BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

359 related articles for article (PubMed ID: 33119899)

  • 1. Deep learning-based radiomics predicts response to chemotherapy in colorectal liver metastases.
    Wei J; Cheng J; Gu D; Chai F; Hong N; Wang Y; Tian J
    Med Phys; 2021 Jan; 48(1):513-522. PubMed ID: 33119899
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Radiomics Texture Analysis for the Identification of Colorectal Liver Metastases Sensitive to First-Line Oxaliplatin-Based Chemotherapy.
    Nakanishi R; Oki E; Hasuda H; Sano E; Miyashita Y; Sakai A; Koga N; Kuriyama N; Nonaka K; Fujimoto Y; Jogo T; Hokonohara K; Hu Q; Hisamatsu Y; Ando K; Kimura Y; Yoshizumi T; Mori M
    Ann Surg Oncol; 2021 Jun; 28(6):2975-2985. PubMed ID: 33454878
    [TBL] [Abstract][Full Text] [Related]  

  • 3. CT-based radiomics for the identification of colorectal cancer liver metastases sensitive to first-line irinotecan-based chemotherapy.
    Qi W; Yang J; Zheng L; Lu Y; Liu R; Ju Y; Niu T; Wang D
    Med Phys; 2023 May; 50(5):2705-2714. PubMed ID: 36841949
    [TBL] [Abstract][Full Text] [Related]  

  • 4. MRI-Based Radiomics Nomogram for Preoperatively Differentiating Intrahepatic Mass-Forming Cholangiocarcinoma From Resectable Colorectal Liver Metastases.
    Xu Y; Ye F; Li L; Yang Y; Ouyang J; Zhou Y; Wang S; Xie L; Zhou J; Zhao H; Zhao X
    Acad Radiol; 2023 Sep; 30(9):2010-2020. PubMed ID: 37414635
    [TBL] [Abstract][Full Text] [Related]  

  • 5. CT radiomics models are unable to predict new liver metastasis after successful thermal ablation of colorectal liver metastases.
    Taghavi M; Staal FC; Simões R; Hong EK; Lambregts DM; van der Heide UA; Beets-Tan RG; Maas M
    Acta Radiol; 2023 Jan; 64(1):5-12. PubMed ID: 34918955
    [TBL] [Abstract][Full Text] [Related]  

  • 6. A CT-based radiomics nomogram for predicting histopathologic growth patterns of colorectal liver metastases.
    Sun C; Liu X; Sun J; Dong L; Wei F; Bao C; Zhong J; Li Y
    J Cancer Res Clin Oncol; 2023 Sep; 149(12):9543-9555. PubMed ID: 37221440
    [TBL] [Abstract][Full Text] [Related]  

  • 7. A predictive model for early therapeutic efficacy of colorectal liver metastases using multimodal MRI data.
    Su X; Zhang H; Wang Y
    J Xray Sci Technol; 2023; 31(2):357-372. PubMed ID: 36591694
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Development and validation of a deep learning signature for predicting lymph node metastasis in lung adenocarcinoma: comparison with radiomics signature and clinical-semantic model.
    Ma X; Xia L; Chen J; Wan W; Zhou W
    Eur Radiol; 2023 Mar; 33(3):1949-1962. PubMed ID: 36169691
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Deep learning-assisted magnetic resonance imaging prediction of tumor response to chemotherapy in patients with colorectal liver metastases.
    Zhu HB; Xu D; Ye M; Sun L; Zhang XY; Li XT; Nie P; Xing BC; Sun YS
    Int J Cancer; 2021 Apr; 148(7):1717-1730. PubMed ID: 33284998
    [TBL] [Abstract][Full Text] [Related]  

