Response monitoring using quantitative ultrasound methods and supervised dictionary learning in locally advanced breast cancer

2016 ◽  
Author(s):  
Mehrdad J. Gangeh ◽  
Brandon Fung ◽  
Hadi Tadayyon ◽  
William T. Tran ◽  
Gregory J. Czarnota
Keyword(s):  
Breast Cancer ◽  
Advanced Breast Cancer ◽  
Dictionary Learning ◽  
Quantitative Ultrasound ◽  
Locally Advanced Breast Cancer ◽  
Locally Advanced ◽  
Advanced Breast ◽  
Response Monitoring ◽  
Supervised Dictionary Learning

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.

scholarly journals A priori prediction of response in multicentre locally advanced breast cancer (LABC) patients using quantitative ultrasound and derivative texture methods

Oncotarget ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 81-94
Author(s):  
Laurentius O. Osapoetra ◽  
Lakshmanan Sannachi ◽  
Karina Quiaoit ◽  
Archya Dasgupta ◽  
Daniel DiCenzo ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Advanced Breast Cancer ◽  
Quantitative Ultrasound ◽  
Locally Advanced Breast Cancer ◽  
A Priori ◽  
Locally Advanced ◽  
Advanced Breast ◽  
Prediction Of Response
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