165 related articles for article (PubMed ID: 37589902)
1. 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]
2. A nomogram based on pretreatment radiomics and dosiomics features for predicting overall survival associated with esophageal squamous cell cancer.
Kawahara D; Nishioka R; Murakami Y; Emoto Y; Iwashita K; Sasaki R
Eur J Surg Oncol; 2024 Jun; 50(7):108450. PubMed ID: 38843660
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. 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]
5. 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]
6. 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]
7. 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]
8. 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]
9. CT-based radiomics nomogram may predict local recurrence-free survival in esophageal cancer patients receiving definitive chemoradiation or radiotherapy: A multicenter study.
Gong J; Zhang W; Huang W; Liao Y; Yin Y; Shi M; Qin W; Zhao L
Radiother Oncol; 2022 Sep; 174():8-15. PubMed ID: 35750106
[TBL] [Abstract][Full Text] [Related]
10. 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]
11. 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]
12. Construction and validation of classification models for predicting the response to concurrent chemo-radiotherapy of patients with esophageal squamous cell carcinoma based on multi-omics data.
Li ZM; Liu W; Chen XL; Wu WZ; Xu XE; Chu MY; Yu SX; Li EM; Huang HC; Xu LY
Clin Res Hepatol Gastroenterol; 2024 Apr; 48(4):102318. PubMed ID: 38471582
[TBL] [Abstract][Full Text] [Related]
13. Dosiomics and radiomics improve the prediction of post-radiotherapy neutrophil-lymphocyte ratio in locally advanced non-small cell lung cancer.
Hou R; Xia W; Zhang C; Shao Y; Zhu X; Feng W; Zhang Q; Yu W; Fu X; Zhao J
Med Phys; 2024 Jan; 51(1):650-661. PubMed ID: 37963229
[TBL] [Abstract][Full Text] [Related]
14. 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]
15. Prediction of Locoregional Recurrence-Free Survival of Oesophageal Squamous Cell Carcinoma After Chemoradiotherapy Based on an Enhanced CT-Based Radiomics Model.
Kong J; Zhu S; Shi G; Liu Z; Zhang J; Ren J
Front Oncol; 2021; 11():739933. PubMed ID: 34631575
[TBL] [Abstract][Full Text] [Related]
16. Development and validation of a radiomics-based model to predict local progression-free survival after chemo-radiotherapy in patients with esophageal squamous cell cancer.
Luo HS; Chen YY; Huang WZ; Wu SX; Huang SF; Xu HY; Xue RL; Du ZS; Li XY; Lin LX; Huang HC
Radiat Oncol; 2021 Oct; 16(1):201. PubMed ID: 34641928
[TBL] [Abstract][Full Text] [Related]
17. Multi-view radiomics and dosiomics analysis with machine learning for predicting acute-phase weight loss in lung cancer patients treated with radiotherapy.
Lee SH; Han P; Hales RK; Voong KR; Noro K; Sugiyama S; Haller JW; McNutt TR; Lee J
Phys Med Biol; 2020 Sep; 65(19):195015. PubMed ID: 32235058
[TBL] [Abstract][Full Text] [Related]
18. A prediction model for degree of differentiation for resectable locally advanced esophageal squamous cell carcinoma based on CT images using radiomics and machine-learning.
Kawahara D; Murakami Y; Tani S; Nagata Y
Br J Radiol; 2021 Aug; 94(1124):20210525. PubMed ID: 34235955
[TBL] [Abstract][Full Text] [Related]
19. Multi-omics to predict acute radiation esophagitis in patients with lung cancer treated with intensity-modulated radiation therapy.
Zheng X; Guo W; Wang Y; Zhang J; Zhang Y; Cheng C; Teng X; Lam S; Zhou T; Ma Z; Liu R; Wu H; Ge H; Cai J; Li B
Eur J Med Res; 2023 Mar; 28(1):126. PubMed ID: 36935504
[TBL] [Abstract][Full Text] [Related]
20. 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]
[Next] [New Search]
