A Retrieval-Based Computer-Aided Diagnosis System for theCharacterization of Liver Lesions in CT Scans

Dankerl P, Cavallaro AJ, Tsymbal A, Costa MJ, Suehling M, Janka RM, Uder M, Hammon M (2013)

Publication Type: Journal article

Publication year: 2013

Journal

Academic Radiology Elsevier

Book Volume: 20

Pages Range: 1526-1534

Journal Issue: 12

DOI: 10.1016/j.acra.2013.09.001

Abstract

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 forthe 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<1cm3). 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 whenaccessing the same image information and demonstrated promising performance in classifying subcentimeter lesions in particular. © 2013 AUR.

Authors with CRIS profile

Peter Dankerl Medizinische Fakultät Alexander Josef Cavallaro Institute of Radiology Rolf Matthias Janka Medizinische Fakultät Michael Uder Lehrstuhl für Diagnostische Radiologie Matthias Hammon Institute of Radiology

Involved external institutions

Siemens AG DE Germany (DE)

How to cite

APA:

Dankerl, P., Cavallaro, A.J., Tsymbal, A., Costa, M.J., Suehling, M., Janka, R.M.,... Hammon, M. (2013). A Retrieval-Based Computer-Aided Diagnosis System for theCharacterization of Liver Lesions in CT Scans. Academic Radiology, 20(12), 1526-1534. https://doi.org/10.1016/j.acra.2013.09.001

MLA:

Dankerl, Peter, et al. "A Retrieval-Based Computer-Aided Diagnosis System for theCharacterization of Liver Lesions in CT Scans." Academic Radiology 20.12 (2013): 1526-1534.

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