193 related articles for article (PubMed ID: 37743537)
1. Contrast-enhanced CT-based radiomic analysis for determining the response to anti-programmed death-1 therapy in esophageal squamous cell carcinoma patients: A pilot study.
Yang Q; Huang H; Zhang G; Weng N; Ou Z; Sun M; Luo H; Zhou X; Gao Y; Wu X
Thorac Cancer; 2023 Nov; 14(33):3266-3274. PubMed ID: 37743537
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. 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]
4. 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]
5. 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]
6. Contrast-enhanced CT radiomics features to preoperatively identify differences between tumor and proximal tumor-adjacent and tumor-distant tissues of resectable esophageal squamous cell carcinoma.
Gao D; Tan BG; Chen XQ; Zhou C; Ou J; Guo WW; Zhou HY; Li R; Zhang XM; Chen TW
Cancer Imaging; 2024 Jan; 24(1):11. PubMed ID: 38243339
[TBL] [Abstract][Full Text] [Related]
7. Predicting response to immunotherapy plus chemotherapy in patients with esophageal squamous cell carcinoma using non-invasive Radiomic biomarkers.
Zhu Y; Yao W; Xu BC; Lei YY; Guo QK; Liu LZ; Li HJ; Xu M; Yan J; Chang DD; Feng ST; Zhu ZH
BMC Cancer; 2021 Oct; 21(1):1167. PubMed ID: 34717582
[TBL] [Abstract][Full Text] [Related]
8. Computed tomography-based radiomics analysis to predict lymphovascular invasion in esophageal squamous cell carcinoma.
Peng H; Yang Q; Xue T; Chen Q; Li M; Duan S; Cai B; Feng F
Br J Radiol; 2022 Feb; 95(1130):20210918. PubMed ID: 34908477
[TBL] [Abstract][Full Text] [Related]
9. Computed tomography-based radiomic analysis for prediction of treatment response to salvage chemoradiotherapy for locoregional lymph node recurrence after curative esophagectomy.
Gu L; Liu Y; Guo X; Tian Y; Ye H; Zhou S; Gao F
J Appl Clin Med Phys; 2021 Nov; 22(11):71-79. PubMed ID: 34614265
[TBL] [Abstract][Full Text] [Related]
10. 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]
11. Preoperative Prediction of Perineural Invasion in Oesophageal Squamous Cell Carcinoma Based on CT Radiomics Nomogram: A Multicenter Study.
Zhou H; Zhou J; Qin C; Tian Q; Zhou S; Qin Y; Wu Y; Shi J; Feng F
Acad Radiol; 2024 Apr; 31(4):1355-1366. PubMed ID: 37949700
[TBL] [Abstract][Full Text] [Related]
12. Preoperative CT radiomics of esophageal squamous cell carcinoma and lymph node to predict nodal disease with a high diagnostic capability.
Wu YP; Wu L; Ou J; Cao JM; Fu MY; Chen TW; Ouchi E; Hu J
Eur J Radiol; 2024 Jan; 170():111197. PubMed ID: 37992611
[TBL] [Abstract][Full Text] [Related]
13. Contrast-Enhanced CT-Based Radiomics Analysis in Predicting Lymphovascular Invasion in Esophageal Squamous Cell Carcinoma.
Li Y; Yu M; Wang G; Yang L; Ma C; Wang M; Yue M; Cong M; Ren J; Shi G
Front Oncol; 2021; 11():644165. PubMed ID: 34055613
[TBL] [Abstract][Full Text] [Related]
14. The role of spleen radiomics model for predicting prognosis in esophageal squamous cell carcinoma patients receiving definitive radiotherapy.
Guo L; Liu A; Geng X; Zhao Z; Nie Y; Wang L; Liu D; Li Y; Li Y; Li D; Wang Q; Li Z; Liu X; Li M
Thorac Cancer; 2024 Apr; 15(12):947-964. PubMed ID: 38480505
[TBL] [Abstract][Full Text] [Related]
15. Establishing a survival prediction model for esophageal squamous cell carcinoma based on CT and histopathological images.
Wang J; Wu LL; Zhang Y; Ma G; Lu Y
Phys Med Biol; 2021 Jul; 66(14):. PubMed ID: 34192686
[TBL] [Abstract][Full Text] [Related]
16. A radiomics nomogram based on contrast-enhanced CT for preoperative prediction of Lymphovascular invasion in esophageal squamous cell carcinoma.
Wang Y; Bai G; Huang W; Zhang H; Chen W
Front Oncol; 2023; 13():1208756. PubMed ID: 37465108
[TBL] [Abstract][Full Text] [Related]
17. 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]
18. Radiomics Analysis of Lymph Nodes with Esophageal Squamous Cell Carcinoma Based on Deep Learning.
Chen L; Ouyang Y; Liu S; Lin J; Chen C; Zheng C; Lin J; Hu Z; Qiu M
J Oncol; 2022; 2022():8534262. PubMed ID: 36147442
[TBL] [Abstract][Full Text] [Related]
19. Predicting programmed death-ligand 1 expression level in non-small cell lung cancer using a combination of peritumoral and intratumoral radiomic features on computed tomography.
Shiinoki T; Fujimoto K; Kawazoe Y; Yuasa Y; Kajima M; Manabe Y; Ono T; Hirano T; Matsunaga K; Tanaka H
Biomed Phys Eng Express; 2022 Feb; 8(2):. PubMed ID: 35051908
[TBL] [Abstract][Full Text] [Related]
20. Radiomics study for predicting the expression of PD-L1 in non-small cell lung cancer based on CT images and clinicopathologic features.
Sun Z; Hu S; Ge Y; Wang J; Duan S; Song J; Hu C; Li Y
J Xray Sci Technol; 2020; 28(3):449-459. PubMed ID: 32176676
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
