151 related articles for article (PubMed ID: 37598277)
1. Machine learning‑based prediction of survival prognosis in esophageal squamous cell carcinoma.
Zhang K; Ye B; Wu L; Ni S; Li Y; Wang Q; Zhang P; Wang D
Sci Rep; 2023 Aug; 13(1):13532. PubMed ID: 37598277
[TBL] [Abstract][Full Text] [Related]
2. Laboratory blood parameters and machine learning for the prognosis of esophageal squamous cell carcinoma.
Lu F; Yang L; Luo Z; He Q; Shangguan L; Cao M; Wu L
Front Oncol; 2024; 14():1367008. PubMed ID: 38638851
[TBL] [Abstract][Full Text] [Related]
3. Using a machine learning approach to identify key prognostic molecules for esophageal squamous cell carcinoma.
Li MX; Sun XM; Cheng WG; Ruan HJ; Liu K; Chen P; Xu HJ; Gao SG; Feng XS; Qi YJ
BMC Cancer; 2021 Aug; 21(1):906. PubMed ID: 34372798
[TBL] [Abstract][Full Text] [Related]
4. Development and validation of machine learning models for predicting prognosis and guiding individualized postoperative chemotherapy: A real-world study of distal cholangiocarcinoma.
Wang D; Pan B; Huang JC; Chen Q; Cui SP; Lang R; Lyu SC
Front Oncol; 2023; 13():1106029. PubMed ID: 37007095
[TBL] [Abstract][Full Text] [Related]
5. Serum levels of chemical elements in esophageal squamous cell carcinoma in Anyang, China: a case-control study based on machine learning methods.
Lin T; Liu T; Lin Y; Zhang C; Yan L; Chen Z; He Z; Wang J
BMJ Open; 2017 Sep; 7(9):e015443. PubMed ID: 28947442
[TBL] [Abstract][Full Text] [Related]
6. The Prognostic Significance of Nomogram-Based Pretreatment Inflammatory Indicators in Patients With Esophageal Squamous Cell Carcinoma Receiving Intensity-Modulated Radiotherapy.
Xu Z; Ke H; Zheng B; Lin C; Zhang Y; Wang L; Lin Y; Ye Y; Cai L; You M; Chen J; Xu Y
Cancer Control; 2023; 30():10732748231185025. PubMed ID: 37339928
[TBL] [Abstract][Full Text] [Related]
7. A novel staging system based on deep learning for overall survival in patients with esophageal squamous cell carcinoma.
Zhang H; Jiang X; Yu Q; Yu H; Xu C
J Cancer Res Clin Oncol; 2023 Sep; 149(11):8935-8944. PubMed ID: 37154930
[TBL] [Abstract][Full Text] [Related]
8. Development and validation of a new clinical staging system to predict survival for esophageal squamous cell carcinoma patients: Application of the nomogram.
Shao CY; Liu XL; Yao S; Li ZJ; Cong ZZ; Luo J; Dong GH; Yi J
Eur J Surg Oncol; 2021 Jun; 47(6):1473-1480. PubMed ID: 33349524
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. Raman spectroscopy and machine learning for the classification of esophageal squamous carcinoma.
Huang W; Shang Q; Xiao X; Zhang H; Gu Y; Yang L; Shi G; Yang Y; Hu Y; Yuan Y; Ji A; Chen L
Spectrochim Acta A Mol Biomol Spectrosc; 2022 Nov; 281():121654. PubMed ID: 35878494
[TBL] [Abstract][Full Text] [Related]
11. Increased prognostic value of clinical-reproductive model in Chinese female patients with esophageal squamous cell carcinoma.
Zhang DY; Ku JW; Zhao XK; Zhang HY; Song X; Wu HF; Fan ZM; Xu RH; You D; Wang R; Zhou RX; Wang LD
World J Gastroenterol; 2022 Apr; 28(13):1347-1361. PubMed ID: 35645543
[TBL] [Abstract][Full Text] [Related]
12. Integrative stemness characteristics associated with prognosis and the immune microenvironment in esophageal cancer.
Yi L; Huang P; Zou X; Guo L; Gu Y; Wen C; Wu G
Pharmacol Res; 2020 Nov; 161():105144. PubMed ID: 32810627
[TBL] [Abstract][Full Text] [Related]
13. 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]
14. A screened predictive model for esophageal squamous cell carcinoma based on salivary flora data.
Meng Y; Duan Q; Jiao K; Xue J
Math Biosci Eng; 2023 Sep; 20(10):18368-18385. PubMed ID: 38052562
[TBL] [Abstract][Full Text] [Related]
15. Development and Validation of a Risk Prediction Model for Esophageal Squamous Cell Carcinoma Using Cohort Studies.
Wang QL; Ness-Jensen E; Santoni G; Xie SH; Lagergren J
Am J Gastroenterol; 2021 Apr; 116(4):683-691. PubMed ID: 33982937
[TBL] [Abstract][Full Text] [Related]
16. Development and validation of MRI-based radiomics signatures models for prediction of disease-free survival and overall survival in patients with esophageal squamous cell carcinoma.
Chu F; Liu Y; Liu Q; Li W; Jia Z; Wang C; Wang Z; Lu S; Li P; Zhang Y; Liao Y; Xu M; Yao X; Wang S; Liu C; Zhang H; Wang S; Yan X; Kamel IR; Sun H; Yang G; Zhang Y; Qu J
Eur Radiol; 2022 Sep; 32(9):5930-5942. PubMed ID: 35384460
[TBL] [Abstract][Full Text] [Related]
17. Identification of Epithelial-Mesenchymal Transition- (EMT-) Related LncRNA for Prognostic Prediction and Risk Stratification in Esophageal Squamous Cell Carcinoma.
Wang P; Chen Y; Zheng Y; Fu Y; Ding Z
Dis Markers; 2021; 2021():5340240. PubMed ID: 34712369
[TBL] [Abstract][Full Text] [Related]
18. A Novel Clinical Six-Flavoprotein-Gene Signature Predicts Prognosis in Esophageal Squamous Cell Carcinoma.
Peng L; Guo JC; Long L; Pan F; Zhao JM; Xu LY; Li EM
Biomed Res Int; 2019; 2019():3869825. PubMed ID: 31815134
[TBL] [Abstract][Full Text] [Related]
19. Correlation of plasma miR-21 and miR-93 with radiotherapy and chemotherapy efficacy and prognosis in patients with esophageal squamous cell carcinoma.
Wang WT; Guo CQ; Cui GH; Zhao S
World J Gastroenterol; 2019 Oct; 25(37):5604-5618. PubMed ID: 31602161
[TBL] [Abstract][Full Text] [Related]
20. Tissue-based metabolomics reveals metabolic biomarkers and potential therapeutic targets for esophageal squamous cell carcinoma.
Chen Z; Gao Y; Huang X; Yao Y; Chen K; Zeng S; Mao W
J Pharm Biomed Anal; 2021 Apr; 197():113937. PubMed ID: 33609949
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]