在CT上自动化间歇性肺异常概率预测:波士顿肺癌研究中的逐步机器学习方法

Akinori Hata¹, Kota Aoyagi¹, Takuya Hino¹+16

• ¹From the Center for Pulmonary Functional Imaging, Department of Radiology (A.H., T.H., N.W., V.I.V., M. Nishino, H.H.), and Pulmonary and Critical Care Division (G.M.H.), Brigham and Women's Hospital and Harvard Medical School, 75 Francis St, Boston, MA 02115; Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, Osaka, Japan (A.H., N.T.); Canon Medical Systems, Tochigi, Japan (K.A., Y.M., M. Nakatsugawa, A.K., N.S., M.O.); Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan (T.H., N.W.); R&D Headquarters, Canon, Tokyo, Japan (M.K.); Department of Biostatistics, University of Michigan, Ann Arbor, Mich (J.S., Y.L.); Departments of Biostatistics (X.W., D.C.C.) and Environmental Health (D.C.C.), Harvard T.H. Chan School of Public Health, Boston, Mass; and Department of Imaging, Dana Farber Cancer Institute, Boston, Mass (M. Nishino).