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Journal Abstract Search

214 related items for PubMed ID: 37401307

  • 1. A decision tree model to distinguish between benign and malignant pulmonary nodules on CT scans.
    Ma XB, Xu QL, Li N, Wang LN, Li HC, Jiang SJ.
    Eur Rev Med Pharmacol Sci; 2023 Jun; 27(12):5692-5699. PubMed ID: 37401307
    [Abstract] [Full Text] [Related]

  • 2. A nomogram combining CT-based radiomic features with clinical features for the differentiation of benign and malignant cystic pulmonary nodules.
    Yao Y, Yang Y, Hu Q, Xie X, Jiang W, Liu C, Li X, Wang Y, Luo L, Li J.
    J Cardiothorac Surg; 2024 Jun 27; 19(1):392. PubMed ID: 38937772
    [Abstract] [Full Text] [Related]

  • 3. Preoperative diagnosis of malignant pulmonary nodules in lung cancer screening with a radiomics nomogram.
    Liu A, Wang Z, Yang Y, Wang J, Dai X, Wang L, Lu Y, Xue F.
    Cancer Commun (Lond); 2020 Jan 27; 40(1):16-24. PubMed ID: 32125097
    [Abstract] [Full Text] [Related]

  • 4. CT-Assisted Improvements in the Accuracy of the Intraoperative Frozen Section Examination of Ground-Glass Density Nodules.
    Xinli W, Xiaoshuang S, Chengxin Y, Qiang Z.
    Comput Math Methods Med; 2022 Jan 27; 2022():8967643. PubMed ID: 35035526
    [Abstract] [Full Text] [Related]

  • 5. [Risk factor analysis of the patients with solitary pulmonary nodules and establishment of a prediction model for the probability of malignancy].
    Wang X, Xu YH, Du ZY, Qian YJ, Xu ZH, Chen R, Shi MH.
    Zhonghua Zhong Liu Za Zhi; 2018 Feb 23; 40(2):115-120. PubMed ID: 29502371
    [Abstract] [Full Text] [Related]

  • 6. Combination of positron emission tomography/computed tomography and chest thin-layer high-resolution computed tomography for evaluation of pulmonary nodules: Correlation with imaging features, maximum standardized uptake value, and pathology.
    Hou S, Lin X, Wang S, Shen Y, Meng Z, Jia Q, Tan J.
    Medicine (Baltimore); 2018 Aug 23; 97(31):e11640. PubMed ID: 30075545
    [Abstract] [Full Text] [Related]

  • 7. Combining serum miRNAs, CEA, and CYFRA21-1 with imaging and clinical features to distinguish benign and malignant pulmonary nodules: a pilot study : Xianfeng Li et al.: Combining biomarker, imaging, and clinical features to distinguish pulmonary nodules.
    Li X, Zhang Q, Jin X, Cao L.
    World J Surg Oncol; 2017 May 25; 15(1):107. PubMed ID: 28545454
    [Abstract] [Full Text] [Related]

  • 8. Diagnostic accuracy of low-dose dual-input computed tomography perfusion in the differential diagnosis of pulmonary benign and malignant ground-glass nodules.
    Hu X, Gou J, Wang L, Lin W, Li W, Yang F.
    Sci Rep; 2024 Jul 24; 14(1):17098. PubMed ID: 39048627
    [Abstract] [Full Text] [Related]

  • 9. Establishment and validation of multiclassification prediction models for pulmonary nodules based on machine learning.
    Liu Q, Lv X, Zhou D, Yu N, Hong Y, Zeng Y.
    Clin Respir J; 2024 May 24; 18(5):e13769. PubMed ID: 38736274
    [Abstract] [Full Text] [Related]

  • 10. A clinically applicable model more suitable for predicting malignancy or benignity of pulmonary ground glass nodules in women patients.
    Zhu X, Shen C, Dong J.
    BMC Cancer; 2024 Oct 03; 24(1):1225. PubMed ID: 39363284
    [Abstract] [Full Text] [Related]

