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Journal Abstract Search

176 related items for PubMed ID: 36006071

  • 1. Performance of Machine Learning and Texture Analysis for Predicting Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer with 3T MRI.
    Bellini D, Carbone I, Rengo M, Vicini S, Panvini N, Caruso D, Iannicelli E, Tombolini V, Laghi A.
    Tomography; 2022 Aug 19; 8(4):2059-2072. PubMed ID: 36006071
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  • 2. Texture analysis as imaging biomarker of tumoral response to neoadjuvant chemoradiotherapy in rectal cancer patients studied with 3-T magnetic resonance.
    De Cecco CN, Ganeshan B, Ciolina M, Rengo M, Meinel FG, Musio D, De Felice F, Raffetto N, Tombolini V, Laghi A.
    Invest Radiol; 2015 Apr 19; 50(4):239-45. PubMed ID: 25501017
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  • 4. MRI T2-weighted sequences-based texture analysis (TA) as a predictor of response to neoadjuvant chemo-radiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC).
    Crimì F, Capelli G, Spolverato G, Bao QR, Florio A, Milite Rossi S, Cecchin D, Albertoni L, Campi C, Pucciarelli S, Stramare R.
    Radiol Med; 2020 Dec 19; 125(12):1216-1224. PubMed ID: 32410063
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  • 6. Complete Response Evaluation of Locally Advanced Rectal Cancer to Neoadjuvant Chemoradiotherapy Using Textural Features Obtained from T2 Weighted Imaging and ADC Maps.
    Azamat S, Karaman Ş, Azamat IF, Ertaş G, Kulle CB, Keskin M, Sakin RND, Bakır B, Oral EN, Kartal MG.
    Curr Med Imaging; 2022 Dec 19; 18(10):1061-1069. PubMed ID: 35240976
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  • 8. MRI features and texture analysis for the early prediction of therapeutic response to neoadjuvant chemoradiotherapy and tumor recurrence of locally advanced rectal cancer.
    Park H, Kim KA, Jung JH, Rhie J, Choi SY.
    Eur Radiol; 2020 Aug 19; 30(8):4201-4211. PubMed ID: 32270317
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  • 9. 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 19; 44(9):2978-2987. PubMed ID: 31327039
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  • 11. Radiomics of locally advanced rectal cancer: machine learning-based prediction of response to neoadjuvant chemoradiotherapy using pre-treatment sagittal T2-weighted MRI.
    Yardimci AH, Kocak B, Sel I, Bulut H, Bektas CT, Cin M, Dursun N, Bektas H, Mermut O, Yardimci VH, Kilickesmez O.
    Jpn J Radiol; 2023 Jan 19; 41(1):71-82. PubMed ID: 35962933
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  • 12. Prediction of efficacy of neoadjuvant chemoradiotherapy for rectal cancer: the value of texture analysis of magnetic resonance images.
    Shu Z, Fang S, Ye Q, Mao D, Cao H, Pang P, Gong X.
    Abdom Radiol (NY); 2019 Nov 19; 44(11):3775-3784. PubMed ID: 30852633
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  • 13. Response to neoadjuvant chemoradiotherapy for locally advanced rectum cancer: Texture analysis of dynamic contrast-enhanced MRI.
    Zou HH, Yu J, Wei Y, Wu JF, Xu Q.
    J Magn Reson Imaging; 2019 Mar 19; 49(3):885-893. PubMed ID: 30079601
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  • 14. Machine learning for prediction of chemoradiation therapy response in rectal cancer using pre-treatment and mid-radiation multi-parametric MRI.
    Shi L, Zhang Y, Nie K, Sun X, Niu T, Yue N, Kwong T, Chang P, Chow D, Chen JH, Su MY.
    Magn Reson Imaging; 2019 Sep 19; 61():33-40. PubMed ID: 31059768
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  • 15. [A prediction model of pathological complete response in patients with locally advanced rectal cancer after PD-1 antibody combined with total neoadjuvant chemoradiotherapy based on MRI radiomics].
    Zhang XY, Zhu HT, Li XT, Li YJ, Li ZW, Wang WH, Wu AW, Sun YS, Zhang L.
    Zhonghua Wei Chang Wai Ke Za Zhi; 2022 Mar 25; 25(3):228-234. PubMed ID: 35340172
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  • 16. The value of diffusion kurtosis magnetic resonance imaging for assessing treatment response of neoadjuvant chemoradiotherapy in locally advanced rectal cancer.
    Yu J, Xu Q, Song JC, Li Y, Dai X, Huang DY, Zhang L, Li Y, Shi HB.
    Eur Radiol; 2017 May 25; 27(5):1848-1857. PubMed ID: 27631106
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  • 20. Predicting pathological complete response by comparing MRI-based radiomics pre- and postneoadjuvant radiotherapy for locally advanced rectal cancer.
    Li Y, Liu W, Pei Q, Zhao L, Güngör C, Zhu H, Song X, Li C, Zhou Z, Xu Y, Wang D, Tan F, Yang P, Pei H.
    Cancer Med; 2019 Dec 25; 8(17):7244-7252. PubMed ID: 31642204
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