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International Journal of Computer Assisted Radiology and Surgery

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02-11-2021 | Original Article

Cystic (including atypical) and solid breast lesion classification using the different features of quantitative ultrasound parametric images

Authors: A. A. Kolchev, D. V. Pasynkov, I. A. Egoshin, I. V. Kliouchkin, O. O. Pasynkova

Published in: International Journal of Computer Assisted Radiology and Surgery | Issue 2/2022

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Abstract

Purpose

The amount of ultrasound (US) breast examinations continues to grow rapidly because of the wider endorsement of breast cancer screening programs. Cysts are the most commonly diagnosed breast lesions. Atypical breast cysts can be a serious differentiation problem in the US. Our goal was to develop noninvasive automated US grayscale image analysis for the cystic and solid breast lesion differentiation based on mathematical image post-processing.

Materials and methods

We used a set of 217 ultrasound images of proven 107 cystic (including 53 atypical) and 110 solid lesions. Empirical statistical and morphological models of the lesions were used to obtain features. The AUC indicator and Student’s t test were used to assess the quality of the individual features. The Pearson correlation matrix was used to calculate the correlation between various features. The LASSO and stepwise regression methods were used to determine the most significant features. Finally, the lesion classification was carried out by the various methods.

Results

The use of LASSO regression for the feature selection made it possible to select the most significant features for classification. The sensitivity increased from 87.1% to 89.2% and the specificity—from 92.2 to 94.8%. After the correlation matrix construction, it was found that features with a high value of the correlation coefficient (0.72; 0.75) can also be used to improve the quality of the classification.

Conclusion

The construction of the empirical model of the lesion pixels brightness behavior can provide parameters that are important for the correct classification of ultrasound images. The optimal set of features with the maximum discriminant characteristics may not be consistent with the correlation of features and the value of the AUC index. Features with a low AUC index (in our case 0.72) can also be important for improving the quality of the classification.

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Title: Cystic (including atypical) and solid breast lesion classification using the different features of quantitative ultrasound parametric images
Authors: A. A. Kolchev
D. V. Pasynkov
I. A. Egoshin
I. V. Kliouchkin
O. O. Pasynkova
Publication date: 02-11-2021
Publisher: Springer International Publishing
Published in: International Journal of Computer Assisted Radiology and Surgery / Issue 2/2022
Print ISSN: 1861-6410
Electronic ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-021-02522-x

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Abstract

Purpose

Materials and methods

Results

Conclusion

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