  • 11. Deep Learning for Malignancy Risk Estimation of Pulmonary Nodules Detected at Low-Dose Screening CT.
    Venkadesh KV, Setio AAA, Schreuder A, Scholten ET, Chung K, W Wille MM, Saghir Z, van Ginneken B, Prokop M, Jacobs C.
    Radiology; 2021 Aug 03; 300(2):438-447. PubMed ID: 34003056
    [Abstract] [Full Text] [Related]

  • 12. Diagnosis of Benign and Malignant Pulmonary Ground-Glass Nodules Using Computed Tomography Radiomics Parameters.
    Liang L, Zhang H, Lei H, Zhou H, Wu Y, Shen J.
    Technol Cancer Res Treat; 2022 Aug 03; 21():15330338221119748. PubMed ID: 36259167
    [Abstract] [Full Text] [Related]

  • 13. Quantitative CT analysis of lung parenchyma to improve malignancy risk estimation in incidental pulmonary nodules.
    Peters AA, Weinheimer O, von Stackelberg O, Kroschke J, Piskorski L, Debic M, Schlamp K, Welzel L, Pohl M, Christe A, Ebner L, Kauczor HU, Heußel CP, Wielpütz MO.
    Eur Radiol; 2023 Jun 03; 33(6):3908-3917. PubMed ID: 36538071
    [Abstract] [Full Text] [Related]

  • 14. Predictive model for the diagnosis of benign/malignant small pulmonary nodules.
    Chen W, Zhu D, Chen H, Luo J, Fu H.
    Medicine (Baltimore); 2020 Apr 03; 99(15):e19452. PubMed ID: 32282697
    [Abstract] [Full Text] [Related]

  • 15. Risk of malignancy in pulmonary nodules: A validation study of four prediction models.
    Al-Ameri A, Malhotra P, Thygesen H, Plant PK, Vaidyanathan S, Karthik S, Scarsbrook A, Callister ME.
    Lung Cancer; 2015 Jul 03; 89(1):27-30. PubMed ID: 25864782
    [Abstract] [Full Text] [Related]

  • 16. Preoperative CT-based radiomics combined with intraoperative frozen section is predictive of invasive adenocarcinoma in pulmonary nodules: a multicenter study.
    Wu G, Woodruff HC, Sanduleanu S, Refaee T, Jochems A, Leijenaar R, Gietema H, Shen J, Wang R, Xiong J, Bian J, Wu J, Lambin P.
    Eur Radiol; 2020 May 03; 30(5):2680-2691. PubMed ID: 32006165
    [Abstract] [Full Text] [Related]

  • 17. [Advances and Clinical Application of Malignant Probability Prediction Models for 
Solitary Pulmonary Nodule].
    Wang Z, Zhao J, Wang M.
    Zhongguo Fei Ai Za Zhi; 2021 Sep 20; 24(9):660-667. PubMed ID: 34455736
    [Abstract] [Full Text] [Related]

  • 18. Characterization of Pulmonary Nodules Based on Features of Margin Sharpness and Texture.
    Ferreira JR, Oliveira MC, de Azevedo-Marques PM.
    J Digit Imaging; 2018 Aug 20; 31(4):451-463. PubMed ID: 29047033
    [Abstract] [Full Text] [Related]

  • 19. Evaluation of Prediction Models for Identifying Malignancy in Pulmonary Nodules Detected via Low-Dose Computed Tomography.
    González Maldonado S, Delorme S, Hüsing A, Motsch E, Kauczor HU, Heussel CP, Kaaks R.
    JAMA Netw Open; 2020 Feb 05; 3(2):e1921221. PubMed ID: 32058555
    [Abstract] [Full Text] [Related]

  • 20. Whole-Lesion Computed Tomography-Based Entropy Parameters for the Differentiation of Minimally Invasive and Invasive Adenocarcinomas Appearing as Pulmonary Subsolid Nodules.
    Chen X, Feng B, Chen Y, Hao Y, Duan X, Cui E, Liu Z, Zhang C, Long W.
    J Comput Assist Tomogr; 2019 Feb 05; 43(5):817-824. PubMed ID: 31343995
    [Abstract] [Full Text] [Related]

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