Breast Cancer Treatment Response Monitoring Using Quantitative Ultrasound and Texture Analysis: Comparative Analysis of Analytical Models

Sannachi, Lakshmanan; Gangeh, Mehrdad J.; Tadayyon, Hadi; Gandhi, Sonal; Wright, Frances C.; Slodkowska, Elzbieta; Curpen, Belinda; Sadeghi‐Naini, Ali; Tran, William T.; Czarnota, Gregory J.

doi:10.1016/j.tranon.2019.06.004

Cited by 35 publications

(58 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…We performed statistical analysis to demonstrate the presence of discriminating features for building a diagnostic model. In order to determine which statistical tests to use, a Shapiro-Wilk normality test was performed on each feature to decide if it followed a normal distribution [ 54 ]. For a normally distributed feature, an unpaired t -test was used.…”

Section: Methodsmentioning

confidence: 99%

“…In leave-one-out cross-validation, the classification model is trained using all observations except one. The left-out observation is used for testing the developed model [ 54 ]. The process is repeated until all observations are left out for testing at least once [ 54 ].…”

Section: Methodsmentioning

confidence: 99%

“…The left-out observation is used for testing the developed model [ 54 ]. The process is repeated until all observations are left out for testing at least once [ 54 ]. The trained classifier performance was assessed based on F1-Score (the harmonic average of precision and sensitivity).…”

Section: Methodsmentioning

confidence: 99%

“…The KNN is an instance-based learning algorithm that predicts class association of a test point in the feature space based on most of the points neighboring the test point and the distance between those points to the test point [ 33 ]. SVM-RBF performs nonlinear mapping of the input data into higher-dimensional space where the data are supposed to have better distribution, before selecting an optimal hyperplane that maximizes the margin between the two classes [ 33 , 54 ]. Class association of the test data is predicted based on which side of the gap they fall on [ 33 ].…”

Section: Methodsmentioning

confidence: 99%

See 3 more Smart Citations

Breast lesion characterization using Quantitative Ultrasound (QUS) and derivative texture methods

Osapoetra

Sannachi

DiCenzo

et al. 2020

Translational Oncology

Self Cite

View full text Add to dashboard Cite

Purpose Accurate and timely diagnosis of breast cancer is extremely important because of its high incidence and high morbidity. Early diagnosis of breast cancer through screening can improve overall prognosis. Currently, biopsy remains as the gold standard for tumor pathological confirmation. Development of diagnostic imaging techniques for rapid and accurate characterization of breast lesions is required. We aim to evaluate the usefulness of texture-derivate features of QUS spectral parametric images for non-invasive characterization of breast lesions. Methods QUS Spectroscopy was used to determine parametric images of mid-band fit (MBF), spectral slope (SS), spectral intercept (SI), average scatterer diameter (ASD), and average acoustic concentration (AAC) in 204 patients with suspicious breast lesions. Subsequently, texture analysis techniques were used to generate texture maps from parametric images to quantify heterogeneities of QUS parametric images. Further, a second-pass texture analysis was applied to obtain texture-derivate features. QUS parameters, texture-parameters and texture-derivate parameters were determined from both tumor core and a 5-mm tumor margin and were used in comparison to histopathological analysis in order to develop a diagnostic model for classifying breast lesions as either benign or malignant. Both leave-one-out and hold-out cross-validations were used to evaluate the performance of the diagnostic model. Three standard classification algorithms including a linear discriminant analysis (LDA), k -nearest neighbors (KNN), and support vector machines-radial basis function (SVM-RBF) were evaluated. Results Core and margin information using the SVM-RBF attained the best classification performance of 90% sensitivity, 92% specificity, 91% accuracy, and 0.93 AUC utilizing QUS parameters and their texture derivatives, evaluated using leave-one-out cross-validation. Implementation of hold-out cross-validation using combination of both core and margin information and SVM-RBF achieved average accuracy and AUC of 88% and 0.92, respectively. Conclusions QUS-based framework and derivative texture methods enable accurate classification of breast lesions. Evaluation of the proposed technique on a large cohort using hold-out cross-validation demonstrates its robustness and its generalization.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Breast lesion characterization using Quantitative Ultrasound (QUS) and derivative texture methods

Osapoetra

Sannachi

DiCenzo

et al. 2020

Translational Oncology

Self Cite

View full text Add to dashboard Cite

show abstract

“…The utilities of QUS spectroscopy have been demonstrated in the assessment of tumour responses to cancer therapies both pre-clinically and clinically [23][24][25][26][27], characterization of different types of tissues such as prostate, liver, and retina [28][29][30][31][32][33], determination of blood-clot and various intravascular plaque components [34][35][36], and detection of tumour deposits in ex vivo lymph nodes [37]. Spontaneously occurring mammary fibroadenomas (benign lesions) and mammary carcinomas (malignant lesions) have also been differentiated using QUS techniques pre-clinically [21].…”