  • 10. CT-Based Radiomics Analysis Before Thermal Ablation to Predict Local Tumor Progression for Colorectal Liver Metastases.
    Taghavi M; Staal F; Gomez Munoz F; Imani F; Meek DB; Simões R; Klompenhouwer LG; van der Heide UA; Beets-Tan RGH; Maas M
    Cardiovasc Intervent Radiol; 2021 Jun; 44(6):913-920. PubMed ID: 33506278
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Magnetic resonance imaging-radiomics evaluation of response to chemotherapy for synchronous liver metastasis of colorectal cancer.
    Ma YQ; Wen Y; Liang H; Zhong JG; Pang PP
    World J Gastroenterol; 2021 Oct; 27(38):6465-6475. PubMed ID: 34720535
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Deep learning radiomics based on contrast enhanced computed tomography predicts microvascular invasion and survival outcome in early stage hepatocellular carcinoma.
    Yang Y; Zhou Y; Zhou C; Ma X
    Eur J Surg Oncol; 2022 May; 48(5):1068-1077. PubMed ID: 34862094
    [TBL] [Abstract][Full Text] [Related]  

  • 13. A clinical-radiomics model incorporating T2-weighted and diffusion-weighted magnetic resonance images predicts the existence of lymphovascular invasion / perineural invasion in patients with colorectal cancer.
    Zhang K; Ren Y; Xu S; Lu W; Xie S; Qu J; Wang X; Shen B; Pang P; Cai X; Sun J
    Med Phys; 2021 Sep; 48(9):4872-4882. PubMed ID: 34042185
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Dual-Energy CT Deep Learning Radiomics to Predict Macrotrabecular-Massive Hepatocellular Carcinoma.
    Li M; Fan Y; You H; Li C; Luo M; Zhou J; Li A; Zhang L; Yu X; Deng W; Zhou J; Zhang D; Zhang Z; Chen H; Xiao Y; Huang B; Wang J
    Radiology; 2023 Aug; 308(2):e230255. PubMed ID: 37606573
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Development and validation of a deep learning radiomics model with clinical-radiological characteristics for the identification of occult peritoneal metastases in patients with pancreatic ductal adenocarcinoma.
    Shi S; Lin C; Zhou J; Wei L; Chen M; Zhang J; Cao K; Fan Y; Huang B; Luo Y; Feng ST
    Int J Surg; 2024 May; 110(5):2669-2678. PubMed ID: 38445459
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Deep learning model based on contrast-enhanced ultrasound for predicting early recurrence after thermal ablation of colorectal cancer liver metastasis.
    Zhao QX; He XL; Wang K; Cheng ZG; Han ZY; Liu FY; Yu XL; Hui Z; Yu J; Chao A; Liang P
    Eur Radiol; 2023 Mar; 33(3):1895-1905. PubMed ID: 36418624
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Advanced image analytics predicting clinical outcomes in patients with colorectal liver metastases: A systematic review of the literature.
    Wesdorp NJ; van Goor VJ; Kemna R; Jansma EP; van Waesberghe JHTM; Swijnenburg RJ; Punt CJA; Huiskens J; Kazemier G
    Surg Oncol; 2021 Sep; 38():101578. PubMed ID: 33866191
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Deep Learning Radiomics Nomogram Based on Enhanced CT to Predict the Response of Metastatic Lymph Nodes to Neoadjuvant Chemotherapy in Locally Advanced Gastric Cancer.
    Zhong H; Wang T; Hou M; Liu X; Tian Y; Cao S; Li Z; Han Z; Liu G; Sun Y; Meng C; Li Y; Jiang Y; Ji Q; Hao D; Liu Z; Zhou Y
    Ann Surg Oncol; 2024 Jan; 31(1):421-432. PubMed ID: 37925653
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Radiomics Response Signature for Identification of Metastatic Colorectal Cancer Sensitive to Therapies Targeting EGFR Pathway.
    Dercle L; Lu L; Schwartz LH; Qian M; Tejpar S; Eggleton P; Zhao B; Piessevaux H
    J Natl Cancer Inst; 2020 Sep; 112(9):902-912. PubMed ID: 32016387
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Deep radiomics-based fusion model for prediction of bevacizumab treatment response and outcome in patients with colorectal cancer liver metastases: a multicentre cohort study.
    Zhou S; Sun D; Mao W; Liu Y; Cen W; Ye L; Liang F; Xu J; Shi H; Ji Y; Wang L; Chang W
    EClinicalMedicine; 2023 Nov; 65():102271. PubMed ID: 37869523
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 18.