180 related articles for article (PubMed ID: 34669994)
1. Deep learning predicts epidermal growth factor receptor mutation subtypes in lung adenocarcinoma.
Song J; Ding C; Huang Q; Luo T; Xu X; Chen Z; Li S
Med Phys; 2021 Dec; 48(12):7891-7899. PubMed ID: 34669994
[TBL] [Abstract][Full Text] [Related]
2. Multiparametric MRI-Based Radiomics Approaches for Preoperative Prediction of EGFR Mutation Status in Spinal Bone Metastases in Patients with Lung Adenocarcinoma.
Jiang X; Ren M; Shuang X; Yang H; Shi D; Lai Q; Dong Y
J Magn Reson Imaging; 2021 Aug; 54(2):497-507. PubMed ID: 33638577
[TBL] [Abstract][Full Text] [Related]
3. MRI-based radiomics analysis for predicting the EGFR mutation based on thoracic spinal metastases in lung adenocarcinoma patients.
Ren M; Yang H; Lai Q; Shi D; Liu G; Shuang X; Su J; Xie L; Dong Y; Jiang X
Med Phys; 2021 Sep; 48(9):5142-5151. PubMed ID: 34318502
[TBL] [Abstract][Full Text] [Related]
4. Computed Tomography-Based Radiomics Signature: A Potential Indicator of Epidermal Growth Factor Receptor Mutation in Pulmonary Adenocarcinoma Appearing as a Subsolid Nodule.
Yang X; Dong X; Wang J; Li W; Gu Z; Gao D; Zhong N; Guan Y
Oncologist; 2019 Nov; 24(11):e1156-e1164. PubMed ID: 30936378
[TBL] [Abstract][Full Text] [Related]
5. Distinguishing EGFR mutation molecular subtypes based on MRI radiomics features of lung adenocarcinoma brain metastases.
Xu J; Yang Y; Gao Z; Song T; Ma Y; Yu X; Shi C
Clin Neurol Neurosurg; 2024 May; 240():108258. PubMed ID: 38552362
[TBL] [Abstract][Full Text] [Related]
6. Combination of
Li S; Li Y; Zhao M; Wang P; Xin J
Korean J Radiol; 2022 Sep; 23(9):921-930. PubMed ID: 36047542
[TBL] [Abstract][Full Text] [Related]
7. Transfer learning-based PET/CT three-dimensional convolutional neural network fusion of image and clinical information for prediction of EGFR mutation in lung adenocarcinoma.
Shao X; Ge X; Gao J; Niu R; Shi Y; Shao X; Jiang Z; Li R; Wang Y
BMC Med Imaging; 2024 Mar; 24(1):54. PubMed ID: 38438844
[TBL] [Abstract][Full Text] [Related]
8. 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]
9. Predicting EGFR mutation status in lung adenocarcinoma presenting as ground-glass opacity: utilizing radiomics model in clinical translation.
Cheng B; Deng H; Zhao Y; Xiong J; Liang P; Li C; Liang H; Shi J; Li J; Xiong S; Lai T; Chen Z; Wu J; Qian T; Huan W; Ng MTA; He J; Liang W
Eur Radiol; 2022 Sep; 32(9):5869-5879. PubMed ID: 35348863
[TBL] [Abstract][Full Text] [Related]
10. Machine Learning-Based Radiomics for Prediction of Epidermal Growth Factor Receptor Mutations in Lung Adenocarcinoma.
Lu J; Ji X; Wang L; Jiang Y; Liu X; Ma Z; Ning Y; Dong J; Peng H; Sun F; Guo Z; Ji Y; Xing J; Lu Y; Lu D
Dis Markers; 2022; 2022():2056837. PubMed ID: 35578691
[TBL] [Abstract][Full Text] [Related]
11. Identifying EGFR mutations in lung adenocarcinoma by noninvasive imaging using radiomics features and random forest modeling.
Jia TY; Xiong JF; Li XY; Yu W; Xu ZY; Cai XW; Ma JC; Ren YC; Larsson R; Zhang J; Zhao J; Fu XL
Eur Radiol; 2019 Sep; 29(9):4742-4750. PubMed ID: 30778717
[TBL] [Abstract][Full Text] [Related]
12. A clinically practical radiomics-clinical combined model based on PET/CT data and nomogram predicts EGFR mutation in lung adenocarcinoma.
Chang C; Zhou S; Yu H; Zhao W; Ge Y; Duan S; Wang R; Qian X; Lei B; Wang L; Liu L; Ruan M; Yan H; Sun X; Xie W
Eur Radiol; 2021 Aug; 31(8):6259-6268. PubMed ID: 33544167
[TBL] [Abstract][Full Text] [Related]
13. Development and externally validate MRI-based nomogram to assess EGFR and T790M mutations in patients with metastatic lung adenocarcinoma.
Fan Y; Dong Y; Wang H; Wang H; Sun X; Wang X; Zhao P; Luo Y; Jiang X
Eur Radiol; 2022 Oct; 32(10):6739-6751. PubMed ID: 35729427
[TBL] [Abstract][Full Text] [Related]
14. MRI-Based Radiomics Nomogram as a Potential Biomarker to Predict the EGFR Mutations in Exon 19 and 21 Based on Thoracic Spinal Metastases in Lung Adenocarcinoma.
Cao R; Dong Y; Wang X; Ren M; Wang X; Zhao N; Yu T; Zhang L; Luo Y; Cui EN; Jiang X
Acad Radiol; 2022 Mar; 29(3):e9-e17. PubMed ID: 34332860
[TBL] [Abstract][Full Text] [Related]
15. Identifying epidermal growth factor receptor mutation status in patients with lung adenocarcinoma by three-dimensional convolutional neural networks.
Xiong JF; Jia TY; Li XY; Yu W; Xu ZY; Cai XW; Fu L; Zhang J; Qin BJ; Fu XL; Zhao J
Br J Radiol; 2018 Dec; 91(1092):20180334. PubMed ID: 30059241
[TBL] [Abstract][Full Text] [Related]
16. 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]
17. Comparison of the radiomics-based predictive models using machine learning and nomogram for epidermal growth factor receptor mutation status and subtypes in lung adenocarcinoma.
Kawazoe Y; Shiinoki T; Fujimoto K; Yuasa Y; Hirano T; Matsunaga K; Tanaka H
Phys Eng Sci Med; 2023 Mar; 46(1):395-403. PubMed ID: 36787023
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. Hybrid deep multi-task learning radiomics approach for predicting EGFR mutation status of non-small cell lung cancer in CT images.
Gong J; Fu F; Ma X; Wang T; Ma X; You C; Zhang Y; Peng W; Chen H; Gu Y
Phys Med Biol; 2023 Dec; 68(24):. PubMed ID: 37972417
[No Abstract] [Full Text] [Related]
20. 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]
[Next] [New Search]
