BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

124 related articles for article (PubMed ID: 38281863)

  • 21. Mammography, US, and MRI to Assess Outcomes of Invasive Breast Cancer with Extensive Intraductal Component: A Matched Cohort Study.
    Ha SM; Cha JH; Shin HJ; Chae EY; Choi WJ; Kim HH
    Radiology; 2019 Aug; 292(2):299-308. PubMed ID: 31135297
    [TBL] [Abstract][Full Text] [Related]  

  • 22. The deep learning model combining CT image and clinicopathological information for predicting ALK fusion status and response to ALK-TKI therapy in non-small cell lung cancer patients.
    Song Z; Liu T; Shi L; Yu Z; Shen Q; Xu M; Huang Z; Cai Z; Wang W; Xu C; Sun J; Chen M
    Eur J Nucl Med Mol Imaging; 2021 Feb; 48(2):361-371. PubMed ID: 32794105
    [TBL] [Abstract][Full Text] [Related]  

  • 23. Transfer learning of pre-treatment quantitative ultrasound multi-parametric images for the prediction of breast cancer response to neoadjuvant chemotherapy.
    Falou O; Sannachi L; Haque M; Czarnota GJ; Kolios MC
    Sci Rep; 2024 Jan; 14(1):2340. PubMed ID: 38282158
    [TBL] [Abstract][Full Text] [Related]  

  • 24. Deep learning model utilizing clinical data alone outperforms image-based model for hernia recurrence following abdominal wall reconstruction with long-term follow up.
    Wilson HH; Ma C; Ku D; Scarola GT; Augenstein VA; Colavita PD; Heniford BT
    Surg Endosc; 2024 Jun; ():. PubMed ID: 38862826
    [TBL] [Abstract][Full Text] [Related]  

  • 25. Cost-effectiveness of using prognostic information to select women with breast cancer for adjuvant systemic therapy.
    Williams C; Brunskill S; Altman D; Briggs A; Campbell H; Clarke M; Glanville J; Gray A; Harris A; Johnston K; Lodge M
    Health Technol Assess; 2006 Sep; 10(34):iii-iv, ix-xi, 1-204. PubMed ID: 16959170
    [TBL] [Abstract][Full Text] [Related]  

  • 26. Ultrasound and clinicopathological characteristics-based model for prediction of pathologic response to neoadjuvant chemotherapy in HER2-positive breast cancer: a case-control study.
    Sui L; Yan Y; Jiang T; Ou D; Chen C; Lai M; Ni C; Zhu X; Wang L; Yang C; Li W; Yao J; Xu D
    Breast Cancer Res Treat; 2023 Nov; 202(1):45-55. PubMed ID: 37639063
    [TBL] [Abstract][Full Text] [Related]  

  • 27. Mammography-based radiomics for predicting the risk of breast cancer recurrence: a multicenter study.
    Mao N; Yin P; Zhang H; Zhang K; Song X; Xing D; Chu T
    Br J Radiol; 2021 Nov; 94(1127):20210348. PubMed ID: 34520235
    [TBL] [Abstract][Full Text] [Related]  

  • 28. Deep learning-based computer-aided diagnosis in screening breast ultrasound to reduce false-positive diagnoses.
    Kim S-; Choi Y; Kim E-; Han BK; Yoon JH; Choi JS; Chang JM
    Sci Rep; 2021 Jan; 11(1):395. PubMed ID: 33432076
    [TBL] [Abstract][Full Text] [Related]  

  • 29. Prediction of postoperative disease-free survival and brain metastasis for HER2-positive breast cancer patients treated with neoadjuvant chemotherapy plus trastuzumab using a machine learning algorithm.
    Takada M; Sugimoto M; Masuda N; Iwata H; Kuroi K; Yamashiro H; Ohno S; Ishiguro H; Inamoto T; Toi M
    Breast Cancer Res Treat; 2018 Dec; 172(3):611-618. PubMed ID: 30194511
    [TBL] [Abstract][Full Text] [Related]  

  • 30. Image Quality Assessment of Fetal Brain MRI Using Multi-Instance Deep Learning Methods.
    Largent A; Kapse K; Barnett SD; De Asis-Cruz J; Whitehead M; Murnick J; Zhao L; Andersen N; Quistorff J; Lopez C; Limperopoulos C
    J Magn Reson Imaging; 2021 Sep; 54(3):818-829. PubMed ID: 33891778
    [TBL] [Abstract][Full Text] [Related]  

