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Breast tumor classification using different features of quantitative ultrasound parametric images

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Rationale and objectives

The ultrasound B-mode-based morphological and texture analysis and Nakagami parametric imaging have been proposed to characterize breast tumors. Since these three feature categories of ultrasonic tissue characterization supply information on different physical characteristics of breast tumors, by combining the above methods is expected to provide more clues for classifying breast tumors.

Materials and methods

To verify the validity of the concept, raw data were obtained from 160 clinical cases. Six different types of morphological-feature parameters, four texture features, and the Nakagami parameter of benignancy and malignancy were extracted for evaluation. The Pearson’s correlation matrix was used to calculate the correlation between different feature parameters. The fuzzy c-means clustering and stepwise regression techniques were utilized to determine the optimal feature set, respectively. The logistic regression, receiver operating characteristic curve, and support vector machine were used to estimate the diagnostic ability.

Results

The best performance was obtained by combining morphological-feature parameter (e.g., standard deviation of the shortest distance), texture feature (e.g., variance), and the Nakagami parameter, with an accuracy of 89.4%, a specificity of 86.3%, a sensitivity of 92.5%, and an area under receiver operating characteristic curve of 0.96. There was no significant difference between using fuzzy c-means clustering, logistic regression, and support vector machine based on the optimal feature set for breast tumors classification.

Conclusion

Therefore, we verified that different physical ultrasonic features are functionally complementary and thus improve the performance in diagnosing breast tumors. Moreover, the optimal feature set had the maximum discriminating performance should be irrelative to the power of classifiers.

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Acknowledgements

This work was supported by grants from the National Tsing Hua University (100N2053E1) and the Hungkuang University and Kuang Tien General Hospital (HK-KTOH-105-04). This study was performed in accordance with the Helsinki Declaration and Good Clinical Practice.

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Authors and Affiliations

  1. Department of Radiology, Kuang Tien General Hospital, Taichung, Taiwan

    Soa-Min Hsu

  2. Department of Surgery, National Taiwan University Hospital, Taipei, Taiwan

    Wen-Hung Kuo

  3. Department of Physical Therapy, Hungkuang University, Taichung, Taiwan

    Fang-Chuan Kuo

  4. Department of Biomedical Engineering, Hungkuang University, 1018, Section 6, Taiwan Boulevard Road, Taichung, 43302, Taiwan, ROC

    Yin-Yin Liao

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  1. Soa-Min Hsu
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Correspondence to Yin-Yin Liao.

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Hsu, SM., Kuo, WH., Kuo, FC. et al. Breast tumor classification using different features of quantitative ultrasound parametric images. Int J CARS 14, 623–633 (2019). https://doi.org/10.1007/s11548-018-01908-8

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