J G Cohen1, E Reymond2, M Medici3, M Lederlin4, S Lantuejoul5, F Laurent4, A C Toffart6, A Moreau-Gaudry3, A Jankowski2, G R Ferretti7. 1. Radiology department, Grenoble Alpes University Teaching Hospital, CS 10217, 38043 Grenoble cedex 9, France. Electronic address: JCohen@chu-grenoble.fr. 2. Radiology department, Grenoble Alpes University Teaching Hospital, CS 10217, 38043 Grenoble cedex 9, France. 3. Clinical Investigation Center for Innovative Technology (CICIT), Grenoble Alpes University Teaching Hospital, CS 10217, 38043 Grenoble cedex 9, France. 4. Department of Medical Imaging, Haut-Lévêque Teaching Hospital, 33000 Bordeaux, France. 5. Pathology department, Grenoble Alpes University Teaching Hospital, CS 10217, 38043 Grenoble cedex 9, France; INSERM research unit 823, Albert Bonniot Institute, 38700 La Tronche, France. 6. INSERM research unit 823, Albert Bonniot Institute, 38700 La Tronche, France; Pneumology department, Grenoble Alpes University Teaching Hospital, CS 10217, 38043 Grenoble cedex 9, France. 7. Radiology department, Grenoble Alpes University Teaching Hospital, CS 10217, 38043 Grenoble cedex 9, France; Pneumology department, Grenoble Alpes University Teaching Hospital, CS 10217, 38043 Grenoble cedex 9, France.
Abstract
PURPOSE: The purpose of this study was to evaluate the usefulness of computed tomography-texture analysis (CTTA) in differentiating between in-situ and minimally-invasive from invasive adenocarcinomas in subsolid lung nodules (SSLNs). MATERIAL AND METHODS: Two radiologists retrospectively reviewed 49 SSLNs in 44 patients. There were 27 men and 17 women with a mean age of 63±7 (SD) years (range: 47-78years). For each SSLN, type (pure ground-glass or part-solid) was assessed by consensus and CTTA was conducted independently by each observer using a filtration-histogram technique. Different filters were used before histogram quantification: no filtration, fine, medium and coarse, followed by histogram quantification using mean intensity, standard deviation (SD), entropy, mean positive pixels (MPP), skewness and kurtosis. RESULTS: We analyzed 13 pure ground-glass and 36 part-solid nodules corresponding to 16 adenocarcinomas in-situ (AIS), 5 minimally invasive adenocarcinomas (MIA) and 28 invasive adenocarcinomas (IVA). At uni- and multivariate analysis CTTA allowed discriminating between IVAs and AIS/MIA (P<0.05 and P=0.025, respectively) with the following histogram parameters: skewness using fine textures and kurtosis using coarse filtration for pure ground-glass nodules, and SD without filtration for part-solid nodules. CONCLUSION: CTTA has the potential to differentiate AIS and MIA from IVA among SSLNs. However, our results require further validation on a larger cohort.
PURPOSE: The purpose of this study was to evaluate the usefulness of computed tomography-texture analysis (CTTA) in differentiating between in-situ and minimally-invasive from invasive adenocarcinomas in subsolid lung nodules (SSLNs). MATERIAL AND METHODS: Two radiologists retrospectively reviewed 49 SSLNs in 44 patients. There were 27 men and 17 women with a mean age of 63±7 (SD) years (range: 47-78years). For each SSLN, type (pure ground-glass or part-solid) was assessed by consensus and CTTA was conducted independently by each observer using a filtration-histogram technique. Different filters were used before histogram quantification: no filtration, fine, medium and coarse, followed by histogram quantification using mean intensity, standard deviation (SD), entropy, mean positive pixels (MPP), skewness and kurtosis. RESULTS: We analyzed 13 pure ground-glass and 36 part-solid nodules corresponding to 16 adenocarcinomas in-situ (AIS), 5 minimally invasive adenocarcinomas (MIA) and 28 invasive adenocarcinomas (IVA). At uni- and multivariate analysis CTTA allowed discriminating between IVAs and AIS/MIA (P<0.05 and P=0.025, respectively) with the following histogram parameters: skewness using fine textures and kurtosis using coarse filtration for pure ground-glass nodules, and SD without filtration for part-solid nodules. CONCLUSION:CTTA has the potential to differentiate AIS and MIA from IVA among SSLNs. However, our results require further validation on a larger cohort.