182 related articles for article (PubMed ID: 38438844)
21. Detailed identification of epidermal growth factor receptor mutations in lung adenocarcinoma: Combining radiomics with machine learning.
Li S; Luo T; Ding C; Huang Q; Guan Z; Zhang H
Med Phys; 2020 Aug; 47(8):3458-3466. PubMed ID: 32416013
[TBL] [Abstract][Full Text] [Related]
22. [Semiquantitative parameters of
Liao XH; Wang RF; Liu M; Chen XQ; Xiong Y; Nong L; Yin L; Zhang BY; DU YJ
Beijing Da Xue Xue Bao Yi Xue Ban; 2020 Oct; 53(2):246-254. PubMed ID: 33879893
[TBL] [Abstract][Full Text] [Related]
23. Radiogenomic Models Using Machine Learning Techniques to Predict EGFR Mutations in Non-Small Cell Lung Cancer.
Nair JKR; Saeed UA; McDougall CC; Sabri A; Kovacina B; Raidu BVS; Khokhar RA; Probst S; Hirsh V; Chankowsky J; Van Kempen LC; Taylor J
Can Assoc Radiol J; 2021 Feb; 72(1):109-119. PubMed ID: 32063026
[TBL] [Abstract][Full Text] [Related]
24. A novel radiomic nomogram for predicting epidermal growth factor receptor mutation in peripheral lung adenocarcinoma.
Lu X; Li M; Zhang H; Hua S; Meng F; Yang H; Li X; Cao D
Phys Med Biol; 2020 Mar; 65(5):055012. PubMed ID: 31978901
[TBL] [Abstract][Full Text] [Related]
25. Prediction model based on 18F-FDG PET/CT radiomic features and clinical factors of EGFR mutations in lung adenocarcinoma.
Zhao HY; Su YX; Zhang LH; Fu P
Neoplasma; 2022 Jan; 69(1):233-241. PubMed ID: 34779641
[TBL] [Abstract][Full Text] [Related]
26. Computed Tomography-derived intratumoral and peritumoral radiomics in predicting EGFR mutation in lung adenocarcinoma.
Shang Y; Chen W; Li G; Huang Y; Wang Y; Kui X; Li M; Zheng H; Zhao W; Liu J
Radiol Med; 2023 Dec; 128(12):1483-1496. PubMed ID: 37749461
[TBL] [Abstract][Full Text] [Related]
27. Efficient 18F-fluorodeoxyglucose positron emission tomography/computed tomography-based machine learning model for predicting epidermal growth factor receptor mutations in non-small cell lung cancer.
Ruan D; Fang J; Teng X
Q J Nucl Med Mol Imaging; 2024 Mar; 68(1):70-83. PubMed ID: 35420272
[TBL] [Abstract][Full Text] [Related]
28. [Evaluate the value of (18)F-FDG PET-CT imaging in predicting the mutationsin epidermal growth factor receptor in lung adenocarcinoma].
Ding CY; Yang WP; Guo Z; Sun J; Li YY; Li TR
Zhonghua Zhong Liu Za Zhi; 2017 Jul; 39(7):528-531. PubMed ID: 28728300
[No Abstract] [Full Text] [Related]
29. Dual-energy spectral CT characteristics in surgically resected lung adenocarcinoma: comparison between Kirsten rat sarcoma viral oncogene mutations and epidermal growth factor receptor mutations.
Li M; Zhang L; Tang W; Duan JC; Jin YJ; Qi LL; Wu N
Cancer Imaging; 2019 Nov; 19(1):77. PubMed ID: 31783917
[TBL] [Abstract][Full Text] [Related]
30. Deep CNN Model Using CT Radiomics Feature Mapping Recognizes EGFR Gene Mutation Status of Lung Adenocarcinoma.
Zhang B; Qi S; Pan X; Li C; Yao Y; Qian W; Guan Y
Front Oncol; 2020; 10():598721. PubMed ID: 33643902
[TBL] [Abstract][Full Text] [Related]
31. An [
Meng N; Feng P; Yu X; Wu Y; Fu F; Li Z; Luo Y; Tan H; Yuan J; Yang Y; Wang Z; Wang M
Eur Radiol; 2024 Jan; 34(1):318-329. PubMed ID: 37530809
[TBL] [Abstract][Full Text] [Related]
32. CT radiomics-based prediction of anaplastic lymphoma kinase and epidermal growth factor receptor mutations in lung adenocarcinoma.
