Quantitative correlation between sonographic textural feature and histopathological components for breast cancer: preliminary results.

In this study, the sonographic texture and the histopathological features of breast cancer were objectively characterized. Textural dissimilarity is demonstrated to correlate well with the corresponding histopathological components. The normalized percentage of both fibrosis area and cellular area has highly linear correlation with the textural feature of dissimilarity. The correlation coefficients are -.880 and .857, respectively. The cancerous region with increased fibrous tissues shows low textural dissimilarity and has a strong tendency of negative correlation, whereas the cancerous region with increased cellularity exhibits high textural dissimilarity and a good positive correlation. These results have not been reported so far, and they can be used to predict cellular and fibrotic portions of breast cancer for biopsy or surgery planning, disease progression monitoring, and therapeutic effect evaluation. The proposed image analysis method may also be extended to similar characterization of cancerous tissue in other applications.

[1]  Alicia Samuels,et al.  Cancer Statistics, 2003 , 2003, CA: a cancer journal for clinicians.

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

[3]  I Zuna,et al.  Ultrasonographic tissue characterization in monitoring tumor response to neoadjuvant chemotherapy in locally advanced breast cancer (work in progress). , 2000, Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine.

[4]  Sung-Nien Yu,et al.  Quantitatively Characterizing the Textural Features of Sonographic Images for Breast Cancer With Histopathologic Correlation , 2005, Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine.

[5]  B. Fornage,et al.  Interventional breast sonography. , 2002, European journal of radiology.

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

[7]  Yung-Chang Chen,et al.  Statistical feature matrix for texture analysis , 1992, CVGIP Graph. Model. Image Process..

[8]  I Zuna,et al.  Relevance of sonographic B-mode criteria and computer-aided ultrasonic tissue characterization in differential/diagnosis of solid breast masses. , 2000, Ultrasound in medicine & biology.

[9]  Steven K. Feiner,et al.  Computer graphics: principles and practice (2nd ed.) , 1990 .

[10]  M. Carter.  Computer graphics: Principles and practice , 1997 .

[11]  Paul Wintz,et al.  Digital image processing (2nd ed.) , 1987 .