Katsutoshi Sugimoto1, Junji Shiraishi, Fuminori Moriyasu, Kunio Doi. 1. Kurt Rossmann Laboratories for Radiologic Imaging Research, Department of Radiology, The University of Chicago, 5841 S. Maryland Ave., MC 2026, Chicago, IL 60637, USA. sugimoto@comcast.net
Abstract
RATIONAL AND OBJECTIVES: To develop a computer-aided diagnostic (CAD) scheme for classifying focal liver lesions (FLLs) by use of physicians' subjective classification of echogenic patterns of FLLs on baseline and contrast-enhanced ultrasonography (US). MATERIALS AND METHODS: A total of 137 hepatic lesions in 137 patients were evaluated with B-mode and NC100100 (Sonazoid)-enhanced pulse-inversion US; lesions included 74 hepatocellular carcinomas (HCCs) (23: well-differentiated, 36: moderately differentiated, 15: poorly differentiated HCCs), 33 liver metastases, and 30 liver hemangiomas. Three physicians evaluated single images at B-mode and arterial phases with a cine mode. Physicians were asked to classify each lesion into one of eight B-mode and one of eight enhancement patterns, but did not make a diagnosis. To classify five types of FLLs, we employed a decision tree model with four decision nodes and four artificial neural networks (ANNs). The results of the physicians' pattern classifications were used successively for four different ANNs in making decisions at each of the decision nodes in the decision tree model. RESULTS: The classification accuracies for the 137 FLLs were 84.8% for metastasis, 93.3% for hemangioma, and 98.6% for all HCCs. In addition, the classification accuracies for histological differentiation types of HCCs were 65.2% for well-differentiated HCC, 41.7% for moderately differentiated HCC, and 80.0% for poorly differentiated HCC. CONCLUSIONS: This CAD scheme has the potential to improve the diagnostic accuracy of liver lesions. However, the accuracy in the histologic differential diagnosis of HCC based on baseline and contrast-enhanced US is still limited.
RATIONAL AND OBJECTIVES: To develop a computer-aided diagnostic (CAD) scheme for classifying focal liver lesions (FLLs) by use of physicians' subjective classification of echogenic patterns of FLLs on baseline and contrast-enhanced ultrasonography (US). MATERIALS AND METHODS: A total of 137 hepatic lesions in 137 patients were evaluated with B-mode and NC100100 (Sonazoid)-enhanced pulse-inversion US; lesions included 74 hepatocellular carcinomas (HCCs) (23: well-differentiated, 36: moderately differentiated, 15: poorly differentiated HCCs), 33 liver metastases, and 30 liver hemangiomas. Three physicians evaluated single images at B-mode and arterial phases with a cine mode. Physicians were asked to classify each lesion into one of eight B-mode and one of eight enhancement patterns, but did not make a diagnosis. To classify five types of FLLs, we employed a decision tree model with four decision nodes and four artificial neural networks (ANNs). The results of the physicians' pattern classifications were used successively for four different ANNs in making decisions at each of the decision nodes in the decision tree model. RESULTS: The classification accuracies for the 137 FLLs were 84.8% for metastasis, 93.3% for hemangioma, and 98.6% for all HCCs. In addition, the classification accuracies for histological differentiation types of HCCs were 65.2% for well-differentiated HCC, 41.7% for moderately differentiated HCC, and 80.0% for poorly differentiated HCC. CONCLUSIONS: This CAD scheme has the potential to improve the diagnostic accuracy of liver lesions. However, the accuracy in the histologic differential diagnosis of HCC based on baseline and contrast-enhanced US is still limited.
Authors: Jean-Michel Correas; Gavin Low; Laurence Needleman; Michelle L Robbin; David Cosgrove; Paul S Sidhu; Chris J Harvey; Thomas Albrecht; Jarl A Jakobsen; Knut Brabrand; Manfred Jenett; Jane Bates; Michel Claudon; Edward Leen Journal: Eur Radiol Date: 2011-04-10 Impact factor: 5.315