BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

226 related articles for article (PubMed ID: 30977048)

  • 1. Multiparametric MRI-based radiomics analysis for prediction of breast cancers insensitive to neoadjuvant chemotherapy.
    Xiong Q; Zhou X; Liu Z; Lei C; Yang C; Yang M; Zhang L; Zhu T; Zhuang X; Liang C; Liu Z; Tian J; Wang K
    Clin Transl Oncol; 2020 Jan; 22(1):50-59. PubMed ID: 30977048
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Predicting pathological complete response to neoadjuvant chemotherapy in breast cancer patients: use of MRI radiomics data from three regions with multiple machine learning algorithms.
    Zheng G; Peng J; Shu Z; Jin H; Han L; Yuan Z; Qin X; Hou J; He X; Gong X
    J Cancer Res Clin Oncol; 2024 Mar; 150(3):147. PubMed ID: 38512406
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Construction and validation of a personalized nomogram of ultrasound for pretreatment prediction of breast cancer patients sensitive to neoadjuvant chemotherapy.
    Zhang MQ; Du Y; Zha HL; Liu XP; Cai MJ; Chen ZH; Chen R; Wang J; Wang SJ; Zhang JL; Li CY
    Br J Radiol; 2022 Dec; 95(1140):20220626. PubMed ID: 36378247
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Development of MRI-Based Deep Learning Signature for Prediction of Axillary Response After NAC in Breast Cancer.
    Zhang B; Yu Y; Mao Y; Wang H; Lv M; Su X; Wang Y; Li Z; Zhang Z; Bian T; Wang Q
    Acad Radiol; 2024 Mar; 31(3):800-811. PubMed ID: 37914627
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Pretreatment ultrasound-based deep learning radiomics model for the early prediction of pathologic response to neoadjuvant chemotherapy in breast cancer.
    Yu FH; Miao SM; Li CY; Hang J; Deng J; Ye XH; Liu Y
    Eur Radiol; 2023 Aug; 33(8):5634-5644. PubMed ID: 36976336
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Enhancing pathological complete response prediction in breast cancer: the role of dynamic characterization of DCE-MRI and its association with tumor heterogeneity.
    Zhang X; Teng X; Zhang J; Lai Q; Cai J
    Breast Cancer Res; 2024 May; 26(1):77. PubMed ID: 38745321
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Radiomic analysis for pretreatment prediction of response to neoadjuvant chemotherapy in locally advanced cervical cancer: A multicentre study.
    Sun C; Tian X; Liu Z; Li W; Li P; Chen J; Zhang W; Fang Z; Du P; Duan H; Liu P; Wang L; Chen C; Tian J
    EBioMedicine; 2019 Aug; 46():160-169. PubMed ID: 31395503
    [TBL] [Abstract][Full Text] [Related]  

