PURPOSE: Develop a quantitative image analysis method to characterize the heterogeneous patterns of nodule components for the classification of pathological categories of nodules. MATERIALS AND METHODS: With IRB approval and permission of the National Lung Screening Trial (NLST) project, 103 subjects with low dose CT (LDCT) were used in this study. We developed a radiomic quantitative CT attenuation distribution descriptor (qADD) to characterize the heterogeneous patterns of nodule components and a hybrid model (qADD+) that combined qADD with subject demographic data and radiologist-provided nodule descriptors to differentiate aggressive tumors from indolent tumors or benign nodules with pathological categorization as reference standard. The classification performances of qADD and qADD + were evaluated and compared to the Brock and the Mayo Clinic models by analysis of the area under the receiver operating characteristic curve (AUC). RESULTS: The radiomic features were consistently selected into qADDs to differentiate pathological invasive nodules from (1) preinvasive nodules, (2) benign nodules, and (3) the group of preinvasive and benign nodules, achieving test AUCs of 0.847 ± 0.002, 0.842 ± 0.002 and 0.810 ± 0.001, respectively. The qADD + obtained test AUCs of 0.867 ± 0.002, 0.888 ± 0.001 and 0.852 ± 0.001, respectively, which were higher than both the Brock and the Mayo Clinic models. CONCLUSION: The pathologic invasiveness of lung tumors could be categorized according to the CT attenuation distribution patterns of the nodule components manifested on LDCT images, and the majority of invasive lung cancers could be identified at baseline LDCT scans.
PURPOSE: Develop a quantitative image analysis method to characterize the heterogeneous patterns of nodule components for the classification of pathological categories of nodules. MATERIALS AND METHODS: With IRB approval and permission of the National Lung Screening Trial (NLST) project, 103 subjects with low dose CT (LDCT) were used in this study. We developed a radiomic quantitative CT attenuation distribution descriptor (qADD) to characterize the heterogeneous patterns of nodule components and a hybrid model (qADD+) that combined qADD with subject demographic data and radiologist-provided nodule descriptors to differentiate aggressive tumors from indolent tumors or benign nodules with pathological categorization as reference standard. The classification performances of qADD and qADD + were evaluated and compared to the Brock and the Mayo Clinic models by analysis of the area under the receiver operating characteristic curve (AUC). RESULTS: The radiomic features were consistently selected into qADDs to differentiate pathological invasive nodules from (1) preinvasive nodules, (2) benign nodules, and (3) the group of preinvasive and benign nodules, achieving test AUCs of 0.847 ± 0.002, 0.842 ± 0.002 and 0.810 ± 0.001, respectively. The qADD + obtained test AUCs of 0.867 ± 0.002, 0.888 ± 0.001 and 0.852 ± 0.001, respectively, which were higher than both the Brock and the Mayo Clinic models. CONCLUSION: The pathologic invasiveness of lung tumors could be categorized according to the CT attenuation distribution patterns of the nodule components manifested on LDCT images, and the majority of invasive lung cancers could be identified at baseline LDCT scans.
Authors: Carole A Ridge; Afra Yildirim; Phillip M Boiselle; Tomas Franquet; Cornelia M Schaefer-Prokop; Denis Tack; Pierre Alain Gevenois; Alexander A Bankier Journal: Radiology Date: 2015-10-09 Impact factor: 11.105
Authors: Iva Petkovska; Matthew S Brown; Jonathan G Goldin; Hyun J Kim; Michael F McNitt-Gray; Fereidoun G Abtin; Raffi J Ghurabi; Denise R Aberle Journal: Acad Radiol Date: 2007-04 Impact factor: 3.173
Authors: Annette McWilliams; Martin C Tammemagi; John R Mayo; Heidi Roberts; Geoffrey Liu; Kam Soghrati; Kazuhiro Yasufuku; Simon Martel; Francis Laberge; Michel Gingras; Sukhinder Atkar-Khattra; Christine D Berg; Ken Evans; Richard Finley; John Yee; John English; Paola Nasute; John Goffin; Serge Puksa; Lori Stewart; Scott Tsai; Michael R Johnston; Daria Manos; Garth Nicholas; Glenwood D Goss; Jean M Seely; Kayvan Amjadi; Alain Tremblay; Paul Burrowes; Paul MacEachern; Rick Bhatia; Ming-Sound Tsao; Stephen Lam Journal: N Engl J Med Date: 2013-09-05 Impact factor: 91.245
Authors: M Noguchi; A Morikawa; M Kawasaki; Y Matsuno; T Yamada; S Hirohashi; H Kondo; Y Shimosato Journal: Cancer Date: 1995-06-15 Impact factor: 6.860