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Machine learning for lung texture analysis on thin-section CT: Capability for assessments of disease severity and therapeutic effect for connective tissue disease patients in comparison with expert panel evaluations.
Ohno Y, Aoyagi K, Takenaka D, Yoshikawa T, Fujisawa Y, Sugihara N, Hamabuchi N, Hanamatsu S, Obama Y, Ueda T, Hattori H, Murayama K, Toyama H. Ohno Y, et al. Among authors: sugihara n. Acta Radiol. 2022 Oct;63(10):1363-1373. doi: 10.1177/02841851211044973. Epub 2021 Oct 12. Acta Radiol. 2022. PMID: 34636644
3D lung motion assessments on inspiratory/expiratory thin-section CT: Capability for pulmonary functional loss of smoking-related COPD in comparison with lung destruction and air trapping.
Koyama H, Ohno Y, Fujisawa Y, Seki S, Negi N, Murakami T, Yoshikawa T, Sugihara N, Nishimura Y, Sugimura K. Koyama H, et al. Among authors: sugihara n. Eur J Radiol. 2016 Feb;85(2):352-9. doi: 10.1016/j.ejrad.2015.11.026. Epub 2015 Nov 24. Eur J Radiol. 2016. PMID: 26781140
Dynamic contrast-enhanced perfusion area-detector CT assessed with various mathematical models: Its capability for therapeutic outcome prediction for non-small cell lung cancer patients with chemoradiotherapy as compared with that of FDG-PET/CT.
Ohno Y, Fujisawa Y, Koyama H, Kishida Y, Seki S, Sugihara N, Yoshikawa T. Ohno Y, et al. Among authors: sugihara n. Eur J Radiol. 2017 Jan;86:83-91. doi: 10.1016/j.ejrad.2016.11.008. Epub 2016 Nov 6. Eur J Radiol. 2017. PMID: 28027771
258 results