BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

1129 related articles for article (PubMed ID: 28939744)

  • 21. Machine learning-based response assessment in patients with rectal cancer after neoadjuvant chemoradiotherapy: radiomics analysis for assessing tumor regression grade using T2-weighted magnetic resonance images.
    Lee YD; Kim HG; Seo M; Moon SK; Park SJ; You MW
    Int J Colorectal Dis; 2024 May; 39(1):78. PubMed ID: 38789861
    [TBL] [Abstract][Full Text] [Related]  

  • 22. A multiple-time-scale comparative study for the added value of magnetic resonance imaging-based radiomics in predicting pathological complete response after neoadjuvant chemoradiotherapy in locally advanced rectal cancer.
    Peng W; Wan L; Wang S; Zou S; Zhao X; Zhang H
    Front Oncol; 2023; 13():1234619. PubMed ID: 37664046
    [TBL] [Abstract][Full Text] [Related]  

  • 23. Pre-treatment ADC image-based random forest classifier for identifying resistant rectal adenocarcinoma to neoadjuvant chemoradiotherapy.
    Yang C; Jiang ZK; Liu LH; Zeng MS
    Int J Colorectal Dis; 2020 Jan; 35(1):101-107. PubMed ID: 31786652
    [TBL] [Abstract][Full Text] [Related]  

  • 24. Local tuning of radiomics-based model for predicting pathological response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer.
    Tang B; Lenkowicz J; Peng Q; Boldrini L; Hou Q; Dinapoli N; Valentini V; Diao P; Yin G; Orlandini LC
    BMC Med Imaging; 2022 Mar; 22(1):44. PubMed ID: 35287607
    [TBL] [Abstract][Full Text] [Related]  

  • 25. Predicting Rectal Cancer Response to Neoadjuvant Chemoradiotherapy Using Deep Learning of Diffusion Kurtosis MRI.
    Zhang XY; Wang L; Zhu HT; Li ZW; Ye M; Li XT; Shi YJ; Zhu HC; Sun YS
    Radiology; 2020 Jul; 296(1):56-64. PubMed ID: 32315264
    [TBL] [Abstract][Full Text] [Related]  

  • 26. Magnetic Resonance, Vendor-independent, Intensity Histogram Analysis Predicting Pathologic Complete Response After Radiochemotherapy of Rectal Cancer.
    Dinapoli N; Barbaro B; Gatta R; Chiloiro G; Casà C; Masciocchi C; Damiani A; Boldrini L; Gambacorta MA; Dezio M; Mattiucci GC; Balducci M; van Soest J; Dekker A; Lambin P; Fiorino C; Sini C; De Cobelli F; Di Muzio N; Gumina C; Passoni P; Manfredi R; Valentini V
    Int J Radiat Oncol Biol Phys; 2018 Nov; 102(4):765-774. PubMed ID: 29891200
    [TBL] [Abstract][Full Text] [Related]  

  • 27. Development and Validation of a Radiomics Model Based on Lymph-Node Regression Grading After Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer.
    Zhang S; Tang B; Yu M; He L; Zheng P; Yan C; Li J; Peng Q
    Int J Radiat Oncol Biol Phys; 2023 Nov; 117(4):821-833. PubMed ID: 37230433
    [TBL] [Abstract][Full Text] [Related]  

  • 28. Radiomics Approach Outperforms Diameter Criteria for Predicting Pathological Lateral Lymph Node Metastasis After Neoadjuvant (Chemo)Radiotherapy in Advanced Low Rectal Cancer.
    Nakanishi R; Akiyoshi T; Toda S; Murakami Y; Taguchi S; Oba K; Hanaoka Y; Nagasaki T; Yamaguchi T; Konishi T; Matoba S; Ueno M; Fukunaga Y; Kuroyanagi H
    Ann Surg Oncol; 2020 Oct; 27(11):4273-4283. PubMed ID: 32767224
    [TBL] [Abstract][Full Text] [Related]  