  • 31. Construction of a risk stratification model integrating ctDNA to predict response and survival in neoadjuvant-treated breast cancer.
    Liu Z; Yu B; Su M; Yuan C; Liu C; Wang X; Song X; Li C; Wang F; Ma J; Wu M; Chen D; Yu J; Yu Z
    BMC Med; 2023 Dec; 21(1):493. PubMed ID: 38087296
    [TBL] [Abstract][Full Text] [Related]  

  • 32. Monitoring response to neoadjuvant therapy for breast cancer in all treatment phases using an ultrasound deep learning model.
    Zhang J; Deng J; Huang J; Mei L; Liao N; Yao F; Lei C; Sun S; Zhang Y
    Front Oncol; 2024; 14():1255618. PubMed ID: 38327750
    [TBL] [Abstract][Full Text] [Related]  

  • 33. Deep Learning Predicts Interval and Screening-detected Cancer from Screening Mammograms: A Case-Case-Control Study in 6369 Women.
    Zhu X; Wolfgruber TK; Leong L; Jensen M; Scott C; Winham S; Sadowski P; Vachon C; Kerlikowske K; Shepherd JA
    Radiology; 2021 Dec; 301(3):550-558. PubMed ID: 34491131
    [TBL] [Abstract][Full Text] [Related]  

  • 34. AsymMirai: Interpretable Mammography-based Deep Learning Model for 1-5-year Breast Cancer Risk Prediction.
    Donnelly J; Moffett L; Barnett AJ; Trivedi H; Schwartz F; Lo J; Rudin C
    Radiology; 2024 Mar; 310(3):e232780. PubMed ID: 38501952
    [TBL] [Abstract][Full Text] [Related]  

  • 35. A divide and conquer approach to maximise deep learning mammography classification accuracies.
    Jaamour A; Myles C; Patel A; Chen SJ; McMillan L; Harris-Birtill D
    PLoS One; 2023; 18(5):e0280841. PubMed ID: 37235566
    [TBL] [Abstract][Full Text] [Related]  

  • 36. Deep-Learning-Based Semantic Labeling for 2D Mammography and Comparison of Complexity for Machine Learning Tasks.
    Yi PH; Lin A; Wei J; Yu AC; Sair HI; Hui FK; Hager GD; Harvey SC
    J Digit Imaging; 2019 Aug; 32(4):565-570. PubMed ID: 31197559
    [TBL] [Abstract][Full Text] [Related]  

  • 37. A deep learning-based automated diagnostic system for classifying mammographic lesions.
    Yamaguchi T; Inoue K; Tsunoda H; Uematsu T; Shinohara N; Mukai H
    Medicine (Baltimore); 2020 Jul; 99(27):e20977. PubMed ID: 32629712
    [TBL] [Abstract][Full Text] [Related]  

  • 38. Deep Learning Model Based on Dual-Modal Ultrasound and Molecular Data for Predicting Response to Neoadjuvant Chemotherapy in Breast Cancer.
    Huang JX; Shi J; Ding SS; Zhang HL; Wang XY; Lin SY; Xu YF; Wei MJ; Liu LZ; Pei XQ
    Acad Radiol; 2023 Sep; 30 Suppl 2():S50-S61. PubMed ID: 37270368
    [TBL] [Abstract][Full Text] [Related]  

  • 39. Comparison of a Deep Learning Risk Score and Standard Mammographic Density Score for Breast Cancer Risk Prediction.
    Dembrower K; Liu Y; Azizpour H; Eklund M; Smith K; Lindholm P; Strand F
    Radiology; 2020 Feb; 294(2):265-272. PubMed ID: 31845842
    [TBL] [Abstract][Full Text] [Related]  

  • 40. Mammography-based deep learning model for coronary artery calcification.
    Ahn S; Chang Y; Kwon R; Kang J; Choi J; Lim GY; Kwon MR; Ryu S; Shin J
    Eur Heart J Cardiovasc Imaging; 2024 Mar; 25(4):456-466. PubMed ID: 37988168
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 7.