  • 8. An MRI-based machine learning radiomics can predict short-term response to neoadjuvant chemotherapy in patients with cervical squamous cell carcinoma: A multicenter study.
    Xin Z; Yan W; Feng Y; Yunzhi L; Zhang Y; Wang D; Chen W; Peng J; Guo C; Chen Z; Wang X; Zhu J; Lei J
    Cancer Med; 2023 Oct; 12(19):19383-19393. PubMed ID: 37772478
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Do the combination of multiparametric MRI-based radiomics and selected blood inflammatory markers predict the grade and proliferation in glioma patients?
    Guo J; Ren J; Shen J; Cheng R; He Y
    Diagn Interv Radiol; 2021 May; 27(3):440-449. PubMed ID: 33769289
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Multiparametric MRI model to predict molecular subtypes of breast cancer using Shapley additive explanations interpretability analysis.
    Huang Y; Wang X; Cao Y; Li M; Li L; Chen H; Tang S; Lan X; Jiang F; Zhang J
    Diagn Interv Imaging; 2024 May; 105(5):191-205. PubMed ID: 38272773
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Breast Multiparametric MRI for Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer: The BMMR2 Challenge.
    Li W; Partridge SC; Newitt DC; Steingrimsson J; Marques HS; Bolan PJ; Hirano M; Bearce BA; Kalpathy-Cramer J; Boss MA; Teng X; Zhang J; Cai J; Kontos D; Cohen EA; Mankowski WC; Liu M; Ha R; Pellicer-Valero OJ; Maier-Hein K; Rabinovici-Cohen S; Tlusty T; Ozery-Flato M; Parekh VS; Jacobs MA; Yan R; Sung K; Kazerouni AS; DiCarlo JC; Yankeelov TE; Chenevert TL; Hylton NM
    Radiol Imaging Cancer; 2024 Jan; 6(1):e230033. PubMed ID: 38180338
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Development of a multiparametric model for predicting the response to neoadjuvant chemotherapy in breast cancer.
    Qian F; Mao Y; Dong J; Xie F; Fang X; Zhang Q; Xia P; Han X; Lu N
    Transl Cancer Res; 2024 Feb; 13(2):558-568. PubMed ID: 38482410
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Pre- and Post-treatment Double-Sequential-Point Dynamic Radiomic Model in the Response Prediction of Gastric Cancer to Neoadjuvant Chemotherapy: 3-Year Survival Analysis.
    Wang Y; Tang L; Ying X; Li J; Shan F; Li S; Jia Y; Xue K; Miao R; Li Z; Li Z; Ji J
    Ann Surg Oncol; 2024 Feb; 31(2):774-782. PubMed ID: 37993745
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Machine learning radiomics of magnetic resonance imaging predicts recurrence-free survival after surgery and correlation of LncRNAs in patients with breast cancer: a multicenter cohort study.
    Yu Y; Ren W; He Z; Chen Y; Tan Y; Mao L; Ouyang W; Lu N; Ouyang J; Chen K; Li C; Zhang R; Wu Z; Su F; Wang Z; Hu Q; Xie C; Yao H
    Breast Cancer Res; 2023 Nov; 25(1):132. PubMed ID: 37915093
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Predicting response of hepatoblastoma primary lesions to neoadjuvant chemotherapy through contrast-enhanced computed tomography radiomics.
    Yang Y; Wang H; Si J; Zhang L; Ding H; Wang F; He L; Chen X
    J Cancer Res Clin Oncol; 2024 Apr; 150(5):223. PubMed ID: 38691204
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Prediction of Response to Preoperative Neoadjuvant Chemotherapy in Locally Advanced Cervical Cancer Using Multicenter CT-Based Radiomic Analysis.
    Tian X; Sun C; Liu Z; Li W; Duan H; Wang L; Fan H; Li M; Li P; Wang L; Liu P; Tian J; Chen C
    Front Oncol; 2020; 10():77. PubMed ID: 32117732
    [No Abstract]   [Full Text] [Related]  

  • 17. Predicting pathological complete response based on weakly and semi-supervised joint learning from breast cancer MRI.
    Hao X; Xu H; Zhao N; Yu T; Hamalainen T; Cong F
    Annu Int Conf IEEE Eng Med Biol Soc; 2023 Jul; 2023():1-4. PubMed ID: 38083773
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Radiomic features from multiparametric magnetic resonance imaging predict molecular subgroups of pediatric low-grade gliomas.
    Liu Z; Hong X; Wang L; Ma Z; Guan F; Wang W; Qiu Y; Zhang X; Duan W; Wang M; Sun C; Zhao Y; Duan J; Sun Q; Liu L; Ding L; Ji Y; Yan D; Liu X; Cheng J; Zhang Z; Li ZC; Yan J
    BMC Cancer; 2023 Sep; 23(1):848. PubMed ID: 37697238
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Integrated single-cell and bulk RNA sequencing analysis identifies a neoadjuvant chemotherapy-related gene signature for predicting survival and therapy in breast cancer.
    Zhang X; Feng R; Guo J; Pan L; Yao Y; Gao J
    BMC Med Genomics; 2023 Nov; 16(1):300. PubMed ID: 37996875
    [TBL] [Abstract][Full Text] [Related]  

  • 20. The prediction of pCR and chemosensitivity for breast cancer patients using DLG3, RADL and Pathomics signatures based on machine learning and deep learning.
    Jiang C; Zhang X; Qu T; Yang X; Xiu Y; Yu X; Zhang S; Qiao K; Meng H; Li X; Huang Y
    Transl Oncol; 2024 May; 46():101985. PubMed ID: 38805774
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 12.