BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

425 related articles for article (PubMed ID: 32941954)

  • 1. Computed tomography-based deep-learning prediction of neoadjuvant chemoradiotherapy treatment response in esophageal squamous cell carcinoma.
    Hu Y; Xie C; Yang H; Ho JWK; Wen J; Han L; Lam KO; Wong IYH; Law SYK; Chiu KWH; Vardhanabhuti V; Fu J
    Radiother Oncol; 2021 Jan; 154():6-13. PubMed ID: 32941954
    [TBL] [Abstract][Full Text] [Related]  

  • 2. A machine learning approach using
    Qi WX; Li S; Xiao J; Li H; Chen J; Zhao S
    Front Immunol; 2024; 15():1351750. PubMed ID: 38352868
    [TBL] [Abstract][Full Text] [Related]  

  • 3. A nomogram based on pretreatment CT radiomics features for predicting complete response to chemoradiotherapy in patients with esophageal squamous cell cancer.
    Luo HS; Huang SF; Xu HY; Li XY; Wu SX; Wu DH
    Radiat Oncol; 2020 Oct; 15(1):249. PubMed ID: 33121507
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Machine learning models predict overall survival and progression free survival of non-surgical esophageal cancer patients with chemoradiotherapy based on CT image radiomics signatures.
    Cui Y; Li Z; Xiang M; Han D; Yin Y; Ma C
    Radiat Oncol; 2022 Dec; 17(1):212. PubMed ID: 36575480
    [TBL] [Abstract][Full Text] [Related]  

  • 5. A prediction model for pathological findings after neoadjuvant chemoradiotherapy for resectable locally advanced esophageal squamous cell carcinoma based on endoscopic images using deep learning.
    Kawahara D; Murakami Y; Tani S; Nagata Y
    Br J Radiol; 2022 Jul; 95(1135):20210934. PubMed ID: 35451338
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Assessment of Intratumoral and Peritumoral Computed Tomography Radiomics for Predicting Pathological Complete Response to Neoadjuvant Chemoradiation in Patients With Esophageal Squamous Cell Carcinoma.
    Hu Y; Xie C; Yang H; Ho JWK; Wen J; Han L; Chiu KWH; Fu J; Vardhanabhuti V
    JAMA Netw Open; 2020 Sep; 3(9):e2015927. PubMed ID: 32910196
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Using clinical and radiomic feature-based machine learning models to predict pathological complete response in patients with esophageal squamous cell carcinoma receiving neoadjuvant chemoradiation.
    Wang J; Zhu X; Zeng J; Liu C; Shen W; Sun X; Lin Q; Fang J; Chen Q; Ji Y
    Eur Radiol; 2023 Dec; 33(12):8554-8563. PubMed ID: 37439939
    [TBL] [Abstract][Full Text] [Related]  

  • 8. CT-based radiomic signatures for prediction of pathologic complete response in esophageal squamous cell carcinoma after neoadjuvant chemoradiotherapy.
    Yang Z; He B; Zhuang X; Gao X; Wang D; Li M; Lin Z; Luo R
    J Radiat Res; 2019 Jul; 60(4):538-545. PubMed ID: 31111948
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Radiomics and dosiomics for predicting complete response to definitive chemoradiotherapy patients with oesophageal squamous cell cancer using the hybrid institution model.
    Kawahara D; Murakami Y; Awane S; Emoto Y; Iwashita K; Kubota H; Sasaki R; Nagata Y
    Eur Radiol; 2024 Feb; 34(2):1200-1209. PubMed ID: 37589902
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Computed tomography-based deep-learning prediction of induction chemotherapy treatment response in locally advanced nasopharyngeal carcinoma.
    Yang Y; Wang M; Qiu K; Wang Y; Ma X
    Strahlenther Onkol; 2022 Feb; 198(2):183-193. PubMed ID: 34817635
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Predicting the Local Response of Esophageal Squamous Cell Carcinoma to Neoadjuvant Chemoradiotherapy by Radiomics with a Machine Learning Method Using
    Murakami Y; Kawahara D; Tani S; Kubo K; Katsuta T; Imano N; Takeuchi Y; Nishibuchi I; Saito A; Nagata Y
    Diagnostics (Basel); 2021 Jun; 11(6):. PubMed ID: 34200332
    [TBL] [Abstract][Full Text] [Related]  

  • 12. MR radiomics predicts pathological complete response of esophageal squamous cell carcinoma after neoadjuvant chemoradiotherapy: a multicenter study.
    Liu Y; Wang Y; Wang X; Xue L; Zhang H; Ma Z; Deng H; Yang Z; Sun X; Men Y; Ye F; Men K; Qin J; Bi N; Wang Q; Hui Z
    Cancer Imaging; 2024 Jan; 24(1):16. PubMed ID: 38263134
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Predicting response to CCRT for esophageal squamous carcinoma by a radiomics-clinical SHAP model.
    Cheng X; Zhang Y; Zhu M; Sun R; Liu L; Li X
    BMC Med Imaging; 2023 Oct; 23(1):145. PubMed ID: 37779188
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Development of a nomogram for the prediction of pathological complete response after neoadjuvant chemoradiotherapy in patients with esophageal squamous cell carcinoma.
    Chao YK; Chang HK; Tseng CK; Liu YH; Wen YW
    Dis Esophagus; 2017 Feb; 30(2):1-8. PubMed ID: 27868287
    [TBL] [Abstract][Full Text] [Related]  

  • 15. CT radiomics features of meso-esophageal fat in predicting overall survival of patients with locally advanced esophageal squamous cell carcinoma treated by definitive chemoradiotherapy.
    Yan S; Li FP; Jian L; Zhu HT; Zhao B; Li XT; Shi YJ; Sun YS
    BMC Cancer; 2023 May; 23(1):477. PubMed ID: 37231388
    [TBL] [Abstract][Full Text] [Related]  

  • 16. The MRI radiomics signature can predict the pathologic response to neoadjuvant chemotherapy in locally advanced esophageal squamous cell carcinoma.
    Lu S; Wang C; Liu Y; Chu F; Jia Z; Zhang H; Wang Z; Lu Y; Wang S; Yang G; Qu J
    Eur Radiol; 2024 Jan; 34(1):485-494. PubMed ID: 37540319
    [TBL] [Abstract][Full Text] [Related]  

  • 17. 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]  

  • 18. A novel CT-based radiomics model for predicting response and prognosis of chemoradiotherapy in esophageal squamous cell carcinoma.
    Kasai A; Miyoshi J; Sato Y; Okamoto K; Miyamoto H; Kawanaka T; Tonoiso C; Harada M; Goto M; Yoshida T; Haga A; Takayama T
    Sci Rep; 2024 Jan; 14(1):2039. PubMed ID: 38263395
    [TBL] [Abstract][Full Text] [Related]  

  • 19. A predictive model for treatment response in patients with locally advanced esophageal squamous cell carcinoma after concurrent chemoradiotherapy: based on SUVmean and NLR.
    Wang C; Zhao K; Hu S; Huang Y; Ma L; Song Y; Li M
    BMC Cancer; 2020 Jun; 20(1):544. PubMed ID: 32522277
    [TBL] [Abstract][Full Text] [Related]  

  • 20. CT radiomic features for predicting resectability of oesophageal squamous cell carcinoma as given by feature analysis: a case control study.
    Ou J; Li R; Zeng R; Wu CQ; Chen Y; Chen TW; Zhang XM; Wu L; Jiang Y; Yang JQ; Cao JM; Tang S; Tang MJ; Hu J
    Cancer Imaging; 2019 Oct; 19(1):66. PubMed ID: 31619297
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 22.