  • 29. Deep learning-based radiomic features for improving neoadjuvant chemoradiation response prediction in locally advanced rectal cancer.
    Fu J; Zhong X; Li N; Van Dams R; Lewis J; Sung K; Raldow AC; Jin J; Qi XS
    Phys Med Biol; 2020 Apr; 65(7):075001. PubMed ID: 32092710
    [TBL] [Abstract][Full Text] [Related]  

  • 30. MRI-based radiomics to predict neoadjuvant chemoradiotherapy outcomes in locally advanced rectal cancer: A multicenter study.
    Xiang Y; Li S; Wang H; Song M; Hu K; Wang F; Wang Z; Niu Z; Liu J; Cai Y; Li Y; Zhu X; Geng J; Zhang Y; Teng H; Wang W
    Clin Transl Radiat Oncol; 2023 Jan; 38():175-182. PubMed ID: 36471751
    [TBL] [Abstract][Full Text] [Related]  

  • 31. 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]  

  • 32. 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]  

  • 33. Association of the collagen signature with pathological complete response in rectal cancer patients.
    Jiang W; Wang S; Wan J; Zheng J; Dong X; Liu Z; Wang G; Xu S; Xiao W; Gao Y; Zhuo S; Yan J
    Cancer Sci; 2022 Jul; 113(7):2409-2424. PubMed ID: 35485874
    [TBL] [Abstract][Full Text] [Related]  

  • 34. 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; 41(1):71-82. PubMed ID: 35962933
    [TBL] [Abstract][Full Text] [Related]  

  • 35. Development and Validation of a Radiomics Nomogram Model for Predicting Postoperative Recurrence in Patients With Esophageal Squamous Cell Cancer Who Achieved pCR After Neoadjuvant Chemoradiotherapy Followed by Surgery.
    Qiu Q; Duan J; Deng H; Han Z; Gu J; Yue NJ; Yin Y
    Front Oncol; 2020; 10():1398. PubMed ID: 32850451
    [No Abstract]   [Full Text] [Related]  

  • 36. Predicting poor response to neoadjuvant chemoradiotherapy for locally advanced rectal cancer: Model constructed using pre-treatment MRI features of structured report template.
    Tang X; Jiang W; Li H; Xie F; Dong A; Liu L; Li L
    Radiother Oncol; 2020 Jul; 148():97-106. PubMed ID: 32339781
    [TBL] [Abstract][Full Text] [Related]  

  • 37. 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; 61():33-40. PubMed ID: 31059768
    [TBL] [Abstract][Full Text] [Related]  

  • 38. MRI-Based Radiomic Models Outperform Radiologists in Predicting Pathological Complete Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer.
    Wen L; Liu J; Hu P; Bi F; Liu S; Jian L; Zhu S; Nie S; Cao F; Lu Q; Yu X; Liu K
    Acad Radiol; 2023 Sep; 30 Suppl 1():S176-S184. PubMed ID: 36739228
    [TBL] [Abstract][Full Text] [Related]  

  • 39. Radiomics performs comparable to morphologic assessment by expert radiologists for prediction of response to neoadjuvant chemoradiotherapy on baseline staging MRI in rectal cancer.
    van Griethuysen JJM; Lambregts DMJ; Trebeschi S; Lahaye MJ; Bakers FCH; Vliegen RFA; Beets GL; Aerts HJWL; Beets-Tan RGH
    Abdom Radiol (NY); 2020 Mar; 45(3):632-643. PubMed ID: 31734709
    [TBL] [Abstract][Full Text] [Related]  

  • 40. Clinical utility of radiomics at baseline rectal MRI to predict complete response of rectal cancer after chemoradiation therapy.
    Petkovska I; Tixier F; Ortiz EJ; Golia Pernicka JS; Paroder V; Bates DD; Horvat N; Fuqua J; Schilsky J; Gollub MJ; Garcia-Aguilar J; Veeraraghavan H
    Abdom Radiol (NY); 2020 Nov; 45(11):3608-3617. PubMed ID: 32296896
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 57.