BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

120 related articles for article (PubMed ID: 37802671)

  • 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. 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]  

  • 23. Multi-modal radiomics model to predict treatment response to neoadjuvant chemotherapy for locally advanced rectal cancer.
    Li ZY; Wang XD; Li M; Liu XJ; Ye Z; Song B; Yuan F; Yuan Y; Xia CC; Zhang X; Li Q
    World J Gastroenterol; 2020 May; 26(19):2388-2402. PubMed ID: 32476800
    [TBL] [Abstract][Full Text] [Related]  

  • 24. 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]  

  • 25. 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]  

  • 26. MRI-based multiregional radiomics for predicting lymph nodes status and prognosis in patients with resectable rectal cancer.
    Li H; Chen XL; Liu H; Lu T; Li ZL
    Front Oncol; 2022; 12():1087882. PubMed ID: 36686763
    [TBL] [Abstract][Full Text] [Related]  

  • 27. 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]  

  • 28. Delta-radiomics signature predicts treatment outcomes after preoperative chemoradiotherapy and surgery in rectal cancer.
    Jeon SH; Song C; Chie EK; Kim B; Kim YH; Chang W; Lee YJ; Chung JH; Chung JB; Lee KW; Kang SB; Kim JS
    Radiat Oncol; 2019 Mar; 14(1):43. PubMed ID: 30866965
    [TBL] [Abstract][Full Text] [Related]  

  • 29. Development and validation of a
    Liu H; Cui Y; Chang C; Zhou Z; Zhang Y; Ma C; Yin Y; Wang R
    BMC Cancer; 2024 Jan; 24(1):150. PubMed ID: 38291351
    [TBL] [Abstract][Full Text] [Related]  

  • 30. T2WI-based MRI radiomics for the prediction of preoperative extranodal extension and prognosis in resectable rectal cancer.
    Li H; Chai L; Pu H; Yin LL; Li M; Zhang X; Liu YS; Pang MH; Lu T
    Insights Imaging; 2024 Feb; 15(1):57. PubMed ID: 38411722
    [TBL] [Abstract][Full Text] [Related]  

  • 31. 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]  

  • 32. 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]  

  • 33. 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]  

  • 34. Development of a joint prediction model based on both the radiomics and clinical factors for preoperative prediction of circumferential resection margin in middle-low rectal cancer using T2WI images.
    Ju Y; Zheng L; Qi W; Tian G; Lu Y
    Med Phys; 2024 Apr; 51(4):2563-2577. PubMed ID: 37987563
    [TBL] [Abstract][Full Text] [Related]  

  • 35. Radiomics of rectal cancer for predicting distant metastasis and overall survival.
    Li M; Zhu YZ; Zhang YC; Yue YF; Yu HP; Song B
    World J Gastroenterol; 2020 Sep; 26(33):5008-5021. PubMed ID: 32952346
    [TBL] [Abstract][Full Text] [Related]  

  • 36. A CT-based deep learning radiomics nomogram for predicting the response to neoadjuvant chemotherapy in patients with locally advanced gastric cancer: A multicenter cohort study.
    Cui Y; Zhang J; Li Z; Wei K; Lei Y; Ren J; Wu L; Shi Z; Meng X; Yang X; Gao X
    EClinicalMedicine; 2022 Apr; 46():101348. PubMed ID: 35340629
    [TBL] [Abstract][Full Text] [Related]  

  • 37. Prediction of locally advanced rectal cancer response to neoadjuvant chemoradiation therapy using volumetric multiparametric MRI-based radiomics.
    El Homsi M; Bane O; Fauveau V; Hectors S; Vietti Violi N; Sylla P; Ko HB; Cuevas J; Carbonell G; Nehlsen A; Vanguri R; Viswanath S; Jambawalikar S; Shaish H; Taouli B
    Abdom Radiol (NY); 2024 Mar; 49(3):791-800. PubMed ID: 38150143
    [TBL] [Abstract][Full Text] [Related]  

  • 38. Predicting outcomes for locally advanced rectal cancer treated with neoadjuvant chemoradiation with CT-based radiomics.
    Wang F; Tan BF; Poh SS; Siow TR; Lim FLWT; Yip CSP; Wang MLC; Nei W; Tan HQ
    Sci Rep; 2022 Apr; 12(1):6167. PubMed ID: 35418656
    [TBL] [Abstract][Full Text] [Related]  

  • 39. Predicting Treatment Response to Neoadjuvant Chemoradiotherapy in Rectal Mucinous Adenocarcinoma Using an MRI-Based Radiomics Nomogram.
    Li Z; Li S; Zang S; Ma X; Chen F; Xia Y; Chen L; Shen F; Lu Y; Lu J
    Front Oncol; 2021; 11():671636. PubMed ID: 34109121
    [TBL] [Abstract][Full Text] [Related]  

  • 40. 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]  

    [Previous]   [Next]    [New Search]
    of 6.