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Journal Abstract Search

688 related items for PubMed ID: 30368011

  • 21. Validation of a Deep Learning Algorithm for the Detection of Malignant Pulmonary Nodules in Chest Radiographs.
    Yoo H, Kim KH, Singh R, Digumarthy SR, Kalra MK.
    JAMA Netw Open; 2020 Sep 01; 3(9):e2017135. PubMed ID: 32970157
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  • 24. Preliminary Investigation and Imaging Analysis of Early Lung Cancer Screening Among Petroleum Workers in North China.
    Guo ZJ, Zhang Q, Liu Y, Bai ZM, Qiang L, Li HY, Zhang YH, Chi HW, Men M, Xu Q, Liang F, Feng WQ, Lu JY, Liu HT, Zeng YH.
    Cancer Invest; 2021 Apr 01; 39(4):321-332. PubMed ID: 33645376
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  • 27. Validation of a deep learning computer aided system for CT based lung nodule detection, classification, and growth rate estimation in a routine clinical population.
    Murchison JT, Ritchie G, Senyszak D, Nijwening JH, van Veenendaal G, Wakkie J, van Beek EJR.
    PLoS One; 2022 Apr 01; 17(5):e0266799. PubMed ID: 35511758
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  • 28. Assessment of Fluorodeoxyglucose F18-Labeled Positron Emission Tomography for Diagnosis of High-Risk Lung Nodules.
    Maiga AW, Deppen SA, Mercaldo SF, Blume JD, Montgomery C, Vaszar LT, Williamson C, Isbell JM, Rickman OB, Pinkerman R, Lambright ES, Nesbitt JC, Grogan EL.
    JAMA Surg; 2018 Apr 01; 153(4):329-334. PubMed ID: 29117314
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  • 29. Risk stratification for lung nodules: Size isn't everything.
    Eguchi T, Adusumilli PS.
    J Thorac Cardiovasc Surg; 2017 Jun 01; 153(6):1557-1562. PubMed ID: 28314535
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  • 31. Prediction of the Risk of Malignancy Among Detected Lung Nodules in the National Lung Screening Trial.
    Hassannezhad R, Vahed N.
    J Am Coll Radiol; 2018 Nov 01; 15(11):1529-1535. PubMed ID: 30145120
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  • 32. 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
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  • 33. Estimation of malignancy of pulmonary nodules at CT scans: Effect of computer-aided diagnosis on diagnostic performance of radiologists.
    Liu J, Zhao L, Han X, Ji H, Liu L, He W.
    Asia Pac J Clin Oncol; 2021 Jun 03; 17(3):216-221. PubMed ID: 32757455
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  • 34. Comparison Between Radiological Semantic Features and Lung-RADS in Predicting Malignancy of Screen-Detected Lung Nodules in the National Lung Screening Trial.
    Li Q, Balagurunathan Y, Liu Y, Qi J, Schabath MB, Ye Z, Gillies RJ.
    Clin Lung Cancer; 2018 Mar 03; 19(2):148-156.e3. PubMed ID: 29137847
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  • 35. A cloud-based computer-aided detection system improves identification of lung nodules on computed tomography scans of patients with extra-thoracic malignancies.
    Vassallo L, Traverso A, Agnello M, Bracco C, Campanella D, Chiara G, Fantacci ME, Lopez Torres E, Manca A, Saletta M, Giannini V, Mazzetti S, Stasi M, Cerello P, Regge D.
    Eur Radiol; 2019 Jan 03; 29(1):144-152. PubMed ID: 29948089
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  • 36. Lung nodules: size still matters.
    Larici AR, Farchione A, Franchi P, Ciliberto M, Cicchetti G, Calandriello L, Del Ciello A, Bonomo L.
    Eur Respir Rev; 2017 Dec 31; 26(146):. PubMed ID: 29263171
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  • 39. Autoantibody Signature Enhances the Positive Predictive Power of Computed Tomography and Nodule-Based Risk Models for Detection of Lung Cancer.
    Massion PP, Healey GF, Peek LJ, Fredericks L, Sewell HF, Murray A, Robertson JF.
    J Thorac Oncol; 2017 Mar 31; 12(3):578-584. PubMed ID: 27615397
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  • 40. [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
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