BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

223 related articles for article (PubMed ID: 34609196)

  • 1. Deep Learning for Automated Triaging of 4581 Breast MRI Examinations from the DENSE Trial.
    Verburg E; van Gils CH; van der Velden BHM; Bakker MF; Pijnappel RM; Veldhuis WB; Gilhuijs KGA
    Radiology; 2022 Jan; 302(1):29-36. PubMed ID: 34609196
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Validation of Combined Deep Learning Triaging and Computer-Aided Diagnosis in 2901 Breast MRI Examinations From the Second Screening Round of the Dense Tissue and Early Breast Neoplasm Screening Trial.
    Verburg E; van Gils CH; van der Velden BHM; Bakker MF; Pijnappel RM; Veldhuis WB; Gilhuijs KGA
    Invest Radiol; 2023 Apr; 58(4):293-298. PubMed ID: 36256783
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Computer-Aided Diagnosis in Multiparametric Magnetic Resonance Imaging Screening of Women With Extremely Dense Breasts to Reduce False-Positive Diagnoses.
    Verburg E; van Gils CH; Bakker MF; Viergever MA; Pijnappel RM; Veldhuis WB; Gilhuijs KGA
    Invest Radiol; 2020 Jul; 55(7):438-444. PubMed ID: 32149858
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Reducing False-Positive Screening MRI Rate in Women with Extremely Dense Breasts Using Prediction Models Based on Data from the DENSE Trial.
    den Dekker BM; Bakker MF; de Lange SV; Veldhuis WB; van Diest PJ; Duvivier KM; Lobbes MBI; Loo CE; Mann RM; Monninkhof EM; Veltman J; Pijnappel RM; van Gils CH;
    Radiology; 2021 Nov; 301(2):283-292. PubMed ID: 34402665
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Supplemental Breast MRI for Women with Extremely Dense Breasts: Results of the Second Screening Round of the DENSE Trial.
    Veenhuizen SGA; de Lange SV; Bakker MF; Pijnappel RM; Mann RM; Monninkhof EM; Emaus MJ; de Koekkoek-Doll PK; Bisschops RHC; Lobbes MBI; de Jong MDF; Duvivier KM; Veltman J; Karssemeijer N; de Koning HJ; van Diest PJ; Mali WPTM; van den Bosch MAAJ; van Gils CH; Veldhuis WB;
    Radiology; 2021 May; 299(2):278-286. PubMed ID: 33724062
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Knowledge-based and deep learning-based automated chest wall segmentation in magnetic resonance images of extremely dense breasts.
    Verburg E; Wolterink JM; de Waard SN; Išgum I; van Gils CH; Veldhuis WB; Gilhuijs KGA
    Med Phys; 2019 Oct; 46(10):4405-4416. PubMed ID: 31274194
    [TBL] [Abstract][Full Text] [Related]  

  • 7. A Deep Learning Model to Triage Screening Mammograms: A Simulation Study.
    Yala A; Schuster T; Miles R; Barzilay R; Lehman C
    Radiology; 2019 Oct; 293(1):38-46. PubMed ID: 31385754
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Automated rating of background parenchymal enhancement in MRI of extremely dense breasts without compromising the association with breast cancer in the DENSE trial.
    Wang H; H M van der Velden B; Verburg E; Bakker MF; Pijnappel RM; Veldhuis WB; van Gils CH; Gilhuijs KGA
    Eur J Radiol; 2024 Jun; 175():111442. PubMed ID: 38583349
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Artificial Intelligence Applied to Breast MRI for Improved Diagnosis.
    Jiang Y; Edwards AV; Newstead GM
    Radiology; 2021 Jan; 298(1):38-46. PubMed ID: 33078996
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Outcomes of Return to Routine Screening for BI-RADS 3 Lesions Detected at Supplemental Automated Whole-Breast Ultrasound in Women With Dense Breasts: A Prospective Study.
    Barr RG; DeSivestri A; Golatta M
    AJR Am J Roentgenol; 2021 Dec; 217(6):1313-1321. PubMed ID: 34259039
    [No Abstract]   [Full Text] [Related]  

