evans DG; Astley S
    Breast Cancer Res; 2018 Jun; 20(1):49. PubMed ID: 29884207
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Beyond breast density: a review on the advancing role of parenchymal texture analysis in breast cancer risk assessment.
    Gastounioti A; Conant EF; Kontos D
    Breast Cancer Res; 2016 Sep; 18(1):91. PubMed ID: 27645219
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Assessment of global and local region-based bilateral mammographic feature asymmetry to predict short-term breast cancer risk.
    Li Y; Fan M; Cheng H; Zhang P; Zheng B; Li L
    Phys Med Biol; 2018 Jan; 63(2):025004. PubMed ID: 29226849
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Relationships between computer-extracted mammographic texture pattern features and BRCA1/2 mutation status: a cross-sectional study.
    Gierach GL; Li H; Loud JT; Greene MH; Chow CK; Lan L; Prindiville SA; Eng-Wong J; Soballe PW; Giambartolomei C; Mai PL; Galbo CE; Nichols K; Calzone KA; Olopade OI; Gail MH; Giger ML
    Breast Cancer Res; 2014; 16(4):424. PubMed ID: 25159706
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Case-control study of mammographic density and breast cancer risk using processed digital mammograms.
    Habel LA; Lipson JA; Achacoso N; Rothstein JH; Yaffe MJ; Liang RY; Acton L; McGuire V; Whittemore AS; Rubin DL; Sieh W
    Breast Cancer Res; 2016 May; 18(1):53. PubMed ID: 27209070
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Derived mammographic masking measures based on simulated lesions predict the risk of interval cancer after controlling for known risk factors: a case-case analysis.
    Hinton B; Ma L; Mahmoudzadeh AP; Malkov S; Fan B; Greenwood H; Joe B; Lee V; Strand F; Kerlikowske K; Shepherd J
    Med Phys; 2019 Mar; 46(3):1309-1316. PubMed ID: 30697755
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 12.