Computer-aided Diagnosis of Breast Tumors with Different US Systems 1

Rationale and Objectives. The authors performed this study to determine whether a computer-aided diagnostic (CAD) system was suitable from one ultrasound (US) unit to another after parameters were adjusted by using intelligent selection algorithms. Materials and Methods. The authors used texture analysis and data mining with a decision tree model to classify breast tumors with different US systems. The databases of training cases from one unit and testing cases from another were collected from different countries. Regions of interest on US scans and co-variance texture parameters were used in the diagnosis system. Proposed adjustment schemes for different US systems were used to transform the information needed for a differential diagnosis. Results. Comparison of the diagnostic system with and without adjustment, respectively, yielded the following results: accuracy, 89.9% and 82.2%; sensitivity, 94.6% and 92.2%; specificity, 85.4% and 72.3%; positive predictive value, 86.5% and 76.8%; and negative predictive value, 94.1% and 90.4%. The improvement in accuracy, specificity, and positive predictive value was statistically significant. Diagnostic performance was improved after the adjustment. Conclusion. After parameters were adjusted by using intelligent selection algorithms, the performance of the proposed CAD system was better both with the same and with different systems. Different resolutions, different setting conditions, and different scanner ages are no longer obstacles to the application of such a CAD system.

[1]  Philip S. Yu,et al.  Data Mining: An Overview from a Database Perspective , 1996, IEEE Trans. Knowl. Data Eng..

[2]  D. Chen,et al.  Texture analysis of breast tumors on sonograms. , 2000, Seminars in ultrasound, CT, and MR.

[3]  D. Chen,et al.  Computer-aided diagnosis for surgical office-based breast ultrasound. , 2000, Archives of surgery.

[4]  A. Stavros,et al.  Solid breast nodules: use of sonography to distinguish between benign and malignant lesions. , 1995, Radiology.

[5]  B Angus,et al.  Prediction of nodal metastasis and prognosis in breast cancer: a neural model. , 1997, Anticancer research.

[6]  C. Floyd,et al.  Breast cancer: prediction with artificial neural network based on BI-RADS standardized lexicon. , 1995, Radiology.

[7]  Joe Mullich.  Data Mining: Making Data Meaningful , 1997, Computer.

[8]  David A. Bell,et al.  Designing a Kernel for Data Mining , 1997, IEEE Expert.

[9]  Witold Pedrycz,et al.  Data Mining Methods for Knowledge Discovery , 1998, IEEE Trans. Neural Networks.

[10]  J. Coatrieux,et al.  Contemporary perspectives in three-dimensional biomedical imaging. , 1997, Studies in health technology and informatics.

[11]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[12]  Haluk Derin,et al.  Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  D. Chen,et al.  Computer-aided diagnosis applied to US of solid breast nodules by using neural networks. , 1999, Radiology.

[14]  A Manduca,et al.  Improvement in specificity of ultrasonography for diagnosis of breast tumors by means of artificial intelligence. , 1992, Medical physics.

[15]  G. Tourassi.  Journey toward computer-aided diagnosis: role of image texture analysis. , 1999, Radiology.

[16]  S C Horii,et al.  Improving the distinction between benign and malignant breast lesions: the value of sonographic texture analysis. , 1993, Ultrasonic imaging.

[17]  D R Proffitt,et al.  Visual learning in the perception of texture: simple and contingent aftereffects of texture density. , 1996, Spatial vision.

[18]  M. M. Fahmy,et al.  Texture segmentation based on a hierarchical Markov random field model , 1991, 1991., IEEE International Sympoisum on Circuits and Systems.

[19]  C. Floyd,et al.  Fractal texture analysis in computer-aided diagnosis of solitary pulmonary nodules. , 1997, Academic radiology.

[20]  T. Freer,et al.  Screening mammography with computer-aided detection: prospective study of 12,860 patients in a community breast center. , 2001, Radiology.

[21]  D. Chen,et al.  Breast cancer diagnosis using self-organizing map for sonography. , 2000, Ultrasound in medicine & biology.

[22]  K. Doi,et al.  Potential of computer-aided diagnosis to reduce variability in radiologists' interpretations of mammograms depicting microcalcifications. , 2001, Radiology.

[23]  J. Thijssen,et al.  Characterization of echographic image texture by cooccurrence matrix parameters. , 1997, Ultrasound in medicine & biology.

[24]  Y. Chou,et al.  Stepwise logistic regression analysis of tumor contour features for breast ultrasound diagnosis. , 2001, Ultrasound in Medicine and Biology.