Radiomics in predicting recurrence for patients with locally advanced breast cancer using quantitative ultrasound
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Archya Dasgupta1,2,3, Divya Bhardwaj3, Daniel DiCenzo3, Kashuf Fatima3, Laurentius Oscar Osapoetra3, Karina Quiaoit3, Murtuza Saifuddin3, Stephen Brade3, Maureen Trudeau4,5, Sonal Gandhi4,5, Andrea Eisen4,5, Frances Wright6,7, Nicole Look-Hong6,7, Ali Sadeghi-Naini1,3,8,9, Belinda Curpen10,11, Michael C. Kolios12, Lakshmanan Sannachi3 and Gregory J. Czarnota1,2,3,8
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 Department of Medical Imaging, Sunnybrook Health Sciences Centre, Toronto, Canada
11 Department of Medical Imaging, University of Toronto, Toronto, Canada
12 Department of Physics, Ryerson University, Toronto, Canada
|Gregory J. Czarnota,||email:||email@example.com|
Keywords: radiomics; breast cancer; quantitative ultrasound; recurrence; machine learning
Received: August 23, 2021 Accepted: November 10, 2021 Published: December 07, 2021
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).
Materials and Methods: A prospective study was conducted in 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.
Conclusions: A QUS-radiomics model using higher-order texture derivatives can identify patients with LABC at higher risk of disease recurrence before starting treatment.
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