These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

643 related articles for article (PubMed ID: 36355199)

  • 1. External validation and comparison of MR-based radiomics models for predicting pathological complete response in locally advanced rectal cancer: a two-centre, multi-vendor study.
    Wei Q; Chen Z; Tang Y; Chen W; Zhong L; Mao L; Hu S; Wu Y; Deng K; Yang W; Liu X
    Eur Radiol; 2023 Mar; 33(3):1906-1917. PubMed ID: 36355199
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Endorectal ultrasound radiomics in locally advanced rectal cancer patients: despeckling and radiotherapy response prediction using machine learning.
    Abbaspour S; Abdollahi H; Arabalibeik H; Barahman M; Arefpour AM; Fadavi P; Ay M; Mahdavi SR
    Abdom Radiol (NY); 2022 Nov; 47(11):3645-3659. PubMed ID: 35951085
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Radiomics analysis of multiparametric MRI for prediction of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer.
    Cui Y; Yang X; Shi Z; Yang Z; Du X; Zhao Z; Cheng X
    Eur Radiol; 2019 Mar; 29(3):1211-1220. PubMed ID: 30128616
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Multimodality radiomics prediction of radiotherapy-induced the early proctitis and cystitis in rectal cancer patients: a machine learning study.
    Abbaspour S; Barahman M; Abdollahi H; Arabalibeik H; Hajainfar G; Babaei M; Iraji H; Barzegartahamtan M; Ay MR; Mahdavi SR
    Biomed Phys Eng Express; 2023 Dec; 10(1):. PubMed ID: 37995359
    [No Abstract]   [Full Text] [Related]  

  • 5. Evaluating treatment response to neoadjuvant chemoradiotherapy in rectal cancer using various MRI-based radiomics models.
    Li Z; Ma X; Shen F; Lu H; Xia Y; Lu J
    BMC Med Imaging; 2021 Feb; 21(1):30. PubMed ID: 33593304
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Prognostic prediction value of the clinical-radiomics tumour-stroma ratio in locally advanced rectal cancer.
    Cai C; Hu T; Rong Z; Gong J; Tong T
    Eur J Radiol; 2024 Jan; 170():111254. PubMed ID: 38091662
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Prediction of the Ki-67 expression level in head and neck squamous cell carcinoma with machine learning-based multiparametric MRI radiomics: a multicenter study.
    Chen W; Lin G; Chen Y; Cheng F; Li X; Ding J; Zhong Y; Kong C; Chen M; Xia S; Lu C; Ji J
    BMC Cancer; 2024 Apr; 24(1):418. PubMed ID: 38580939
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Multiparametric MRI-based radiomic model for predicting lymph node metastasis after neoadjuvant chemoradiotherapy in locally advanced rectal cancer.
    Wei Q; Chen L; Hou X; Lin Y; Xie R; Yu X; Zhang H; Wen Z; Wu Y; Liu X; Chen W
    Insights Imaging; 2024 Jun; 15(1):163. PubMed ID: 38922456
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Comparison of radiomics-based machine-learning classifiers for the pretreatment prediction of pathologic complete response to neoadjuvant therapy in breast cancer.
    Li X; Li C; Wang H; Jiang L; Chen M
    PeerJ; 2024; 12():e17683. PubMed ID: 39026540
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Preoperative MR radiomics based on high-resolution T2-weighted images and amide proton transfer-weighted imaging for predicting lymph node metastasis in rectal adenocarcinoma.
    Wei Q; Yuan W; Jia Z; Chen J; Li L; Yan Z; Liao Y; Mao L; Hu S; Liu X; Chen W
    Abdom Radiol (NY); 2023 Feb; 48(2):458-470. PubMed ID: 36460837
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Treatment response prediction using MRI-based pre-, post-, and delta-radiomic features and machine learning algorithms in colorectal cancer.
    Shayesteh S; Nazari M; Salahshour A; Sandoughdaran S; Hajianfar G; Khateri M; Yaghobi Joybari A; Jozian F; Fatehi Feyzabad SH; Arabi H; Shiri I; Zaidi H
    Med Phys; 2021 Jul; 48(7):3691-3701. PubMed ID: 33894058
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Radiomics signature as a new biomarker for preoperative prediction of neoadjuvant chemoradiotherapy response in locally advanced rectal cancer.
    Zhang Z; Jiang X; Zhang R; Yu T; Liu S; Luo Y
    Diagn Interv Radiol; 2021 May; 27(3):308-314. PubMed ID: 34003118
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Radiomics Signature Based on Support Vector Machines for the Prediction of Pathological Complete Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer.
    Li C; Chen H; Zhang B; Fang Y; Sun W; Wu D; Su Z; Shen L; Wei Q
    Cancers (Basel); 2023 Oct; 15(21):. PubMed ID: 37958309
    [TBL] [Abstract][Full Text] [Related]  

  • 14. MRI radiomics signature to predict lymph node metastasis after neoadjuvant chemoradiation therapy in locally advanced rectal cancer.
    Fang Z; Pu H; Chen XL; Yuan Y; Zhang F; Li H
    Abdom Radiol (NY); 2023 Jul; 48(7):2270-2283. PubMed ID: 37085730
    [TBL] [Abstract][Full Text] [Related]  

  • 15. MRI-based delta-radiomics are predictive of pathological complete response after neoadjuvant chemoradiotherapy in locally advanced rectal cancer.
    Wan L; Peng W; Zou S; Ye F; Geng Y; Ouyang H; Zhao X; Zhang H
    Acad Radiol; 2021 Nov; 28 Suppl 1():S95-S104. PubMed ID: 33189550
    [TBL] [Abstract][Full Text] [Related]  

  • 16. MRI-based radiomics to predict response in locally advanced rectal cancer: comparison of manual and automatic segmentation on external validation in a multicentre study.
    Defeudis A; Mazzetti S; Panic J; Micilotta M; Vassallo L; Giannetto G; Gatti M; Faletti R; Cirillo S; Regge D; Giannini V
    Eur Radiol Exp; 2022 May; 6(1):19. PubMed ID: 35501512
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Develop and validate a radiomics space-time model to predict the pathological complete response in patients undergoing neoadjuvant treatment of rectal cancer: an artificial intelligence model study based on machine learning.
    Peng J; Wang W; Jin H; Qin X; Hou J; Yang Z; Shu Z
    BMC Cancer; 2023 Apr; 23(1):365. PubMed ID: 37085830
    [TBL] [Abstract][Full Text] [Related]  

  • 18. MRI-based pre-Radiomics and delta-Radiomics models accurately predict the post-treatment response of rectal adenocarcinoma to neoadjuvant chemoradiotherapy.
    Wang L; Wu X; Tian R; Ma H; Jiang Z; Zhao W; Cui G; Li M; Hu Q; Yu X; Xu W
    Front Oncol; 2023; 13():1133008. PubMed ID: 36925913
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Developing a prediction model based on MRI for pathological complete response after neoadjuvant chemoradiotherapy in locally advanced rectal cancer.
    Wan L; Zhang C; Zhao Q; Meng Y; Zou S; Yang Y; Liu Y; Jiang J; Ye F; Ouyang H; Zhao X; Zhang H
    Abdom Radiol (NY); 2019 Sep; 44(9):2978-2987. PubMed ID: 31327039
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Machine learning-based multiparametric MRI radiomics for predicting poor responders after neoadjuvant chemoradiotherapy in rectal Cancer patients.
    Wang J; Chen J; Zhou R; Gao Y; Li J
    BMC Cancer; 2022 Apr; 22(1):420. PubMed ID: 35439946
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 33.