115 related articles for article (PubMed ID: 38591974)
1. Predicting Invasiveness of Lung Adenocarcinoma at Chest CT with Deep Learning Ternary Classification Models.
Pan Z; Hu G; Zhu Z; Tan W; Han W; Zhou Z; Song W; Yu Y; Song L; Jin Z
Radiology; 2024 Apr; 311(1):e232057. PubMed ID: 38591974
[TBL] [Abstract][Full Text] [Related]
2. Lung-PNet: An Automated Deep Learning Model for the Diagnosis of Invasive Adenocarcinoma in Pure Ground-Glass Nodules on Chest CT.
Qi K; Wang K; Wang X; Zhang YD; Lin G; Zhang X; Liu H; Huang W; Wu J; Zhao K; Liu J; Li J; Zhang X
AJR Am J Roentgenol; 2024 Jan; 222(1):e2329674. PubMed ID: 37493322
[No Abstract] [Full Text] [Related]
3. A nomogram for predicting invasiveness of lung adenocarcinoma manifesting as pure ground-glass nodules: incorporating subjective CT signs and histogram parameters based on artificial intelligence.
Gao R; Gao Y; Zhang J; Zhu C; Zhang Y; Yan C
J Cancer Res Clin Oncol; 2023 Nov; 149(17):15323-15333. PubMed ID: 37624396
[TBL] [Abstract][Full Text] [Related]
4. Diagnosis of Invasive Lung Adenocarcinoma Based on Chest CT Radiomic Features of Part-Solid Pulmonary Nodules: A Multicenter Study.
Wu G; Woodruff HC; Shen J; Refaee T; Sanduleanu S; Ibrahim A; Leijenaar RTH; Wang R; Xiong J; Bian J; Wu J; Lambin P
Radiology; 2020 Nov; 297(2):451-458. PubMed ID: 32840472
[TBL] [Abstract][Full Text] [Related]
5. Radiomic signature based on CT imaging to distinguish invasive adenocarcinoma from minimally invasive adenocarcinoma in pure ground-glass nodules with pleural contact.
Jiang Y; Che S; Ma S; Liu X; Guo Y; Liu A; Li G; Li Z
Cancer Imaging; 2021 Jan; 21(1):1. PubMed ID: 33407884
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. Preoperative CT-based Deep Learning Model for Predicting Disease-Free Survival in Patients with Lung Adenocarcinomas.
Kim H; Goo JM; Lee KH; Kim YT; Park CM
Radiology; 2020 Jul; 296(1):216-224. PubMed ID: 32396042
[TBL] [Abstract][Full Text] [Related]
8. Radiomics for lung adenocarcinoma manifesting as pure ground-glass nodules: invasive prediction.
Sun Y; Li C; Jin L; Gao P; Zhao W; Ma W; Tan M; Wu W; Duan S; Shan Y; Li M
Eur Radiol; 2020 Jul; 30(7):3650-3659. PubMed ID: 32162003
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. Feature-shared adaptive-boost deep learning for invasiveness classification of pulmonary subsolid nodules in CT images.
Wang J; Chen X; Lu H; Zhang L; Pan J; Bao Y; Su J; Qian D
Med Phys; 2020 Apr; 47(4):1738-1749. PubMed ID: 32020649
[TBL] [Abstract][Full Text] [Related]
11. CT Characteristics for Predicting Invasiveness in Pulmonary Pure Ground-Glass Nodules.
Chu ZG; Li WJ; Fu BJ; Lv FJ
AJR Am J Roentgenol; 2020 Aug; 215(2):351-358. PubMed ID: 32348187
[No Abstract] [Full Text] [Related]
12. A radiomics study to predict invasive pulmonary adenocarcinoma appearing as pure ground-glass nodules.
Cai J; Liu H; Yuan H; Wu Y; Xu Q; Lv Y; Li J; Fu J; Ye J
Clin Radiol; 2021 Feb; 76(2):143-151. PubMed ID: 33187676
[TBL] [Abstract][Full Text] [Related]
13. Predicting benign, preinvasive, and invasive lung nodules on computed tomography scans using machine learning.
Ashraf SF; Yin K; Meng CX; Wang Q; Wang Q; Pu J; Dhupar R
J Thorac Cardiovasc Surg; 2022 Apr; 163(4):1496-1505.e10. PubMed ID: 33726909
[TBL] [Abstract][Full Text] [Related]
14. Hybrid Clinical-Radiomics Model for Precisely Predicting the Invasiveness of Lung Adenocarcinoma Manifesting as Pure Ground-Glass Nodule.
Song L; Xing T; Zhu Z; Han W; Fan G; Li J; Du H; Song W; Jin Z; Zhang G
Acad Radiol; 2021 Sep; 28(9):e267-e277. PubMed ID: 32534967
[TBL] [Abstract][Full Text] [Related]
15. CT quantitative parameters to predict the invasiveness of lung pure ground-glass nodules (pGGNs).
Han L; Zhang P; Wang Y; Gao Z; Wang H; Li X; Ye Z
Clin Radiol; 2018 May; 73(5):504.e1-504.e7. PubMed ID: 29397913
[TBL] [Abstract][Full Text] [Related]
16. Measuring pure ground-glass nodules on computed tomography: assessing agreement between a commercially available deep learning algorithm and radiologists' readings.
Zuo Z; Wang P; Zeng W; Qi W; Zhang W
Acta Radiol; 2023 Apr; 64(4):1422-1430. PubMed ID: 36317301
[TBL] [Abstract][Full Text] [Related]
17. 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]
18. Discriminating invasive adenocarcinoma among lung pure ground-glass nodules: a multi-parameter prediction model.
Hu F; Huang H; Jiang Y; Feng M; Wang H; Tang M; Zhou Y; Tan X; Liu Y; Xu C; Ding N; Bai C; Hu J; Yang D; Zhang Y
J Thorac Dis; 2021 Sep; 13(9):5383-5394. PubMed ID: 34659805
[TBL] [Abstract][Full Text] [Related]
19. Deep learning-based differentiation of invasive adenocarcinomas from preinvasive or minimally invasive lesions among pulmonary subsolid nodules.
Park S; Park G; Lee SM; Kim W; Park H; Jung K; Seo JB
Eur Radiol; 2021 Aug; 31(8):6239-6247. PubMed ID: 33555355
[TBL] [Abstract][Full Text] [Related]
20. Computed Tomography Findings for Predicting Invasiveness of Lung Adenocarcinomas Manifesting as Pure Ground-Glass Nodules.
Park J; Doo KW; Sung YE; Jung JI; Chang S
Can Assoc Radiol J; 2023 Feb; 74(1):137-146. PubMed ID: 35840350
[No Abstract] [Full Text] [Related]
[Next] [New Search]