285 related articles for article (PubMed ID: 34733879)
1. Pre-operative Prediction of Ki-67 Expression in Various Histological Subtypes of Lung Adenocarcinoma Based on CT Radiomic Features.
Huang Z; Lyu M; Ai Z; Chen Y; Liang Y; Xiang Z
Front Surg; 2021; 8():736737. PubMed ID: 34733879
[No Abstract] [Full Text] [Related]
2. CT-Imaging Based Analysis of Invasive Lung Adenocarcinoma Presenting as Ground Glass Nodules Using Peri- and Intra-nodular Radiomic Features.
Wu L; Gao C; Xiang P; Zheng S; Pang P; Xu M
Front Oncol; 2020; 10():838. PubMed ID: 32537436
[No Abstract] [Full Text] [Related]
3. Differentiation of predominant subtypes of lung adenocarcinoma using a quantitative radiomics approach on CT.
Park S; Lee SM; Noh HN; Hwang HJ; Kim S; Do KH; Seo JB
Eur Radiol; 2020 Sep; 30(9):4883-4892. PubMed ID: 32300970
[TBL] [Abstract][Full Text] [Related]
4. 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]
5. A comparative study to evaluate CT-based semantic and radiomic features in preoperative diagnosis of invasive pulmonary adenocarcinomas manifesting as subsolid nodules.
Wu YJ; Liu YC; Liao CY; Tang EK; Wu FZ
Sci Rep; 2021 Jan; 11(1):66. PubMed ID: 33462251
[TBL] [Abstract][Full Text] [Related]
6. Value of contrast-enhanced magnetic resonance imaging-T2WI-based radiomic features in distinguishing lung adenocarcinoma from lung squamous cell carcinoma with solid components >8 mm.
Yang M; Shi L; Huang T; Li G; Shao H; Shen Y; Zhu J; Ni B
J Thorac Dis; 2023 Feb; 15(2):635-648. PubMed ID: 36910079
[TBL] [Abstract][Full Text] [Related]
7. Value of
Hu Y; Zhao X; Zhang J; Han J; Dai M
Eur J Nucl Med Mol Imaging; 2021 Jan; 48(1):231-240. PubMed ID: 32588088
[TBL] [Abstract][Full Text] [Related]
8. CT Radiomics Combined With Clinicopathological Features to Predict Invasive Mucinous Adenocarcinoma in Patients With Lung Adenocarcinoma.
Zhang J; Hao L; Li M; Xu Q; Shi G
Technol Cancer Res Treat; 2023; 22():15330338231174306. PubMed ID: 37278046
[No Abstract] [Full Text] [Related]
9. Development and validation of a radiomic nomogram based on pretherapy dual-energy CT for distinguishing adenocarcinoma from squamous cell carcinoma of the lung.
Chen Z; Yi L; Peng Z; Zhou J; Zhang Z; Tao Y; Lin Z; He A; Jin M; Zuo M
Front Oncol; 2022; 12():949111. PubMed ID: 36505773
[TBL] [Abstract][Full Text] [Related]
10. Utility of CT radiomics for prediction of PD-L1 expression in advanced lung adenocarcinomas.
Yoon J; Suh YJ; Han K; Cho H; Lee HJ; Hur J; Choi BW
Thorac Cancer; 2020 Apr; 11(4):993-1004. PubMed ID: 32043309
[TBL] [Abstract][Full Text] [Related]
11. A novel radiomic nomogram for predicting epidermal growth factor receptor mutation in peripheral lung adenocarcinoma.
Lu X; Li M; Zhang H; Hua S; Meng F; Yang H; Li X; Cao D
Phys Med Biol; 2020 Mar; 65(5):055012. PubMed ID: 31978901
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. [Clinical value of a differentiation prediction model for invasive lung adenocarcinoma].
Shan WL; Kong D; Zhang H; Zhang JD; Duan SF; Guo LL
Zhonghua Zhong Liu Za Zhi; 2022 Jul; 44(7):767-775. PubMed ID: 35880343
[No Abstract] [Full Text] [Related]
14. A Combination of Radiomic Features, Imaging Characteristics, and Serum Tumor Biomarkers to Predict the Possibility of the High-Grade Subtypes of Lung Adenocarcinoma.
Liu Y; Chang Y; Zha X; Bao J; Wu Q; Dai H; Hu C
Acad Radiol; 2022 Dec; 29(12):1792-1801. PubMed ID: 35351366
[TBL] [Abstract][Full Text] [Related]
15. Predicting the Ki-67 proliferation index in pulmonary adenocarcinoma patients presenting with subsolid nodules: construction of a nomogram based on CT images.
Yan J; Xue X; Gao C; Guo Y; Wu L; Zhou C; Chen F; Xu M
Quant Imaging Med Surg; 2022 Jan; 12(1):642-652. PubMed ID: 34993108
[TBL] [Abstract][Full Text] [Related]
16. 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]
17. Predicting Ki-67 labeling index level in early-stage lung adenocarcinomas manifesting as ground-glass opacity nodules using intra-nodular and peri-nodular radiomic features.
Zhu M; Yang Z; Zhao W; Wang M; Shi W; Cheng Z; Ye C; Zhu Q; Liu L; Liang Z; Chen L
Cancer Med; 2022 Nov; 11(21):3982-3992. PubMed ID: 35332684
[TBL] [Abstract][Full Text] [Related]
18. Development of a novel nomogram-based model incorporating 3D radiomic signatures and lung CT radiological features for differentiating invasive adenocarcinoma from adenocarcinoma in situ and minimally invasive adenocarcinoma.
Ren H; Xiao Z; Ling C; Wang J; Wu S; Zeng Y; Li P
Quant Imaging Med Surg; 2023 Jan; 13(1):237-248. PubMed ID: 36620176
[TBL] [Abstract][Full Text] [Related]
19. Radiomics study for predicting the expression of PD-L1 in non-small cell lung cancer based on CT images and clinicopathologic features.
Sun Z; Hu S; Ge Y; Wang J; Duan S; Song J; Hu C; Li Y
J Xray Sci Technol; 2020; 28(3):449-459. PubMed ID: 32176676
[TBL] [Abstract][Full Text] [Related]
20. Differentiating Peripherally-Located Small Cell Lung Cancer From Non-small Cell Lung Cancer Using a CT Radiomic Approach.
Chen BT; Chen Z; Ye N; Mambetsariev I; Fricke J; Daniel E; Wang G; Wong CW; Rockne RC; Colen RR; Nasser MW; Batra SK; Holodny AI; Sampath S; Salgia R
Front Oncol; 2020; 10():593. PubMed ID: 32391274
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]