Quantitative ultrasound radiomics using texture derivatives in prediction of treatment response to neo-adjuvant chemotherapy for locally advanced breast cancer
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Archya Dasgupta1,2,3, Stephen Brade1, Lakshmanan Sannachi1,2,3, Karina Quiaoit1,2,3, Kashuf Fatima1,2,3, Daniel DiCenzo1,2,3, Laurentius O. Osapoetra1,2,3, Murtuza Saifuddin1,2,3, Maureen Trudeau4,5, Sonal Gandhi4,5, Andrea Eisen4,5, Frances Wright6,7, Nicole Look-Hong6,7, Ali Sadeghi-Naini1,3,8,9, William T. Tran1,2,10, Belinda Curpen11,12 and Gregory J. Czarnota1,2,3,8,9
1 Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada
2 Department of Radiation Oncology, University of Toronto, Toronto, Canada
3 Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
4 Department of Medical Oncology, Department of Medicine, Sunnybrook Health Sciences Centre, Toronto, Canada
5 Department of Medicine, University of Toronto, Toronto, Canada
6 Department of Surgical Oncology, Department of Surgery, Sunnybrook Health Sciences Centre, Toronto, Canada
7 Department of Surgery, University of Toronto, Toronto, Canada
8 Department of Medical Biophysics, University of Toronto, Toronto, Canada
9 Department of Electrical Engineering and Computer Sciences, Lassonde School of Engineering, York University, Toronto, Canada
10 Evaluative Clinical Sciences, Sunnybrook Research Institute, Toronto, Canada
11 Department of Medical Imaging, Sunnybrook Health Sciences Centre, Toronto, Canada
12 Department of Medical Imaging, University of Toronto, Toronto, Canada
|Gregory J. Czarnota,||email:||[email protected]|
Keywords: radiomics; breast cancer; texture derivatives; quantitative ultrasound; neoadjuvant chemotherapy
Received: May 18, 2020 Accepted: August 24, 2020 Published: October 20, 2020
Background: To investigate quantitative ultrasound (QUS) based higher-order texture derivatives in predicting the response to neoadjuvant chemotherapy (NAC) in patients with locally advanced breast cancer (LABC).
Materials and Methods: 100 Patients with LABC were scanned before starting NAC. Five QUS parametric image-types were generated from radio-frequency data over the tumor volume. From each QUS parametric-image, 4 grey level co-occurrence matrix-based texture images were derived (20 QUS-Tex1), which were further processed to create texture derivatives (80 QUS-Tex1-Tex2). Patients were classified into responders and non-responders based on clinical/pathological responses to treatment. Three machine learning algorithms based on linear discriminant (FLD), k-nearest-neighbors (KNN), and support vector machine (SVM) were used for developing radiomic models of response prediction.
Results: A KNN-model provided the best results with sensitivity, specificity, accuracy, and area under curve (AUC) of 87%, 81%, 82%, and 0.86, respectively. The most helpful features in separating the two response groups were QUS-Tex1-Tex2 features. The 5-year recurrence-free survival (RFS) calculated for KNN predicted responders and non-responders using QUS-Tex1-Tex2 model were comparable to RFS for the actual response groups.
Conclusions: We report the first study demonstrating QUS texture-derivative methods in predicting NAC responses in LABC, which leads to better results compared to using texture features alone.
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