656 related articles for article (PubMed ID: 33738250)
1. 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]
2. Clinical, Conventional CT and Radiomic Feature-Based Machine Learning Models for Predicting ALK Rearrangement Status in Lung Adenocarcinoma Patients.
Song L; Zhu Z; Mao L; Li X; Han W; Du H; Wu H; Song W; Jin Z
Front Oncol; 2020; 10():369. PubMed ID: 32266148
[No Abstract] [Full Text] [Related]
3. Predicting anaplastic lymphoma kinase rearrangement status in patients with non-small cell lung cancer using a machine learning algorithm that combines clinical features and CT images.
Hao P; Deng BY; Huang CT; Xu J; Zhou F; Liu ZX; Zhou W; Xu YK
Front Oncol; 2022; 12():994285. PubMed ID: 36338735
[TBL] [Abstract][Full Text] [Related]
4. Machine learning based on clinico-biological features integrated
Ren C; Zhang J; Qi M; Zhang J; Zhang Y; Song S; Sun Y; Cheng J
Eur J Nucl Med Mol Imaging; 2021 May; 48(5):1538-1549. PubMed ID: 33057772
[TBL] [Abstract][Full Text] [Related]
5. A Machine Learning Model Based on PET/CT Radiomics and Clinical Characteristics Predicts Tumor Immune Profiles in Non-Small Cell Lung Cancer: A Retrospective Multicohort Study.
Tong H; Sun J; Fang J; Zhang M; Liu H; Xia R; Zhou W; Liu K; Chen X
Front Immunol; 2022; 13():859323. PubMed ID: 35572597
[TBL] [Abstract][Full Text] [Related]
6. Next-Generation Radiogenomics Sequencing for Prediction of EGFR and KRAS Mutation Status in NSCLC Patients Using Multimodal Imaging and Machine Learning Algorithms.
Shiri I; Maleki H; Hajianfar G; Abdollahi H; Ashrafinia S; Hatt M; Zaidi H; Oveisi M; Rahmim A
Mol Imaging Biol; 2020 Aug; 22(4):1132-1148. PubMed ID: 32185618
[TBL] [Abstract][Full Text] [Related]
7. Predicting N2 lymph node metastasis in presurgical stage I-II non-small cell lung cancer using multiview radiomics and deep learning method.
Zhang H; Liao M; Guo Q; Chen J; Wang S; Liu S; Xiao F
Med Phys; 2023 Apr; 50(4):2049-2060. PubMed ID: 36563341
[TBL] [Abstract][Full Text] [Related]
8. Identifying pathological subtypes of non-small-cell lung cancer by using the radiomic features of
Sha X; Gong G; Qiu Q; Duan J; Li D; Yin Y
Transl Cancer Res; 2019 Sep; 8(5):1741-1749. PubMed ID: 35116924
[TBL] [Abstract][Full Text] [Related]
9. CT radiomics-based prediction of anaplastic lymphoma kinase and epidermal growth factor receptor mutations in lung adenocarcinoma.
Choe J; Lee SM; Kim W; Do KH; Kim S; Choi S; Seo JB
Eur J Radiol; 2021 Jun; 139():109710. PubMed ID: 33862316
[TBL] [Abstract][Full Text] [Related]
10. Positron Emission Tomography/Magnetic Resonance Imaging Radiomics in Predicting Lung Adenocarcinoma and Squamous Cell Carcinoma.
Tang X; Liang J; Xiang B; Yuan C; Wang L; Zhu B; Ge X; Fang M; Ding Z
Front Oncol; 2022; 12():803824. PubMed ID: 35186742
[TBL] [Abstract][Full Text] [Related]
11. Predicting EGFR mutation subtypes in lung adenocarcinoma using
Liu Q; Sun D; Li N; Kim J; Feng D; Huang G; Wang L; Song S
Transl Lung Cancer Res; 2020 Jun; 9(3):549-562. PubMed ID: 32676319
[TBL] [Abstract][Full Text] [Related]
12. Value of pre-therapy
Zhang J; Zhao X; Zhao Y; Zhang J; Zhang Z; Wang J; Wang Y; Dai M; Han J
Eur J Nucl Med Mol Imaging; 2020 May; 47(5):1137-1146. PubMed ID: 31728587
[TBL] [Abstract][Full Text] [Related]
13. CT-Based Radiomic Signature as a Prognostic Factor in Stage IV
Li H; Zhang R; Wang S; Fang M; Zhu Y; Hu Z; Dong D; Shi J; Tian J
Front Oncol; 2020; 10():57. PubMed ID: 32133282
[No Abstract] [Full Text] [Related]
14. A Comprehensive Nomogram Combining CT Imaging with Clinical Features for Prediction of Lymph Node Metastasis in Stage I-IIIB Non-small Cell Lung Cancer.
Zheng X; Shao J; Zhou L; Wang L; Ge Y; Wang G; Feng F
Ther Innov Regul Sci; 2022 Jan; 56(1):155-167. PubMed ID: 34699046
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. A clinically practical radiomics-clinical combined model based on PET/CT data and nomogram predicts EGFR mutation in lung adenocarcinoma.
Chang C; Zhou S; Yu H; Zhao W; Ge Y; Duan S; Wang R; Qian X; Lei B; Wang L; Liu L; Ruan M; Yan H; Sun X; Xie W
Eur Radiol; 2021 Aug; 31(8):6259-6268. PubMed ID: 33544167
[TBL] [Abstract][Full Text] [Related]
17. IMPORTANCE of PRETREATMENT 18F-FDG PET/CT TEXTURE ANALYSIS in PREDICTING EGFR and ALK MUTATION in PATIENTS with NON-SMALL CELL LUNG CANCER.
Agüloğlu N; Aksu A; Akyol M; Katgı N; Doksöz TÇ
Nuklearmedizin; 2022 Dec; 61(6):433-439. PubMed ID: 35977671
[TBL] [Abstract][Full Text] [Related]
18. Machine learning for differentiating lung squamous cell cancer from adenocarcinoma using Clinical-Metabolic characteristics and 18F-FDG PET/CT radiomics.
Zhang Y; Liu H; Chang C; Yin Y; Wang R
PLoS One; 2024; 19(4):e0300170. PubMed ID: 38568892
[TBL] [Abstract][Full Text] [Related]
19. Dual-Centre Harmonised Multimodal Positron Emission Tomography/Computed Tomography Image Radiomic Features and Machine Learning Algorithms for Non-small Cell Lung Cancer Histopathological Subtype Phenotype Decoding.
Khodabakhshi Z; Amini M; Hajianfar G; Oveisi M; Shiri I; Zaidi H
Clin Oncol (R Coll Radiol); 2023 Nov; 35(11):713-725. PubMed ID: 37599160
[TBL] [Abstract][Full Text] [Related]
20. Individualized nomogram for predicting ALK rearrangement status in lung adenocarcinoma patients.
Song L; Zhu Z; Wu H; Han W; Cheng X; Li J; Du H; Lei J; Sui X; Song W; Jin ZY
Eur Radiol; 2021 Apr; 31(4):2034-2047. PubMed ID: 33146791
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
