BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

167 related articles for article (PubMed ID: 35962988)

  • 1. A spatial attention guided deep learning system for prediction of pathological complete response using breast cancer histopathology images.
    Duanmu H; Bhattarai S; Li H; Shi Z; Wang F; Teodoro G; Gogineni K; Subhedar P; Kiraz U; Janssen EAM; Aneja R; Kong J
    Bioinformatics; 2022 Sep; 38(19):4605-4612. PubMed ID: 35962988
    [TBL] [Abstract][Full Text] [Related]  

  • 2. A hierarchical self-attention-guided deep learning framework to predict breast cancer response to chemotherapy using pre-treatment tumor biopsies.
    Saednia K; Tran WT; Sadeghi-Naini A
    Med Phys; 2023 Dec; 50(12):7852-7864. PubMed ID: 37403567
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Digital image analysis and machine learning-assisted prediction of neoadjuvant chemotherapy response in triple-negative breast cancer.
    Fisher TB; Saini G; Rekha TS; Krishnamurthy J; Bhattarai S; Callagy G; Webber M; Janssen EAM; Kong J; Aneja R
    Breast Cancer Res; 2024 Jan; 26(1):12. PubMed ID: 38238771
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Deep learning with biopsy whole slide images for pretreatment prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer:A multicenter study.
    Li B; Li F; Liu Z; Xu F; Ye G; Li W; Zhang Y; Zhu T; Shao L; Chen C; Sun C; Qiu B; Bu H; Wang K; Tian J
    Breast; 2022 Dec; 66():183-190. PubMed ID: 36308926
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Predicting Neoadjuvant Treatment Response in Triple-Negative Breast Cancer Using Machine Learning.
    Bhattarai S; Saini G; Li H; Seth G; Fisher TB; Janssen EAM; Kiraz U; Kong J; Aneja R
    Diagnostics (Basel); 2023 Dec; 14(1):. PubMed ID: 38201383
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Spatial Attention-Based Deep Learning System for Breast Cancer Pathological Complete Response Prediction with Serial Histopathology Images in Multiple Stains.
    Duanmu H; Bhattarai S; Li H; Cheng CC; Wang F; Teodoro G; Janssen EAM; Gogineni K; Subhedar P; Aneja R; Kong J
    Med Image Comput Comput Assist Interv; 2021; 12908():550-560. PubMed ID: 36222817
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Predicting neoadjuvant treatment response in triple-negative breast cancer using machine learning.
    Bhattarai S; Saini G; Li H; Duanmu H; Seth G; Fisher TB; Janssen EAM; Kiraz U; Kong J; Aneja R
    bioRxiv; 2023 Apr; ():. PubMed ID: 37131688
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Deep learning-based predictive biomarker of pathological complete response to neoadjuvant chemotherapy from histological images in breast cancer.
    Li F; Yang Y; Wei Y; He P; Chen J; Zheng Z; Bu H
    J Transl Med; 2021 Aug; 19(1):348. PubMed ID: 34399795
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Deep learning-based predictive model for pathological complete response to neoadjuvant chemotherapy in breast cancer from biopsy pathological images: a multicenter study.
    Zeng H; Qiu S; Zhuang S; Wei X; Wu J; Zhang R; Chen K; Wu Z; Zhuang Z
    Front Physiol; 2024; 15():1279982. PubMed ID: 38357498
    [No Abstract]   [Full Text] [Related]  

