BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

122 related articles for article (PubMed ID: 37728592)

  • 1. 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]  

  • 2. Tumor glycolytic heterogeneity improves detection of regional nodal metastasis in patients with lung adenocarcinoma.
    Lue KH; Chu SC; Wang LY; Chen YC; Li MH; Chang BS; Chan SC; Chen YH; Lin CB; Liu SH
    Ann Nucl Med; 2022 Mar; 36(3):256-266. PubMed ID: 34817824
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Predicting occult lymph node metastasis in solid-predominantly invasive lung adenocarcinoma across multiple centers using radiomics-deep learning fusion model.
    Tian W; Yan Q; Huang X; Feng R; Shan F; Geng D; Zhang Z
    Cancer Imaging; 2024 Jan; 24(1):8. PubMed ID: 38216999
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Development and validation of a deep learning signature for predicting lymph node metastasis in lung adenocarcinoma: comparison with radiomics signature and clinical-semantic model.
    Ma X; Xia L; Chen J; Wan W; Zhou W
    Eur Radiol; 2023 Mar; 33(3):1949-1962. PubMed ID: 36169691
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Developing a primary tumor and lymph node 18F-FDG PET/CT-clinical (TLPC) model to predict lymph node metastasis of resectable T2-4 NSCLC.
    Wang M; Liu L; Dai Q; Jin M; Huang G
    J Cancer Res Clin Oncol; 2023 Jan; 149(1):247-261. PubMed ID: 36565319
    [TBL] [Abstract][Full Text] [Related]  

  • 6. A multidomain fusion model of radiomics and deep learning to discriminate between PDAC and AIP based on
    Wei W; Jia G; Wu Z; Wang T; Wang H; Wei K; Cheng C; Liu Z; Zuo C
    Jpn J Radiol; 2023 Apr; 41(4):417-427. PubMed ID: 36409398
    [TBL] [Abstract][Full Text] [Related]  

  • 7. A deep learning-based radiomics model for predicting lymph node status from lung adenocarcinoma.
    Xie H; Song C; Jian L; Guo Y; Li M; Luo J; Li Q; Tan T
    BMC Med Imaging; 2024 May; 24(1):121. PubMed ID: 38789936
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Combination of
    Li S; Li Y; Zhao M; Wang P; Xin J
    Korean J Radiol; 2022 Sep; 23(9):921-930. PubMed ID: 36047542
    [TBL] [Abstract][Full Text] [Related]  

  • 9. 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]  

  • 10. A machine learning tool to improve prediction of mediastinal lymph node metastases in non-small cell lung cancer using routinely obtainable [
    Rogasch JMM; Michaels L; Baumgärtner GL; Frost N; Rückert JC; Neudecker J; Ochsenreither S; Gerhold M; Schmidt B; Schneider P; Amthauer H; Furth C; Penzkofer T
    Eur J Nucl Med Mol Imaging; 2023 Jun; 50(7):2140-2151. PubMed ID: 36820890
    [TBL] [Abstract][Full Text] [Related]  

  • 11. 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]  

  • 12. Multi-lesion radiomics of PET/CT for non-invasive survival stratification and histologic tumor risk profiling in patients with lung adenocarcinoma.
    Zhao M; Kluge K; Papp L; Grahovac M; Yang S; Jiang C; Krajnc D; Spielvogel CP; Ecsedi B; Haug A; Wang S; Hacker M; Zhang W; Li X
    Eur Radiol; 2022 Oct; 32(10):7056-7067. PubMed ID: 35896836
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Optimal
    Zuo Y; Liu Q; Li N; Li P; Zhang J; Song S
    Front Oncol; 2023; 13():1173355. PubMed ID: 37223682
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Preoperative prediction of mediastinal lymph node metastasis in non-small cell lung cancer based on
    Huang Y; Jiang X; Xu H; Zhang D; Liu LN; Xia YX; Xu DK; Wu HJ; Cheng G; Shi YH
    Clin Radiol; 2023 Jan; 78(1):8-17. PubMed ID: 36192203
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Occult mediastinal lymph node metastasis in FDG-PET/CT node-negative lung adenocarcinoma patients: Risk factors and histopathological study.
    Miao H; Shaolei L; Nan L; Yumei L; Shanyuan Z; Fangliang L; Yue Y
    Thorac Cancer; 2019 Jun; 10(6):1453-1460. PubMed ID: 31127706
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Multiparametric MRI-Based Radiomics Approaches for Preoperative Prediction of EGFR Mutation Status in Spinal Bone Metastases in Patients with Lung Adenocarcinoma.
    Jiang X; Ren M; Shuang X; Yang H; Shi D; Lai Q; Dong Y
    J Magn Reson Imaging; 2021 Aug; 54(2):497-507. PubMed ID: 33638577
    [TBL] [Abstract][Full Text] [Related]  

  • 17. FDG PET/CT radiomics as a tool to differentiate between reactive axillary lymphadenopathy following COVID-19 vaccination and metastatic breast cancer axillary lymphadenopathy: a pilot study.
    Eifer M; Pinian H; Klang E; Alhoubani Y; Kanana N; Tau N; Davidson T; Konen E; Catalano OA; Eshet Y; Domachevsky L
    Eur Radiol; 2022 Sep; 32(9):5921-5929. PubMed ID: 35385985
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Value of multi-center
    Zuo Y; Liu L; Chang C; Yan H; Wang L; Sun D; Ruan M; Lei B; Xia X; Xie W; Song S; Huang G
    Med Phys; 2024 Jan; ():. PubMed ID: 38285641
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Prediction of lung malignancy progression and survival with machine learning based on pre-treatment FDG-PET/CT.
    Huang B; Sollee J; Luo YH; Reddy A; Zhong Z; Wu J; Mammarappallil J; Healey T; Cheng G; Azzoli C; Korogodsky D; Zhang P; Feng X; Li J; Yang L; Jiao Z; Bai HX
    EBioMedicine; 2022 Aug; 82():104127. PubMed ID: 35810561
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Ability of FDG PET and CT radiomics features to differentiate between primary and metastatic lung lesions.
    Kirienko M; Cozzi L; Rossi A; Voulaz E; Antunovic L; Fogliata A; Chiti A; Sollini M
    Eur J Nucl Med Mol Imaging; 2018 Sep; 45(10):1649-1660. PubMed ID: 29623375
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.