Section: Introductionmentioning

confidence: 99%

Comparison of methods for texture analysis of QUS parametric images in the characterization of breast lesions

Osapoetra¹,

Chan²,

Tran³

et al. 2020

PLoS ONE

Self Cite

View full text Add to dashboard Cite

Purpose Accurate and timely diagnosis of breast carcinoma is very crucial because of its high incidence and high morbidity. Screening can improve overall prognosis by detecting the disease early. Biopsy remains as the gold standard for pathological confirmation of malignancy and tumour grading. The development of diagnostic imaging techniques as an alternative for the rapid and accurate characterization of breast masses is necessitated. Quantitative ultrasound (QUS) spectroscopy is a modality well suited for this purpose. This study was carried out to evaluate different texture analysis methods applied on QUS spectral parametric images for the characterization of breast lesions. Methods Parametric images of mid-band-fit (MBF), spectral-slope (SS), spectral-intercept (SI), average scatterer diameter (ASD), and average acoustic concentration (AAC) were determined using QUS spectroscopy from 193 patients with breast lesions. Texture methods were used to quantify heterogeneities of the parametric images. Three statistical-based approaches for texture analysis that include Gray Level Co-occurrence Matrix (GLCM), Gray Level Run-length Matrix (GRLM), and Gray Level Size Zone Matrix (GLSZM) methods were evaluated. QUS and texture-parameters were determined from both tumour core and a 5-mm tumour margin and were used in comparison to histopathological analysis in order to classify breast lesions as either benign or malignant. We developed a diagnostic model using different classification algorithms including linear discriminant analysis (LDA), k-nearest neighbours (KNN), support vector machine with radial basis function kernel (SVM-RBF), and an artificial neural network (ANN). Model performance was evaluated using leave-one-out cross-validation (LOOCV) and hold-out validation. Results Classifier performances ranged from 73% to 91% in terms of accuracy dependent on tumour margin inclusion and classifier methodology. Utilizing information from tumour core alone, the ANN achieved the best classification performance of 93% sensitivity, 88% specificity, 91% accuracy, 0.95 AUC using QUS parameters and their GLSZM texture features. Conclusions A QUS-based framework and texture analysis methods enabled classification of breast lesions with >90% accuracy. The results suggest that optimizing method for extracting discriminative textural features from QUS spectral parametric images can improve classification performance. Evaluation of the proposed technique on a larger cohort of patients with proper validation technique demonstrated the robustness and generalization of the approach.

show abstract

Quantitative ultrasound radiomics in predicting response to neoadjuvant chemotherapy in patients with locally advanced breast cancer: Results from multi‐institutional study

et al. 2020

Self Cite

View full text Add to dashboard Cite

Background This study was conducted in order to develop a model for predicting response to neoadjuvant chemotherapy (NAC) in patients with locally advanced breast cancer (LABC) using pretreatment quantitative ultrasound (QUS) radiomics. Methods This was a multicenter study involving four sites across North America, and appropriate approval was obtained from the individual ethics committees. Eighty‐two patients with LABC were included for final analysis. Primary tumors were scanned using a clinical ultrasound system before NAC was started. The tumors were contoured, and radiofrequency data were acquired and processed from whole tumor regions of interest. QUS spectral parameters were derived from the normalized power spectrum, and texture analysis was performed based on six QUS features using a gray level co‐occurrence matrix. Patients were divided into responder or nonresponder classes based on their clinical‐pathological response. Classification analysis was performed using machine learning algorithms, which were trained to optimize classification accuracy. Cross‐validation was performed using a leave‐one‐out cross‐validation method. Results Based on the clinical outcomes of NAC treatment, there were 48 responders and 34 nonresponders. A K ‐nearest neighbors ( K‐ NN) approach resulted in the best classifier performance, with a sensitivity of 91%, a specificity of 83%, and an accuracy of 87%. Conclusion QUS‐based radiomics can predict response to NAC based on pretreatment features with acceptable accuracy.

show abstract

Breast Cancer Treatment Response Monitoring Using Quantitative Ultrasound and Texture Analysis: Comparative Analysis of Analytical Models

Cited by 35 publications

References 36 publications

Breast lesion characterization using Quantitative Ultrasound (QUS) and derivative texture methods

Breast lesion characterization using Quantitative Ultrasound (QUS) and derivative texture methods

Comparison of methods for texture analysis of QUS parametric images in the characterization of breast lesions

Quantitative ultrasound radiomics in predicting response to neoadjuvant chemotherapy in patients with locally advanced breast cancer: Results from multi‐institutional study

Contact Info

Product

Resources

About