A priori prediction of breast tumour response to chemotherapy using quantitative ultrasound imaging and artificial neural networks
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Hadi Tadayyon1,2, Mehrdad Gangeh1,2, Lakshmanan Sannachi1,2, Maureen Trudeau3, Kathleen Pritchard3, Sonal Ghandi3, Andrea Eisen3, Nicole Look-Hong4, Claire Holloway4, Frances Wright4, Eileen Rakovitch5,6, Danny Vesprini5,6, William Tyler Tran5,6, Belinda Curpen7 and Gregory Czarnota1,2,3,5,6
1 Physical Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
2 Department of Medical Biophysics, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
3 Division of Medical Oncology, Department of Medicine, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
4 Surgical Oncology, Department of Surgery, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
5 Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
6 Department of Radiation Oncology, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
7 Department of Medical Imaging, Sunnybrook Health Sciences Centre, and Faculty of Medicine, University of Toronto, Toronto, ON, Canada
Keywords: quantitative ultrasound; artificial neural networks; ultrasound spectroscopy; tumour response assessment; prognostic biomarker
Received: November 26, 2018 Accepted: May 13, 2019 Published: June 11, 2019
We demonstrate the clinical utility of combining quantitative ultrasound (QUS) imaging of the breast with an artificial neural network (ANN) classifier to predict the response of breast cancer patients to neoadjuvant chemotherapy (NAC) administration prior to the start of treatment.
Using a 6 MHz ultrasound system, radiofrequency (RF) ultrasound data were acquired from 100 patients with biopsy-confirmed locally advanced breast cancer prior to the start of NAC. Quantitative ultrasound mean parameter intensity and texture features were computed from the tumour core and margin, and were compared to the clinical/pathological response and 5-year recurrence-free survival (RFS) of patients. A multi-parametric QUS model in conjunction with an ANN classifier predicted patient response with 96 ± 6% accuracy, and a 0.96 ± 0.08 area under the receiver operating characteristic curve (AUC), compared to 65 ± 10 % accuracy and 0.67 ± 0.14 AUC achieved using a K-Nearest Neighbour (KNN) algorithm. A separate ANN model predicted patient RFS with 85 ± 7% accuracy, and a 0.89 ± 0.11 AUC, whereas the KNN methodology achieved a 58 ± 6 % accuracy and a 0.64 ± 0.09 AUC.
The application of ANN for classifying patient response based on tumour QUS features performs well in terms of predicting response to chemotherapy. The findings here provide a framework for developing personalized a priori chemotherapy selection for patients that are candidates for NAC, potentially resulting in improved patient treatment outcomes and prognosis.
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