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 *

238 related articles for article (PubMed ID: 32086583)

  • 1. A quantitative imaging biomarker for predicting disease-free-survival-associated histologic subgroups in lung adenocarcinoma.
    Lu L; Wang D; Wang L; E L; Guo P; Li Z; Xiang J; Yang H; Li H; Yin S; Schwartz LH; Xie C; Zhao B
    Eur Radiol; 2020 Jul; 30(7):3614-3623. PubMed ID: 32086583
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Preoperative CT-based Deep Learning Model for Predicting Disease-Free Survival in Patients with Lung Adenocarcinomas.
    Kim H; Goo JM; Lee KH; Kim YT; Park CM
    Radiology; 2020 Jul; 296(1):216-224. PubMed ID: 32396042
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Use of a prognostic risk score that aggregates the FDG-PET/CT SUVmax, tumor size, and histologic group for predicting the prognosis of pStage I lung adenocarcinoma.
    Kawakita N; Toba H; Kawakami Y; Takizawa H; Bando Y; Otuska H; Matsumoto D; Takashima M; Tsuboi M; Yoshida M; Kondo K; Tangoku A
    Int J Clin Oncol; 2020 Jun; 25(6):1079-1089. PubMed ID: 32130543
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Marginal radiomics features as imaging biomarkers for pathological invasion in lung adenocarcinoma.
    Cho HH; Lee G; Lee HY; Park H
    Eur Radiol; 2020 May; 30(5):2984-2994. PubMed ID: 31965255
    [TBL] [Abstract][Full Text] [Related]  

  • 5. A comparative study to evaluate CT-based semantic and radiomic features in preoperative diagnosis of invasive pulmonary adenocarcinomas manifesting as subsolid nodules.
    Wu YJ; Liu YC; Liao CY; Tang EK; Wu FZ
    Sci Rep; 2021 Jan; 11(1):66. PubMed ID: 33462251
    [TBL] [Abstract][Full Text] [Related]  

  • 6. PET/CT-based deep learning grading signature to optimize surgical decisions for clinical stage I invasive lung adenocarcinoma and biologic basis under its prediction: a multicenter study.
    Zhong Y; Cai C; Chen T; Gui H; Chen C; Deng J; Yang M; Yu B; Song Y; Wang T; Chen Y; Shi H; Xie D; Chen C; She Y
    Eur J Nucl Med Mol Imaging; 2024 Jan; 51(2):521-534. PubMed ID: 37725128
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Prognostication of lung adenocarcinomas using CT-based deep learning of morphological and histopathological features: a retrospective dual-institutional study.
    Lee T; Lee KH; Lee JH; Park S; Kim YT; Goo JM; Kim H
    Eur Radiol; 2024 May; 34(5):3431-3443. PubMed ID: 37861801
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Outcome prediction in resectable lung adenocarcinoma patients: value of CT radiomics.
    Choe J; Lee SM; Do KH; Kim S; Choi S; Lee JG; Seo JB
    Eur Radiol; 2020 Sep; 30(9):4952-4963. PubMed ID: 32356158
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Prognostic analysis and risk stratification of lung adenocarcinoma undergoing EGFR-TKI therapy with time-serial CT-based radiomics signature.
    Zhang X; Lu B; Yang X; Lan D; Lin S; Zhou Z; Li K; Deng D; Peng P; Zeng Z; Long L
    Eur Radiol; 2023 Feb; 33(2):825-835. PubMed ID: 36166088
    [TBL] [Abstract][Full Text] [Related]  

  • 10. CT-based radiomics and machine learning to predict spread through air space in lung adenocarcinoma.
    Jiang C; Luo Y; Yuan J; You S; Chen Z; Wu M; Wang G; Gong J
    Eur Radiol; 2020 Jul; 30(7):4050-4057. PubMed ID: 32112116
    [TBL] [Abstract][Full Text] [Related]  

  • 11. CT texture analysis-based nomogram for the preoperative prediction of visceral pleural invasion in cT1N0M0 lung adenocarcinoma: an external validation cohort study.
    Zuo Z; Li Y; Peng K; Li X; Tan Q; Mo Y; Lan Y; Zeng W; Qi W
    Clin Radiol; 2022 Mar; 77(3):e215-e221. PubMed ID: 34916048
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Computed tomography-based 3D convolutional neural network deep learning model for predicting micropapillary or solid growth pattern of invasive lung adenocarcinoma.
    Huo J; Min X; Luo T; Lv F; Feng Y; Fan Q; Wang D; Ma D; Li Q
    Radiol Med; 2024 May; 129(5):776-784. PubMed ID: 38512613
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Solitary solid pulmonary nodules: a CT-based deep learning nomogram helps differentiate tuberculosis granulomas from lung adenocarcinomas.
    Feng B; Chen X; Chen Y; Lu S; Liu K; Li K; Liu Z; Hao Y; Li Z; Zhu Z; Yao N; Liang G; Zhang J; Long W; Liu X
    Eur Radiol; 2020 Dec; 30(12):6497-6507. PubMed ID: 32594210
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Comparison of a radiomic biomarker with volumetric analysis for decoding tumour phenotypes of lung adenocarcinoma with different disease-specific survival.
    Yuan M; Zhang YD; Pu XH; Zhong Y; Li H; Wu JF; Yu TF
    Eur Radiol; 2017 Nov; 27(11):4857-4865. PubMed ID: 28523350
    [TBL] [Abstract][Full Text] [Related]  

  • 15. 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]  

  • 16. 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]  

  • 17. Radiographical assessment of tumour stroma and treatment outcomes using deep learning: a retrospective, multicohort study.
    Jiang Y; Liang X; Han Z; Wang W; Xi S; Li T; Chen C; Yuan Q; Li N; Yu J; Xie Y; Xu Y; Zhou Z; Poultsides GA; Li G; Li R
    Lancet Digit Health; 2021 Jun; 3(6):e371-e382. PubMed ID: 34045003
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Computed Tomography-Based Score Indicative of Lung Cancer Aggression (SILA) Predicts the Degree of Histologic Tissue Invasion and Patient Survival in Lung Adenocarcinoma Spectrum.
    Varghese C; Rajagopalan S; Karwoski RA; Bartholmai BJ; Maldonado F; Boland JM; Peikert T
    J Thorac Oncol; 2019 Aug; 14(8):1419-1429. PubMed ID: 31063863
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Predicting adenocarcinoma recurrence using computational texture models of nodule components in lung CT.
    Depeursinge A; Yanagawa M; Leung AN; Rubin DL
    Med Phys; 2015 Apr; 42(4):2054-63. PubMed ID: 25832095
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Radiomic signature as a diagnostic factor for histologic subtype classification of non-small cell lung cancer.
    Zhu X; Dong D; Chen Z; Fang M; Zhang L; Song J; Yu D; Zang Y; Liu Z; Shi J; Tian J
    Eur Radiol; 2018 Jul; 28(7):2772-2778. PubMed ID: 29450713
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 12.