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Cited by in F6Publishing
For: Zhang B, Qi S, Pan X, Li C, Yao Y, Qian W, Guan Y. Deep CNN Model Using CT Radiomics Feature Mapping Recognizes EGFR Gene Mutation Status of Lung Adenocarcinoma. Front Oncol 2020;10:598721. [PMID: 33643902 DOI: 10.3389/fonc.2020.598721] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
Number Citing Articles
1 Silva F, Pereira T, Neves I, Morgado J, Freitas C, Malafaia M, Sousa J, Fonseca J, Negrão E, Flor de Lima B, Correia da Silva M, Madureira AJ, Ramos I, Costa JL, Hespanhol V, Cunha A, Oliveira HP. Towards Machine Learning-Aided Lung Cancer Clinical Routines: Approaches and Open Challenges. JPM 2022;12:480. [DOI: 10.3390/jpm12030480] [Reference Citation Analysis]
2 Yoon JH, Sun SH, Xiao M, Yang H, Lu L, Li Y, Schwartz LH, Zhao B. Convolutional Neural Network Addresses the Confounding Impact of CT Reconstruction Kernels on Radiomics Studies. Tomography 2021;7:877-92. [PMID: 34941646 DOI: 10.3390/tomography7040074] [Reference Citation Analysis]
3 Yang L, Xu P, Li M, Wang M, Peng M, Zhang Y, Wu T, Chu W, Wang K, Meng H, Zhang L. PET/CT Radiomic Features: A Potential Biomarker for EGFR Mutation Status and Survival Outcome Prediction in NSCLC Patients Treated With TKIs. Front Oncol 2022;12:894323. [DOI: 10.3389/fonc.2022.894323] [Reference Citation Analysis]
4 Huang W, Wang J, Wang H, Zhang Y, Zhao F, Li K, Su L, Kang F, Cao X. PET/CT Based EGFR Mutation Status Classification of NSCLC Using Deep Learning Features and Radiomics Features. Front Pharmacol 2022;13:898529. [PMID: 35571081 DOI: 10.3389/fphar.2022.898529] [Reference Citation Analysis]
5 Shen WQ, Guo Y, Ru WE, Li C, Zhang GC, Liao N, Du GQ. Using an Improved Residual Network to Identify PIK3CA Mutation Status in Breast Cancer on Ultrasound Image. Front Oncol 2022;12:850515. [PMID: 35719907 DOI: 10.3389/fonc.2022.850515] [Reference Citation Analysis]
6 Sengupta PP, Chandrashekhar Y. Imaging With Deep Learning: Sharpening the Cutting Edge. JACC Cardiovasc Imaging 2022;15:547-9. [PMID: 35272811 DOI: 10.1016/j.jcmg.2022.02.001] [Reference Citation Analysis]
7 Huang X, Sun Y, Tan M, Ma W, Gao P, Qi L, Lu J, Yang Y, Wang K, Chen W, Jin L, Kuang K, Duan S, Li M. Three-Dimensional Convolutional Neural Network-Based Prediction of Epidermal Growth Factor Receptor Expression Status in Patients With Non-Small Cell Lung Cancer. Front Oncol 2022;12:772770. [PMID: 35186727 DOI: 10.3389/fonc.2022.772770] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
8 Wang B, Hamal P, Meng X, Sun K, Yang Y, Sun Y, Sun X. Evaluation of the Radiomics Method for the Prediction of Atypical Adenomatous Hyperplasia in Patients With Subcentimeter Pulmonary Ground-Glass Nodules. Front Oncol 2021;11:698053. [PMID: 34422651 DOI: 10.3389/fonc.2021.698053] [Reference Citation Analysis]
9 Wang C, Xu X, Shao J, Zhou K, Zhao K, He Y, Li J, Guo J, Yi Z, Li W. Deep Learning to Predict EGFR Mutation and PD-L1 Expression Status in Non-Small-Cell Lung Cancer on Computed Tomography Images. J Oncol 2021;2021:5499385. [PMID: 35003258 DOI: 10.1155/2021/5499385] [Reference Citation Analysis]
10 Gui D, Song Q, Song B, Li H, Wang M, Min X, Li A. AIR-Net: A novel multi-task learning method with auxiliary image reconstruction for predicting EGFR mutation status on CT images of NSCLC patients. Comput Biol Med 2021;141:105157. [PMID: 34953355 DOI: 10.1016/j.compbiomed.2021.105157] [Reference Citation Analysis]
11 Ma JW, Li M. Molecular typing of lung adenocarcinoma with computed tomography and CT image-based radiomics: a narrative review of research progress and prospects. Transl Cancer Res 2021;10:4217-31. [PMID: 35116717 DOI: 10.21037/tcr-21-1037] [Reference Citation Analysis]
12 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, Yang Y. Machine Learning-Based Radiomics for Prediction of Epidermal Growth Factor Receptor Mutations in Lung Adenocarcinoma. Disease Markers 2022;2022:1-14. [DOI: 10.1155/2022/2056837] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
13 Chang R, Qi S, Zuo Y, Yue Y, Zhang X, Guan Y, Qian W. Predicting chemotherapy response in non-small-cell lung cancer via computed tomography radiomic features: Peritumoral, intratumoral, or combined? Front Oncol 2022;12:915835. [DOI: 10.3389/fonc.2022.915835] [Reference Citation Analysis]