  • 10. Digital image analysis and machine learning-assisted prediction of neoadjuvant chemotherapy response in triple-negative breast cancer.
    Fisher TB; Saini G; Ts R; Krishnamurthy J; Bhattarai S; Callagy G; Webber M; Janssen EAM; Kong J; Aneja R
    Res Sq; 2023 Aug; ():. PubMed ID: 37645881
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Deep learning radiomics of ultrasonography for comprehensively predicting tumor and axillary lymph node status after neoadjuvant chemotherapy in breast cancer patients: A multicenter study.
    Gu J; Tong T; Xu D; Cheng F; Fang C; He C; Wang J; Wang B; Yang X; Wang K; Tian J; Jiang T
    Cancer; 2023 Feb; 129(3):356-366. PubMed ID: 36401611
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer using a deep learning (DL) method.
    Qu YH; Zhu HT; Cao K; Li XT; Ye M; Sun YS
    Thorac Cancer; 2020 Mar; 11(3):651-658. PubMed ID: 31944571
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Predicting Response of Triple-Negative Breast Cancer to Neoadjuvant Chemotherapy Using a Deep Convolutional Neural Network-Based Artificial Intelligence Tool.
    Krishnamurthy S; Jain P; Tripathy D; Basset R; Randhawa R; Muhammad H; Huang W; Yang H; Kummar S; Wilding G; Roy R
    JCO Clin Cancer Inform; 2023 Mar; 7():e2200181. PubMed ID: 36961981
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Radiomics features based on automatic segmented MRI images: Prognostic biomarkers for triple-negative breast cancer treated with neoadjuvant chemotherapy.
    Ma M; Gan L; Liu Y; Jiang Y; Xin L; Liu Y; Qin N; Cheng Y; Liu Q; Xu L; Zhang Y; Wang X; Zhang X; Ye J; Wang X
    Eur J Radiol; 2022 Jan; 146():110095. PubMed ID: 34890936
    [TBL] [Abstract][Full Text] [Related]  

  • 15. [Prediction of Response to Neoadjuvant Chemotherapy for Breast Cancer Based on Histomorphology Analysis of Needle Biopsy Images].
    Xu CY; Xie JW; Yang CX; Jiang YN; Zhang ZH; Xu J
    Sichuan Da Xue Xue Bao Yi Xue Ban; 2021 Mar; 52(2):279-285. PubMed ID: 33829703
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Deep learning radiopathomics based on preoperative US images and biopsy whole slide images can distinguish between luminal and non-luminal tumors in early-stage breast cancers.
    Huang Y; Yao Z; Li L; Mao R; Huang W; Hu Z; Hu Y; Wang Y; Guo R; Tang X; Yang L; Wang Y; Luo R; Yu J; Zhou J
    EBioMedicine; 2023 Aug; 94():104706. PubMed ID: 37478528
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Predicting pathological complete response to neoadjuvant systemic therapy for triple-negative breast cancers using deep learning on multiparametric MRIs.
    Zhou Z; Adrada BE; Candelaria RP; Elshafeey NA; Boge M; Mohamed RM; Pashapoor S; Sun J; Xu Z; Panthi B; Son JB; Guirguis MS; Patel MM; Whitman GJ; Moseley TW; Scoggins ME; White JB; Litton JK; Valero V; Hunt KK; Tripathy D; Yang W; Wei P; Yam C; Pagel MD; Rauch GM; Ma J
    Annu Int Conf IEEE Eng Med Biol Soc; 2023 Jul; 2023():1-4. PubMed ID: 38083160
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Prediction of pathologic complete response to neoadjuvant systemic therapy in triple negative breast cancer using deep learning on multiparametric MRI.
    Zhou Z; Adrada BE; Candelaria RP; Elshafeey NA; Boge M; Mohamed RM; Pashapoor S; Sun J; Xu Z; Panthi B; Son JB; Guirguis MS; Patel MM; Whitman GJ; Moseley TW; Scoggins ME; White JB; Litton JK; Valero V; Hunt KK; Tripathy D; Yang W; Wei P; Yam C; Pagel MD; Rauch GM; Ma J
    Sci Rep; 2023 Jan; 13(1):1171. PubMed ID: 36670144
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Multimodal deep learning models for the prediction of pathologic response to neoadjuvant chemotherapy in breast cancer.
    Joo S; Ko ES; Kwon S; Jeon E; Jung H; Kim JY; Chung MJ; Im YH
    Sci Rep; 2021 Sep; 11(1):18800. PubMed ID: 34552163
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Predicting breast cancer response to neoadjuvant chemotherapy using ensemble deep transfer learning based on CT images.
    Rezaeijo SM; Ghorvei M; Mofid B
    J Xray Sci Technol; 2021; 29(5):835-850. PubMed ID: 34219704
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 9.