BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

130 related articles for article (PubMed ID: 34391002)

  • 21. A Radiomics Signature in Preoperative Predicting Degree of Tumor Differentiation in Patients with Non-small Cell Lung Cancer.
    Chen X; Fang M; Dong D; Wei X; Liu L; Xu X; Jiang X; Tian J; Liu Z
    Acad Radiol; 2018 Dec; 25(12):1548-1555. PubMed ID: 29572049
    [TBL] [Abstract][Full Text] [Related]  

  • 22. Classification of early stage non-small cell lung cancers on computed tomographic images into histological types using radiomic features: interobserver delineation variability analysis.
    Haga A; Takahashi W; Aoki S; Nawa K; Yamashita H; Abe O; Nakagawa K
    Radiol Phys Technol; 2018 Mar; 11(1):27-35. PubMed ID: 29209915
    [TBL] [Abstract][Full Text] [Related]  

  • 23. Deep learning combined with radiomics may optimize the prediction in differentiating high-grade lung adenocarcinomas in ground glass opacity lesions on CT scans.
    Wang X; Zhang L; Yang X; Tang L; Zhao J; Chen G; Li X; Yan S; Li S; Yang Y; Kang Y; Li Q; Wu N
    Eur J Radiol; 2020 Aug; 129():109150. PubMed ID: 32604042
    [TBL] [Abstract][Full Text] [Related]  

  • 24. CT radiomics analysis of lung cancers: Differentiation of squamous cell carcinoma from adenocarcinoma, a correlative study with FDG uptake.
    Tomori Y; Yamashiro T; Tomita H; Tsubakimoto M; Ishigami K; Atsumi E; Murayama S
    Eur J Radiol; 2020 Jul; 128():109032. PubMed ID: 32361604
    [TBL] [Abstract][Full Text] [Related]  

  • 25. Intratumoral and peritumoral CT-based radiomics strategy reveals distinct subtypes of non-small-cell lung cancer.
    Tang X; Huang H; Du P; Wang L; Yin H; Xu X
    J Cancer Res Clin Oncol; 2022 Sep; 148(9):2247-2260. PubMed ID: 35430688
    [TBL] [Abstract][Full Text] [Related]  

  • 26. Correlation between CT based radiomics features and gene expression data in non-small cell lung cancer.
    Wang T; Gong J; Duan HH; Wang LJ; Ye XD; Nie SD
    J Xray Sci Technol; 2019; 27(5):773-803. PubMed ID: 31450540
    [TBL] [Abstract][Full Text] [Related]  

  • 27. High-dimensional multinomial multiclass severity scoring of COVID-19 pneumonia using CT radiomics features and machine learning algorithms.
    Shiri I; Mostafaei S; Haddadi Avval A; Salimi Y; Sanaat A; Akhavanallaf A; Arabi H; Rahmim A; Zaidi H
    Sci Rep; 2022 Sep; 12(1):14817. PubMed ID: 36050434
    [TBL] [Abstract][Full Text] [Related]  

  • 28. Evaluating Histological Subtypes Classification of Primary Lung Cancers on Unenhanced Computed Tomography Based on Random Forest Model.
    Huang J; He W; Xu H; Yang S; Dai J; Guo W; Zeng M
    J Healthc Eng; 2023; 2023():8964676. PubMed ID: 36794098
    [TBL] [Abstract][Full Text] [Related]  

  • 29. Homology-based radiomic features for prediction of the prognosis of lung cancer based on CT-based radiomics.
    Kadoya N; Tanaka S; Kajikawa T; Tanabe S; Abe K; Nakajima Y; Yamamoto T; Takahashi N; Takeda K; Dobashi S; Takeda K; Nakane K; Jingu K
    Med Phys; 2020 Jun; 47(5):2197-2205. PubMed ID: 32096876
    [TBL] [Abstract][Full Text] [Related]  

  • 30. A PET/CT nomogram incorporating SUVmax and CT radiomics for preoperative nodal staging in non-small cell lung cancer.
    Xie Y; Zhao H; Guo Y; Meng F; Liu X; Zhang Y; Huai X; Wong Q; Fu Y; Zhang H
    Eur Radiol; 2021 Aug; 31(8):6030-6038. PubMed ID: 33560457
    [TBL] [Abstract][Full Text] [Related]  

  • 31. Feature selection methodology for longitudinal cone-beam CT radiomics.
    van Timmeren JE; Leijenaar RTH; van Elmpt W; Reymen B; Lambin P
    Acta Oncol; 2017 Nov; 56(11):1537-1543. PubMed ID: 28826307
    [TBL] [Abstract][Full Text] [Related]  

  • 32. 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]  

  • 33. Peritumoral radiomics features predict distant metastasis in locally advanced NSCLC.
    Dou TH; Coroller TP; van Griethuysen JJM; Mak RH; Aerts HJWL
    PLoS One; 2018; 13(11):e0206108. PubMed ID: 30388114
    [TBL] [Abstract][Full Text] [Related]  

  • 34. Imaging Phenotyping Using Radiomics to Predict Micropapillary Pattern within Lung Adenocarcinoma.
    Song SH; Park H; Lee G; Lee HY; Sohn I; Kim HS; Lee SH; Jeong JY; Kim J; Lee KS; Shim YM
    J Thorac Oncol; 2017 Apr; 12(4):624-632. PubMed ID: 27923715
    [TBL] [Abstract][Full Text] [Related]  

  • 35. Can CT radiomic analysis in NSCLC predict histology and EGFR mutation status?
    Digumarthy SR; Padole AM; Gullo RL; Sequist LV; Kalra MK
    Medicine (Baltimore); 2019 Jan; 98(1):e13963. PubMed ID: 30608433
    [TBL] [Abstract][Full Text] [Related]  

  • 36. Exploring imaging features of molecular subtypes of large cell neuroendocrine carcinoma (LCNEC).
    Hermans BCM; Sanduleanu S; Derks JL; Woodruff H; Hillen LM; Casale R; Hoesein FM; de Jong E; Berge DMHJT; Speel EJM; Lambin P; Gietema HA; Dingemans AC
    Lung Cancer; 2020 Oct; 148():94-99. PubMed ID: 32858338
    [TBL] [Abstract][Full Text] [Related]  

  • 37. A prognostic analysis method for non-small cell lung cancer based on the computed tomography radiomics.
    Wang X; Duan H; Li X; Ye X; Huang G; Nie S
    Phys Med Biol; 2020 Feb; 65(4):045006. PubMed ID: 31962301
    [TBL] [Abstract][Full Text] [Related]  

  • 38. 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]  

  • 39. Radiomics signature: A potential and incremental predictor for EGFR mutation status in NSCLC patients, comparison with CT morphology.
    Tu W; Sun G; Fan L; Wang Y; Xia Y; Guan Y; Li Q; Zhang D; Liu S; Li Z
    Lung Cancer; 2019 Jun; 132():28-35. PubMed ID: 31097090
    [TBL] [Abstract][Full Text] [Related]  

  • 40. Measuring Computed Tomography Scanner Variability of Radiomics Features.
    Mackin D; Fave X; Zhang L; Fried D; Yang J; Taylor B; Rodriguez-Rivera E; Dodge C; Jones AK; Court L
    Invest Radiol; 2015 Nov; 50(11):757-65. PubMed ID: 26115366
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 7.