BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

256 related articles for article (PubMed ID: 34381070)

  • 1. Effects of interobserver and interdisciplinary segmentation variabilities on CT-based radiomics for pancreatic cancer.
    Wong J; Baine M; Wisnoskie S; Bennion N; Zheng D; Yu L; Dalal V; Hollingsworth MA; Lin C; Zheng D
    Sci Rep; 2021 Aug; 11(1):16328. PubMed ID: 34381070
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Reproducibility of F18-FDG PET radiomic features for different cervical tumor segmentation methods, gray-level discretization, and reconstruction algorithms.
    Altazi BA; Zhang GG; Fernandez DC; Montejo ME; Hunt D; Werner J; Biagioli MC; Moros EG
    J Appl Clin Med Phys; 2017 Nov; 18(6):32-48. PubMed ID: 28891217
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Lung tumor segmentation methods: Impact on the uncertainty of radiomics features for non-small cell lung cancer.
    Owens CA; Peterson CB; Tang C; Koay EJ; Yu W; Mackin DS; Li J; Salehpour MR; Fuentes DT; Court LE; Yang J
    PLoS One; 2018; 13(10):e0205003. PubMed ID: 30286184
    [TBL] [Abstract][Full Text] [Related]  

  • 4. 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]  

  • 5. Interobserver delineation variability of computed tomography-based radiomic features of the parotid gland.
    Buasawat K; Chamchod S; Fuangrod T; Suntiwong S; Liamsuwan T
    Radiat Oncol J; 2024 Mar; 42(1):63-73. PubMed ID: 38549385
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Robust Radiomics feature quantification using semiautomatic volumetric segmentation.
    Parmar C; Rios Velazquez E; Leijenaar R; Jermoumi M; Carvalho S; Mak RH; Mitra S; Shankar BU; Kikinis R; Haibe-Kains B; Lambin P; Aerts HJ
    PLoS One; 2014; 9(7):e102107. PubMed ID: 25025374
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Radiomic feature reproducibility in contrast-enhanced CT of the pancreas is affected by variabilities in scan parameters and manual segmentation.
    Yamashita R; Perrin T; Chakraborty J; Chou JF; Horvat N; Koszalka MA; Midya A; Gonen M; Allen P; Jarnagin WR; Simpson AL; Do RKG
    Eur Radiol; 2020 Jan; 30(1):195-205. PubMed ID: 31392481
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Two-stage deep learning model for fully automated pancreas segmentation on computed tomography: Comparison with intra-reader and inter-reader reliability at full and reduced radiation dose on an external dataset.
    Panda A; Korfiatis P; Suman G; Garg SK; Polley EC; Singh DP; Chari ST; Goenka AH
    Med Phys; 2021 May; 48(5):2468-2481. PubMed ID: 33595105
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Influence of inter-observer delineation variability on radiomics stability in different tumor sites.
    Pavic M; Bogowicz M; Würms X; Glatz S; Finazzi T; Riesterer O; Roesch J; Rudofsky L; Friess M; Veit-Haibach P; Huellner M; Opitz I; Weder W; Frauenfelder T; Guckenberger M; Tanadini-Lang S
    Acta Oncol; 2018 Aug; 57(8):1070-1074. PubMed ID: 29513054
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Interchangeability of radiomic features between [18F]-FDG PET/CT and [18F]-FDG PET/MR.
    Vuong D; Tanadini-Lang S; Huellner MW; Veit-Haibach P; Unkelbach J; Andratschke N; Kraft J; Guckenberger M; Bogowicz M
    Med Phys; 2019 Apr; 46(4):1677-1685. PubMed ID: 30714158
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Autosegmentation of prostate anatomy for radiation treatment planning using deep decision forests of radiomic features.
    Macomber MW; Phillips M; Tarapov I; Jena R; Nori A; Carter D; Folgoc LL; Criminisi A; Nyflot MJ
    Phys Med Biol; 2018 Nov; 63(23):235002. PubMed ID: 30465543
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Reproducibility and non-redundancy of radiomic features extracted from arterial phase CT scans in hepatocellular carcinoma patients: impact of tumor segmentation variability.
    Qiu Q; Duan J; Duan Z; Meng X; Ma C; Zhu J; Lu J; Liu T; Yin Y
    Quant Imaging Med Surg; 2019 Mar; 9(3):453-464. PubMed ID: 31032192
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Current state of radiomic research in pancreatic cancer: focusing on study design and reproducibility of findings.
    Malcolm JA; Tacey M; Gibbs P; Lee B; Ko HS
    Eur Radiol; 2023 Oct; 33(10):6659-6669. PubMed ID: 37079029
    [TBL] [Abstract][Full Text] [Related]  

  • 14. 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]  

  • 15. Magnetic resonance imaging (MRI) compared with computed tomography (CT) for interobserver agreement of gross tumor volume delineation in pancreatic cancer: a multi-institutional contouring study on behalf of the AIRO group for gastrointestinal cancers.
    Caravatta L; Cellini F; Simoni N; Rosa C; Niespolo RM; Lupattelli M; Picardi V; Macchia G; Sainato A; Mantello G; Dionisi F; Rosetto ME; Fusco V; Navarria F; De Paoli A; Guido A; Vecchi C; Basilico R; Cianci R; Delli Pizzi A; Di Nicola M; Mattiucci GC; Valentini V; Morganti AG; Genovesi D
    Acta Oncol; 2019 Apr; 58(4):439-447. PubMed ID: 30632876
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Multiphase computed tomography radiomics of pancreatic intraductal papillary mucinous neoplasms to predict malignancy.
    Polk SL; Choi JW; McGettigan MJ; Rose T; Ahmed A; Kim J; Jiang K; Balagurunathan Y; Qi J; Farah PT; Rathi A; Permuth JB; Jeong D
    World J Gastroenterol; 2020 Jun; 26(24):3458-3471. PubMed ID: 32655269
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Cone-beam CT-based delineation of stereotactic lung targets: the influence of image modality and target size on interobserver variability.
    Altorjai G; Fotina I; Lütgendorf-Caucig C; Stock M; Pötter R; Georg D; Dieckmann K
    Int J Radiat Oncol Biol Phys; 2012 Feb; 82(2):e265-72. PubMed ID: 21620581
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Multiple U-Net-Based Automatic Segmentations and Radiomics Feature Stability on Ultrasound Images for Patients With Ovarian Cancer.
    Jin J; Zhu H; Zhang J; Ai Y; Zhang J; Teng Y; Xie C; Jin X
    Front Oncol; 2020; 10():614201. PubMed ID: 33680934
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Robustness of pulmonary nodule radiomic features on computed tomography as a function of varying radiation dose levels-a multi-dose in vivo patient study.
    Bartholomeus GA; van Amsterdam WAC; Harder AMD; Willemink MJ; van Hamersvelt RW; de Jong PA; Leiner T
    Eur Radiol; 2023 Oct; 33(10):7044-7055. PubMed ID: 37074424
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Stability and reproducibility of computed tomography radiomic features extracted from peritumoral regions of lung cancer lesions.
    Tunali I; Hall LO; Napel S; Cherezov D; Guvenis A; Gillies RJ; Schabath MB
    Med Phys; 2019 Nov; 46(11):5075-5085. PubMed ID: 31494946
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 13.