282 related articles for article (PubMed ID: 37223682)
41.
Xue XQ; Yu WJ; Shi X; Shao XL; Wang YT
Front Oncol; 2022; 12():911168. PubMed ID: 36003788
[TBL] [Abstract][Full Text] [Related]
42. Using Multi-phase CT Radiomics Features to Predict EGFR Mutation Status in Lung Adenocarcinoma Patients.
Zhang G; Man Q; Shang L; Zhang J; Cao Y; Li S; Qian R; Ren J; Pu H; Zhou J; Zhang Z; Kong W
Acad Radiol; 2024 Jan; ():. PubMed ID: 38290884
[TBL] [Abstract][Full Text] [Related]
43. Imbalanced Data Correction Based PET/CT Radiomics Model for Predicting Lymph Node Metastasis in Clinical Stage T1 Lung Adenocarcinoma.
Lv J; Chen X; Liu X; Du D; Lv W; Lu L; Wu H
Front Oncol; 2022; 12():788968. PubMed ID: 35155231
[TBL] [Abstract][Full Text] [Related]
44. 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]
45. Analysis of Cross-Combinations of Feature Selection and Machine-Learning Classification Methods Based on [
Gómez OV; Herraiz JL; Udías JM; Haug A; Papp L; Cioni D; Neri E
Cancers (Basel); 2022 Jun; 14(12):. PubMed ID: 35740588
[TBL] [Abstract][Full Text] [Related]
46. Prediction of HER2 Expression in Gastric Adenocarcinoma Based On Preoperative Noninvasive Multimodal
Qin L; Chen W; Ye Y; Yi H; Pang W; Long B; Wang Y; Ye T; Li L
Acad Radiol; 2024 Jan; ():. PubMed ID: 38302386
[TBL] [Abstract][Full Text] [Related]
47. Radiomics Analysis of PET and CT Components of
Wang X; Lu Z
Front Oncol; 2021; 11():638124. PubMed ID: 33928029
[TBL] [Abstract][Full Text] [Related]
48. Using contrast-enhanced CT and non-contrast-enhanced CT to predict EGFR mutation status in NSCLC patients-a radiomics nomogram analysis.
Yang X; Liu M; Ren Y; Chen H; Yu P; Wang S; Zhang R; Dai H; Wang C
Eur Radiol; 2022 Apr; 32(4):2693-2703. PubMed ID: 34807270
[TBL] [Abstract][Full Text] [Related]
49. Value of radiomics model based on multi-parametric magnetic resonance imaging in predicting epidermal growth factor receptor mutation status in patients with lung adenocarcinoma.
Wang Y; Wan Q; Xia X; Hu J; Liao Y; Wang P; Peng Y; Liu H; Li X
J Thorac Dis; 2021 Jun; 13(6):3497-3508. PubMed ID: 34277045
[TBL] [Abstract][Full Text] [Related]
50. A comparison of 18 F-FDG PET-based radiomics and deep learning in predicting regional lymph node metastasis in patients with resectable lung adenocarcinoma: a cross-scanner and temporal validation study.
Lue KH; Chen YH; Chu SC; Chang BS; Lin CB; Chen YC; Lin HH; Liu SH
Nucl Med Commun; 2023 Dec; 44(12):1094-1105. PubMed ID: 37728592
[TBL] [Abstract][Full Text] [Related]
51. Preoperative
Liu Q; Li J; Xin B; Sun Y; Wang X; Song S
Quant Imaging Med Surg; 2023 Mar; 13(3):1537-1549. PubMed ID: 36915308
[TBL] [Abstract][Full Text] [Related]
52. A subregion-based positron emission tomography/computed tomography (PET/CT) radiomics model for the classification of non-small cell lung cancer histopathological subtypes.
Shen H; Chen L; Liu K; Zhao K; Li J; Yu L; Ye H; Zhu W
Quant Imaging Med Surg; 2021 Jul; 11(7):2918-2932. PubMed ID: 34249623
[TBL] [Abstract][Full Text] [Related]
53. The Machine Learning Model for Distinguishing Pathological Subtypes of Non-Small Cell Lung Cancer.
Zhao H; Su Y; Wang M; Lyu Z; Xu P; Jiao Y; Zhang L; Han W; Tian L; Fu P
Front Oncol; 2022; 12():875761. PubMed ID: 35692759
[TBL] [Abstract][Full Text] [Related]
54. 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]
55. Evaluating Solid Lung Adenocarcinoma Anaplastic Lymphoma Kinase Gene Rearrangement Using Noninvasive Radiomics Biomarkers.
Ma DN; Gao XY; Dan YB; Zhang AN; Wang WJ; Yang G; Zhu HZ
Onco Targets Ther; 2020; 13():6927-6935. PubMed ID: 32764984
[TBL] [Abstract][Full Text] [Related]
56. Integrated CT Radiomics Features Could Enhance the Efficacy of
Zhou W; Huang Q; Wen J; Li M; Zhu Y; Liu Y; Dai Y; Guan Y; Zhou Z; Hua T
Front Oncol; 2021; 11():772703. PubMed ID: 34869011
[TBL] [Abstract][Full Text] [Related]
57. Predicting the Efficacy of SBRT for Lung Cancer with
Chen K; Hou L; Chen M; Li S; Shi Y; Raynor WY; Yang H
Life (Basel); 2023 Mar; 13(4):. PubMed ID: 37109413
[TBL] [Abstract][Full Text] [Related]
58. A Machine Learning Model Based on PET/CT Radiomics and Clinical Characteristics Predicts ALK Rearrangement Status in Lung Adenocarcinoma.
Chang C; Sun X; Wang G; Yu H; Zhao W; Ge Y; Duan S; Qian X; Wang R; Lei B; Wang L; Liu L; Ruan M; Yan H; Liu C; Chen J; Xie W
Front Oncol; 2021; 11():603882. PubMed ID: 33738250
[TBL] [Abstract][Full Text] [Related]
59. 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]
60. 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]
[Previous] [Next] [New Search]