269 related articles for article (PubMed ID: 32296645)
1. Comparison and Fusion of Deep Learning and Radiomics Features of Ground-Glass Nodules to Predict the Invasiveness Risk of Stage-I Lung Adenocarcinomas in CT Scan.
Xia X; Gong J; Hao W; Yang T; Lin Y; Wang S; Peng W
Front Oncol; 2020; 10():418. PubMed ID: 32296645
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. Computer-aided diagnosis of ground glass pulmonary nodule by fusing deep learning and radiomics features.
Hu X; Gong J; Zhou W; Li H; Wang S; Wei M; Peng W; Gu Y
Phys Med Biol; 2021 Mar; 66(6):065015. PubMed ID: 33596552
[TBL] [Abstract][Full Text] [Related]
4. Predicting the invasiveness of lung adenocarcinomas appearing as ground-glass nodule on CT scan using multi-task learning and deep radiomics.
Wang X; Li Q; Cai J; Wang W; Xu P; Zhang Y; Fang Q; Fu C; Fan L; Xiao Y; Liu S
Transl Lung Cancer Res; 2020 Aug; 9(4):1397-1406. PubMed ID: 32953512
[TBL] [Abstract][Full Text] [Related]
5. Fusion of CT images and clinical variables based on deep learning for predicting invasiveness risk of stage I lung adenocarcinoma.
Huang H; Zheng D; Chen H; Wang Y; Chen C; Xu L; Li G; Wang Y; He X; Li W
Med Phys; 2022 Oct; 49(10):6384-6394. PubMed ID: 35938604
[TBL] [Abstract][Full Text] [Related]
6. Marginal radiomics features as imaging biomarkers for pathological invasion in lung adenocarcinoma.
Cho HH; Lee G; Lee HY; Park H
Eur Radiol; 2020 May; 30(5):2984-2994. PubMed ID: 31965255
[TBL] [Abstract][Full Text] [Related]
7. Determining the invasiveness of ground-glass nodules using a 3D multi-task network.
Yu Y; Wang N; Huang N; Liu X; Zheng Y; Fu Y; Li X; Wu H; Xu J; Cheng J
Eur Radiol; 2021 Sep; 31(9):7162-7171. PubMed ID: 33665717
[TBL] [Abstract][Full Text] [Related]
8. Baseline whole-lung CT features deriving from deep learning and radiomics: prediction of benign and malignant pulmonary ground-glass nodules.
Huang W; Deng H; Li Z; Xiong Z; Zhou T; Ge Y; Zhang J; Jing W; Geng Y; Wang X; Tu W; Dong P; Liu S; Fan L
Front Oncol; 2023; 13():1255007. PubMed ID: 37664069
[TBL] [Abstract][Full Text] [Related]
9. Deep Learning-Based Stage-Wise Risk Stratification for Early Lung Adenocarcinoma in CT Images: A Multi-Center Study.
Gong J; Liu J; Li H; Zhu H; Wang T; Hu T; Li M; Xia X; Hu X; Peng W; Wang S; Tong T; Gu Y
Cancers (Basel); 2021 Jun; 13(13):. PubMed ID: 34209366
[TBL] [Abstract][Full Text] [Related]
10. Value of
Shao X; Niu R; Shao X; Jiang Z; Wang Y
EJNMMI Res; 2020 Jul; 10(1):80. PubMed ID: 32661639
[TBL] [Abstract][Full Text] [Related]
11. Computer-aided diagnosis of ground-glass opacity pulmonary nodules using radiomic features analysis.
Gong J; Liu J; Hao W; Nie S; Wang S; Peng W
Phys Med Biol; 2019 Jul; 64(13):135015. PubMed ID: 31167172
[TBL] [Abstract][Full Text] [Related]
12. Radiomic-Based Quantitative CT Analysis of Pure Ground-Glass Nodules to Predict the Invasiveness of Lung Adenocarcinoma.
Xu F; Zhu W; Shen Y; Wang J; Xu R; Qutesh C; Song L; Gan Y; Pu C; Hu H
Front Oncol; 2020; 10():872. PubMed ID: 32850301
[No Abstract] [Full Text] [Related]
13. Invasive Prediction of Ground Glass Nodule Based on Clinical Characteristics and Radiomics Feature.
Zheng H; Zhang H; Wang S; Xiao F; Liao M
Front Genet; 2021; 12():783391. PubMed ID: 35069686
[No Abstract] [Full Text] [Related]
14. Use of CT radiomics to differentiate minimally invasive adenocarcinomas and invasive adenocarcinomas presenting as pure ground-glass nodules larger than 10 mm.
Xiong Z; Jiang Y; Che S; Zhao W; Guo Y; Li G; Liu A; Li Z
Eur J Radiol; 2021 Aug; 141():109772. PubMed ID: 34022476
[TBL] [Abstract][Full Text] [Related]
15. Development, Validation, and Comparison of Image-Based, Clinical Feature-Based and Fusion Artificial Intelligence Diagnostic Models in Differentiating Benign and Malignant Pulmonary Ground-Glass Nodules.
Wang X; Gao M; Xie J; Deng Y; Tu W; Yang H; Liang S; Xu P; Zhang M; Lu Y; Fu C; Li Q; Fan L; Liu S
Front Oncol; 2022; 12():892890. PubMed ID: 35747810
[TBL] [Abstract][Full Text] [Related]
16. CT-based radiomics for differentiating invasive adenocarcinomas from indolent lung adenocarcinomas appearing as ground-glass nodules: Asystematic review.
Shi L; Zhao J; Peng X; Wang Y; Liu L; Sheng M
Eur J Radiol; 2021 Nov; 144():109956. PubMed ID: 34563797
[TBL] [Abstract][Full Text] [Related]
17. A semiautomated radiomics model based on multimodal dual-layer spectral CT for preoperative discrimination of the invasiveness of pulmonary ground-glass nodules.
Wang Y; Chen H; Chen Y; Zhong Z; Huang H; Sun P; Zhang X; Wan Y; Li L; Ye T; Pan F; Yang L
J Thorac Dis; 2023 May; 15(5):2505-2516. PubMed ID: 37324063
[TBL] [Abstract][Full Text] [Related]
18. Development and validation of a radiomics nomogram for identifying invasiveness of pulmonary adenocarcinomas appearing as subcentimeter ground-glass opacity nodules.
Zhao W; Xu Y; Yang Z; Sun Y; Li C; Jin L; Gao P; He W; Wang P; Shi H; Hua Y; Li M
Eur J Radiol; 2019 Mar; 112():161-168. PubMed ID: 30777206
[TBL] [Abstract][Full Text] [Related]
19. Changes in quantitative CT image features of ground-glass nodules in differentiating invasive pulmonary adenocarcinoma from benign and in situ lesions: histopathological comparisons.
Zhang YP; Heuvelmans MA; Zhang H; Oudkerk M; Zhang GX; Xie XQ
Clin Radiol; 2018 May; 73(5):504.e9-504.e16. PubMed ID: 29329732
[TBL] [Abstract][Full Text] [Related]
20. Consecutive Serial Non-Contrast CT Scan-Based Deep Learning Model Facilitates the Prediction of Tumor Invasiveness of Ground-Glass Nodules.
Xu Y; Li Y; Yin H; Tang W; Fan G
Front Oncol; 2021; 11():725599. PubMed ID: 34568054
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