  • 11. Assessing Quantitative Parenchymal Features at Baseline Dynamic Contrast-enhanced MRI and Cancer Occurrence in Women with Extremely Dense Breasts.
    Wang H; van der Velden BHM; Verburg E; Bakker MF; Pijnappel RM; Veldhuis WB; van Gils CH; Gilhuijs KGA
    Radiology; 2023 Aug; 308(2):e222841. PubMed ID: 37552061
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Automated Triage of Screening Breast MRI Examinations in High-Risk Women Using an Ensemble Deep Learning Model.
    Bhowmik A; Monga N; Belen K; Varela K; Sevilimedu V; Thakur SB; Martinez DF; Sutton EJ; Pinker K; Eskreis-Winkler S
    Invest Radiol; 2023 Oct; 58(10):710-719. PubMed ID: 37058323
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Deep learning combining mammography and ultrasound images to predict the malignancy of BI-RADS US 4A lesions in women with dense breasts: a diagnostic study.
    Yang Y; Zhong Y; Li J; Feng J; Gong C; Yu Y; Hu Y; Gu R; Wang H; Liu F; Mei J; Jiang X; Wang J; Yao Q; Wu W; Liu Q; Yao H
    Int J Surg; 2024 May; 110(5):2604-2613. PubMed ID: 38348891
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Reasons for (non)participation in supplemental population-based MRI breast screening for women with extremely dense breasts.
    de Lange SV; Bakker MF; Monninkhof EM; Peeters PHM; de Koekkoek-Doll PK; Mann RM; Rutten MJCM; Bisschops RHC; Veltman J; Duvivier KM; Lobbes MBI; de Koning HJ; Karssemeijer N; Pijnappel RM; Veldhuis WB; van Gils CH
    Clin Radiol; 2018 Aug; 73(8):759.e1-759.e9. PubMed ID: 29759590
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Mammographic Breast Density Assessment Using Deep Learning: Clinical Implementation.
    Lehman CD; Yala A; Schuster T; Dontchos B; Bahl M; Swanson K; Barzilay R
    Radiology; 2019 Jan; 290(1):52-58. PubMed ID: 30325282
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Using deep learning to safely exclude lesions with only ultrafast breast MRI to shorten acquisition and reading time.
    Jing X; Wielema M; Cornelissen LJ; van Gent M; Iwema WM; Zheng S; Sijens PE; Oudkerk M; Dorrius MD; van Ooijen PMA
    Eur Radiol; 2022 Dec; 32(12):8706-8715. PubMed ID: 35614363
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Deep Learning Model for Automated Detection and Classification of Central Canal, Lateral Recess, and Neural Foraminal Stenosis at Lumbar Spine MRI.
    Hallinan JTPD; Zhu L; Yang K; Makmur A; Algazwi DAR; Thian YL; Lau S; Choo YS; Eide SE; Yap QV; Chan YH; Tan JH; Kumar N; Ooi BC; Yoshioka H; Quek ST
    Radiology; 2021 Jul; 300(1):130-138. PubMed ID: 33973835
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Frequency and Cancer Yield of BI-RADS Category 3 Lesions Detected at High-Risk Screening Breast MRI.
    Edmonds CE; Lamb LR; Mercaldo SF; Sippo DA; Burk KS; Lehman CD
    AJR Am J Roentgenol; 2020 Feb; 214(2):240-248. PubMed ID: 31799867
    [No Abstract]   [Full Text] [Related]  

  • 19. Breast density and the likelihood of malignant MRI-detected lesions in women diagnosed with breast cancer.
    Sassi A; Salminen A; Jukkola A; Tervo M; Mäenpää N; Turtiainen S; Tiainen L; Liimatainen T; Tolonen T; Huhtala H; Rinta-Kiikka I; Arponen O
    Eur Radiol; 2023 Nov; 33(11):8080-8088. PubMed ID: 37646814
    [TBL] [Abstract][Full Text] [Related]  

  • 20.
    ; ; . PubMed ID:
    [No Abstract]   [Full Text] [Related]  

    [Next]    [New Search]
    of 12.