Document Detail

A Retrieval-Based Computer-Aided Diagnosis System for the Characterization of Liver Lesions in CT Scans.
MedLine Citation:
PMID:  24200479     Owner:  NLM     Status:  In-Data-Review    
RATIONALE AND OBJECTIVES: 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.
MATERIALS AND 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 cm³). 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.
Peter Dankerl; Alexander Cavallaro; Alexey Tsymbal; Maria Jimena Costa; Michael Suehling; Rolf Janka; Michael Uder; Matthias Hammon
Related Documents :
20037899 - Use of amniotic membrane transplantation in isolated conjunctival bowen disease: a case...
24194749 - An increasing prominent disease of klebsiella pneumoniae liver abscess: etiology, diagn...
1424659 - Persistent superficial punctate keratitis after resolution of chlamydial follicular con...
24016779 - Pathology and localization of avibacterium endocarditidis in experimentally infected br...
24263149 - Hemophagocytic syndrome diagnosed by liver biopsy in a female patient with systemic lup...
23282739 - Mucinous cystic neoplasm or non-parasitic liver cyst? - a challenging diagnosis.
17686009 - Stromal myofibroblasts in central giant cell granuloma of the jaws cannot distinguish b...
8029749 - Cyst of the ligamentum flavum.
3208049 - Obstructive pancreatitis: unusual causes of chronic pancreatitis.
Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Academic radiology     Volume:  20     ISSN:  1878-4046     ISO Abbreviation:  Acad Radiol     Publication Date:  2013 Dec 
Date Detail:
Created Date:  2013-11-08     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9440159     Medline TA:  Acad Radiol     Country:  United States    
Other Details:
Languages:  eng     Pagination:  1526-34     Citation Subset:  IM    
Copyright Information:
Copyright © 2013 AUR. Published by Elsevier Inc. All rights reserved.
Department of Radiology, University Hospital Erlangen, Maximiliansplatz 1, 91058 Erlangen, Germany. Electronic address:
Export Citation:
APA/MLA Format     Download EndNote     Download BibTex
MeSH Terms

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

Previous Document:  Differentiating Calcium Oxalate and Hydroxyapatite Stones In Vivo Using Dual-Energy CT and Urine Sup...
Next Document:  Diffusion-Weighted Imaging of Prostate Cancer on 3T MR: Relationship between Apparent Diffusion Coef...