Quantitative ultrasound characterization of locally advanced breast cancer by estimation of its scatterer properties

2014 ◽  
Vol 41 (1) ◽  
pp. 012903 ◽  
Author(s):  
Hadi Tadayyon ◽  
Ali Sadeghi-Naini ◽  
Lauren Wirtzfeld ◽  
Frances C. Wright ◽  
Gregory Czarnota
Keyword(s):  
Breast Cancer ◽  
Advanced Breast Cancer ◽  
Quantitative Ultrasound ◽  
Locally Advanced Breast Cancer ◽  
Locally Advanced ◽  
Advanced Breast

Characterization of HER1 (c-erbB1) Status in Locally Advanced Breast Cancer Using Fluorescence in situ Hybridization and Immunohistochemistry

Tumor Biology ◽  
2005 ◽  
Vol 26 (1) ◽  
pp. 25-30 ◽  
Author(s):  
Cristina Corzo ◽  
Ignacio Tusquets ◽  
Marta Salido ◽  
Josep Maria Corominas ◽  
Meritxell Bellet ◽  
...  
Keyword(s):  
Breast Cancer ◽  
In Situ Hybridization ◽  
Advanced Breast Cancer ◽  
Fluorescence In Situ Hybridization ◽  
Locally Advanced Breast Cancer ◽  
Locally Advanced ◽  
Advanced Breast

scholarly journals Radiomics in Predicting Recurrence for Patients with Locally Advanced Breast Cancer using Quantitative Ultrasound

2020 ◽  
Author(s):  
Archya Dasgupta ◽  
Divya Bhardwaj ◽  
Daniel DiCenzo ◽  
Kashuf Fatima ◽  
Laurentiusoscar Osapoetra ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Advanced Breast Cancer ◽  
Quantitative Ultrasound ◽  
Locally Advanced Breast Cancer ◽  
Locally Advanced ◽  
Disease Recurrence ◽  
Advanced Breast ◽  
Support Vector ◽  
Svm Classifier ◽  
Imaging Features

Abstract Background The purpose of the study was to investigate the role of pre-treatment quantitative ultrasound (QUS)-radiomics in predicting recurrence for patients with locally advanced breast cancer (LABC). Methods A prospective study was conducted with patients with LABC (n=83). Primary tumours were scanned using a clinical ultrasound device before starting treatment. Ninety-five imaging features were extracted-spectral features, texture, and texture-derivatives. Patients were determined to have recurrence or no recurrence based on clinical outcomes. Machine learning classifiers with k-nearest neighbour (KNN) and support vector machine (SVM) were evaluated for model development using a maximum of 3 features and leave-one-out cross-validation.Results With a median follow up of 69 months (range 7-118 months), 28 patients had disease recurrence (local or distant). The best classification results were obtained using an SVM classifier with a sensitivity, specificity, accuracy and area under curve of 71%, 87%, 82%, and 0.76, respectively. Using the SVM model for the predicted non-recurrence and recurrence groups, the estimated 5-year recurrence-free survival was 83% and 54% (p=0.003), and the predicted 5-year overall survival was 85% and 74% (p=0.083), respectively.Conclusion A QUS-radiomics model using higher-order texture derivatives can predict patients with LABC at higher risk of disease recurrence before starting treatment.

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