title: An integrated segmentation and shape-based classification scheme for distinguishing adenocarcinomas from granulomas on lung CT.
abstract: OBJECTIVE
  Distinguishing between benign granulmoas and adenocarcinomas is confounded by their similar visual appearance on routine CT scans. Unfortunately, owing to the inability to discriminate these lesions radigraphically, many patients with benign granulomas are subjected to unnecessary surgical wedge resections and biopsies for pathologic confirmation of cancer presence or absence. This suggests the need for improved computerized characterization of these nodules in order to distinguish between these two classes of lesions on CT scans. While there has been substantial interest in the use of textural analysis for radiomic characterization of lung nodules, relatively less work has been done in shape based characterization of lung nodules, particularly with respect to granulmoas and adenocarcinomas. The primary goal of this study is to evaluate the role of 3D shape features for discrimination of benign granulomas from malignant adenocarcinomas on lung CT images. Towards this end we present an integrated framework for segmentation, feature characterization and classification of these nodules on CT.
  
  
  METHODS
  The nodule segmentation method starts with separation of lung regions from the surrounding lung anatomy. Next, the lung CT scans are projected into and represented in a three dimensional spectral embedding (SE) space, allowing for better determination of the boundaries of the nodule. This then enables the application of a gradient vector flow active contour (SEGvAC) model for nodule boundary extraction. A set of 24 shape features from both 2D slices and 3D surface of the segmented nodules are extracted, including features pertaining to the angularity, spiculation, elongation and nodule compactness. A feature selection scheme, PCA-VIP, is employed to identify the most discriminating set of features to distinguish granulmoas from adenocarcinomas within a learning set of 82 patients. The features thus identified were then combined with a support vector machine classifier and independently validated on a distinct test set comprising 67 patients. The performance of the classifier for both of the training and validation cohorts was evaluated by the area under receiver characteristic curve (ROC).
  
  
  RESULTS
  We used 82 and 67 studies from two different institutions respectively for training and independent validation of the model and the shape features. The Dice coefficient between automatically segmented nodules by SEGvAC and the manual delineations by expert radiologists (readers) was 0.84± 0.04 whereas inter-reader segmentation agreement was 0.79± 0.12. We also identified a set of consistent features (Roughness, Convexity and Spherecity) that were found to be strongly correlated across both manual and automated nodule segmentations (R > 0.80, p < 0.0001) and capture the marginal smoothness and 3D compactness of the nodules. On the independent validation set of 67 studies our classifier yielded a ROC AUC of 0.72 and 0.64 for manually- and automatically segmented nodules respectively. On a subset of 20 studies, the AUCs for the two expert radiologists and 1 pulmonologist were found to be 0.82, 0.68 and 0.58 respectively.
  
  
  CONCLUSIONS
  The major finding of this study was that certain shape features appear to differentially express between granulomas and adenocarcinomas and thus computer extracted shape cues could be used to distinguish these radiographically similar pathologies.
authors.affiliation: Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA.
authors.forename: Mehdi
authors.initials: M
authors.lastname: Alilou
authors.affiliation: Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA.
authors.forename: Niha
authors.initials: N
authors.lastname: Beig
authors.affiliation: Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA.
authors.forename: Mahdi
authors.initials: M
authors.lastname: Orooji
authors.affiliation: Department of Radiology, University of Texas Southwestern Medical Centre, Dallas, TX, 75390, USA.
authors.forename: Prabhakar
authors.initials: P
authors.lastname: Rajiah
authors.affiliation: Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, 44195, USA.
authors.forename: Vamsidhar
authors.initials: V
authors.lastname: Velcheti
authors.affiliation: Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, 44195, USA.
authors.forename: Sagar
authors.initials: S
authors.lastname: Rakshit
authors.affiliation: Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, 44195, USA.
authors.forename: Niyoti
authors.initials: N
authors.lastname: Reddy
authors.affiliation: Department of Pathology, University Hospital Cleveland Medical Center, Cleveland, OH, 44106, USA.
authors.forename: Michael
authors.initials: M
authors.lastname: Yang
authors.affiliation: Division of Pulmonology and Critical Care, Louis Stokes Cleveland VA Medical Center, Cleveland, OH, 44106, USA.
authors.forename: Frank
authors.initials: F
authors.lastname: Jacono
authors.affiliation: Department of Radiology, University Hospital Cleveland Medical Center, Cleveland, OH, 44106, USA.
authors.forename: Robert C
authors.initials: RC
authors.lastname: Gilkeson
authors.affiliation: Division of Thoracic and Esophageal Surgery, University Hospital Cleveland Medical Center, Cleveland, OH, 44106, USA.
authors.forename: Philip
authors.initials: P
authors.lastname: Linden
authors.affiliation: Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA.
authors.forename: Anant
authors.initials: A
authors.lastname: Madabhushi
mesh_terms: D000230:Adenocarcinoma / Q000000981:diagnostic imaging*; D000077192:Adenocarcinoma of Lung; D006099:Granuloma; D006801:Humans; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D011857:Radiographic Image Interpretation, Computer-Assisted; D003074:Solitary Pulmonary Nodule / Q000000981:diagnostic imaging*; D014057:Tomography, X-Ray Computed
keywords: CADx; Lung CT; nodule characterization; segmentation; shape analysis