Choe J; Lee SM; Kim W; Do KH; Kim S; Choi S; Seo JB
Eur J Radiol; 2021 Jun; 139():109710. PubMed ID: 33862316
[TBL] [Abstract][Full Text] [Related]
33. A deep residual learning network for predicting lung adenocarcinoma manifesting as ground-glass nodule on CT images.
Gong J; Liu J; Hao W; Nie S; Zheng B; Wang S; Peng W
Eur Radiol; 2020 Apr; 30(4):1847-1855. PubMed ID: 31811427
[TBL] [Abstract][Full Text] [Related]
34. [Application of radiomics captured from CT to predict the EGFR mutation status and TKIs therapeutic sensitivity of advanced lung adenocarcinoma].
Yang CS; Chen WD; Gong GZ; Li ZJ; Qiu QT; Yin Y
Zhonghua Zhong Liu Za Zhi; 2019 Apr; 41(4):282-287. PubMed ID: 31014053
[No Abstract] [Full Text] [Related]
35. Correlation between EGFR mutation status and F
Zhu L; Yin G; Chen W; Li X; Yu X; Zhu X; Jiang W; Jia C; Chen P; Zhang Y; Lu D; Yu L; Li X; Xu W
Thorac Cancer; 2019 Apr; 10(4):659-664. PubMed ID: 30776196
[TBL] [Abstract][Full Text] [Related]
36. Prediction of lung malignancy progression and survival with machine learning based on pre-treatment FDG-PET/CT.
Huang B; Sollee J; Luo YH; Reddy A; Zhong Z; Wu J; Mammarappallil J; Healey T; Cheng G; Azzoli C; Korogodsky D; Zhang P; Feng X; Li J; Yang L; Jiao Z; Bai HX
EBioMedicine; 2022 Aug; 82():104127. PubMed ID: 35810561
[TBL] [Abstract][Full Text] [Related]
37. Additional value of metabolic parameters to PET/CT-based radiomics nomogram in predicting lymphovascular invasion and outcome in lung adenocarcinoma.
Nie P; Yang G; Wang N; Yan L; Miao W; Duan Y; Wang Y; Gong A; Zhao Y; Wu J; Zhang C; Wang M; Cui J; Yu M; Li D; Sun Y; Wang Y; Wang Z
Eur J Nucl Med Mol Imaging; 2021 Jan; 48(1):217-230. PubMed ID: 32451603
[TBL] [Abstract][Full Text] [Related]
38. Applications of radiomics-based analysis pipeline for predicting epidermal growth factor receptor mutation status.
Liu Z; Zhang T; Lin L; Long F; Guo H; Han L
Biomed Eng Online; 2023 Feb; 22(1):17. PubMed ID: 36810090
[TBL] [Abstract][Full Text] [Related]
39. Accurate prediction of epidermal growth factor receptor mutation status in early-stage lung adenocarcinoma, using radiomics and clinical features.
Zhu H; Song Y; Huang Z; Zhang L; Chen Y; Tao G; She Y; Sun X; Yu H
Asia Pac J Clin Oncol; 2022 Dec; 18(6):586-594. PubMed ID: 35098682
[TBL] [Abstract][Full Text] [Related]
40. CT imaging-based histogram features for prediction of EGFR mutation status of bone metastases in patients with primary lung adenocarcinoma.
Shen TX; Liu L; Li WH; Fu P; Xu K; Jiang YQ; Pan F; Guo Y; Zhang MC
Cancer Imaging; 2019 Jun; 19(1):34. PubMed ID: 31174617
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
