title: A retrieval-based computer-aided diagnosis system for the characterization of liver lesions in CT scans.
abstract: OBJECTIVE
  To evaluate a computer-aided diagnosis (CADx) system for the characterization of liver lesions in computed tomography (CT) scans. The stand-alone predictive performance of the CADx system was assessed and compared to that of three radiologists who were provided with the same amount of image information to which the CADx system had access.
  
  
  METHODS
  The CADx system operates as an image search engine exploiting texture analysis of liver lesion image data for the lesion in question and lesions from a database. A region of interest drawn around an indeterminate liver lesion is used as input query. The CADx system retrieves lesions of similar histology (benign/malignant), density (hypodense/hyperdense), or type (cyst/hemangioma/metastasis). The system's performance was evaluated with leave-one-patient-out receiver operating characteristic area under the curve on 685 CT scans from 372 patients that contained 2325 liver lesions (193 <1 cm(3)). Sensitivity, specificity, and positive and negative predictive values were evaluated separately for subcentimeter lesions. Results were compared to those of three radiologists who rated 83 liver lesions (20 hemangiomas, 20 metastases, 20 cysts, 20 hepatocellular carcinomas, and 3 focal nodular hyperplasias) displaying only the liver.
  
  
  RESULTS
  The CADx system's leave-one-patient-out receiver operating characteristic area under the curve was 97.1% for density, 91.4% for histology, and 95.5% for lesion type. For subcentimeter lesions, input of additional semantic information improved the system's performance. The CADx system has been proved to significantly outperform radiologists in discriminating lesion histology and type, provided the radiologists have no access to information other than the image. The radiologists were most reliable in diagnosing hemangioma given the limited image data.
  
  
  CONCLUSIONS
  The CADx system under study discriminated reliably between various liver lesions, even outperforming radiologists when accessing the same image information and demonstrated promising performance in classifying subcentimeter lesions in particular.
authors.affiliation: Department of Radiology, University Hospital Erlangen, Maximiliansplatz 1, 91058 Erlangen, Germany. Electronic address: peter.dankerl@uk-erlangen.de.
authors.forename: Peter
authors.initials: P
authors.lastname: Dankerl
authors.forename: Alexander
authors.initials: A
authors.lastname: Cavallaro
authors.forename: Alexey
authors.initials: A
authors.lastname: Tsymbal
authors.forename: Maria Jimena
authors.initials: MJ
authors.lastname: Costa
authors.forename: Michael
authors.initials: M
authors.lastname: Suehling
authors.forename: Rolf
authors.initials: R
authors.lastname: Janka
authors.forename: Michael
authors.initials: M
authors.lastname: Uder
authors.forename: Matthias
authors.initials: M
authors.lastname: Hammon
mesh_terms: D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D016247:Information Storage and Retrieval / Q000379:methods*; D008099:Liver / Q000000981:diagnostic imaging*; D008113:Liver Neoplasms / Q000000981:diagnostic imaging*; D008297:Male; D008875:Middle Aged; D012372:ROC Curve; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*
keywords: Imaging technology; computer-assisted diagnosis; hepatobiliary; special interest