These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

155 related articles for article (PubMed ID: 36338735)

  • 1. 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]  

  • 2. 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]  

  • 3. 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]  

  • 4. 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]  

  • 5. The deep learning model combining CT image and clinicopathological information for predicting ALK fusion status and response to ALK-TKI therapy in non-small cell lung cancer patients.
    Song Z; Liu T; Shi L; Yu Z; Shen Q; Xu M; Huang Z; Cai Z; Wang W; Xu C; Sun J; Chen M
    Eur J Nucl Med Mol Imaging; 2021 Feb; 48(2):361-371. PubMed ID: 32794105
    [TBL] [Abstract][Full Text] [Related]  

  • 6. 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]  

  • 7. 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]  

  • 8. Assessment of a Radiomic Signature Developed in a General NSCLC Cohort for Predicting Overall Survival of ALK-Positive Patients With Different Treatment Types.
    Huang L; Chen J; Hu W; Xu X; Liu D; Wen J; Lu J; Cao J; Zhang J; Gu Y; Wang J; Fan M
    Clin Lung Cancer; 2019 Nov; 20(6):e638-e651. PubMed ID: 31375452
    [TBL] [Abstract][Full Text] [Related]  

  • 9. 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]  

  • 10. 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]  

  • 11. 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]  

  • 12. Predicting EGFR mutation, ALK rearrangement, and uncommon EGFR mutation in NSCLC patients by driverless artificial intelligence: a cohort study.
    Tan X; Li Y; Wang S; Xia H; Meng R; Xu J; Duan Y; Li Y; Yang G; Ma Y; Jin Y
    Respir Res; 2022 May; 23(1):132. PubMed ID: 35624472
    [TBL] [Abstract][Full Text] [Related]  

  • 13. 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]  

  • 14. Optimizing the radiomics-machine-learning model based on non-contrast enhanced CT for the simplified risk categorization of thymic epithelial tumors: A large cohort retrospective study.
    Feng XL; Wang SZ; Chen HH; Huang YX; Xin YK; Zhang T; Cheng DL; Mao L; Li XL; Liu CX; Hu YC; Wang W; Cui GB; Nan HY
    Lung Cancer; 2022 Apr; 166():150-160. PubMed ID: 35287067
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Application of machine learning model to predict osteoporosis based on abdominal computed tomography images of the psoas muscle: a retrospective study.
    Huang CB; Hu JS; Tan K; Zhang W; Xu TH; Yang L
    BMC Geriatr; 2022 Oct; 22(1):796. PubMed ID: 36229793
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Pretreatment Thoracic CT Radiomic Features to Predict Brain Metastases in Patients With
    Wang H; Chen YZ; Li WH; Han Y; Li Q; Ye Z
    Front Genet; 2022; 13():772090. PubMed ID: 35281837
    [No Abstract]   [Full Text] [Related]  

  • 17. 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]  

  • 18. Prediction of TTF-1 expression in non-small-cell lung cancer using machine learning-based radiomics.
    Zhang R; Huo X; Wang Q; Zhang J; Duan S; Zhang Q; Zhang S
    J Cancer Res Clin Oncol; 2023 Jul; 149(8):4547-4554. PubMed ID: 36151427
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Development and validation of a Radiopathomics model based on CT scans and whole slide images for discriminating between Stage I-II and Stage III gastric cancer.
    Tan Y; Feng LJ; Huang YH; Xue JW; Feng ZB; Long LL
    BMC Cancer; 2024 Mar; 24(1):368. PubMed ID: 38519974
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Machine Learning-Based CT Radiomics Method for Identifying the Stage of Wilms Tumor in Children.
    Ma XH; Shu L; Jia X; Zhou HC; Liu TT; Liang JW; Ding YS; He M; Shu Q
    Front Pediatr; 2022; 10():873035. PubMed ID: 35676904
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.