Research Papers:

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

Laurentius O. Osapoetra, Lakshmanan Sannachi, Karina Quiaoit, Archya Dasgupta, Daniel DiCenzo, Kashuf Fatima, Frances Wright, Robert Dinniwell, Maureen Trudeau, Sonal Gandhi, William Tran, Michael C. Kolios, Wei Yang and Gregory J. Czarnota _

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Laurentius O. Osapoetra1,2,3,4, Lakshmanan Sannachi1,2,3,4, Karina Quiaoit1,2,3, Archya Dasgupta1,2,3, Daniel DiCenzo1,2,3, Kashuf Fatima1,2,3, Frances Wright5,6, Robert Dinniwell7,8,9, Maureen Trudeau10,11, Sonal Gandhi10,11, William Tran1,2,12, Michael C. Kolios13, Wei Yang14 and Gregory J. Czarnota1,2,3,4

1 Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, ON, Canada

2 Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada

3 Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada

4 Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada

5 Department of Surgical Oncology, Department of Surgery, Sunnybrook Health Sciences Centre, Toronto, ON, Canada

6 Department of Surgery, University of Toronto, Toronto, ON, Canada

7 Department of Radiation Oncology, Princess Margaret Hospital, University Health Network, Toronto, ON, Canada

8 Radiation Oncology, London Health Sciences Centre, London, ON, Canada

9 Department of Oncology, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada

10 Medical Oncology, Department of Medicine, Sunnybrook Health Sciences Centre, Toronto, ON, Canada

11 Department of Medicine, University of Toronto, Toronto, ON, Canada

12 Evaluative Clinical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada

13 Department of Physics, Ryerson University, Toronto, ON, Canada

14 Department of Diagnostic Radiology, University of Texas, Houston, Texas, USA

Correspondence to:

Gregory J. Czarnota,email: [email protected]

Keywords: radiomics; breast cancer; texture-derivate; quantitative ultrasound; neoadjuvant chemotherapy

Received: August 21, 2020     Accepted: December 29, 2020     Published: January 19, 2021

Copyright: © 2021 Osapoetra et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Purpose: We develop a multi-centric response predictive model using QUS spectral parametric imaging and novel texture-derivate methods for determining tumour responses to neoadjuvant chemotherapy (NAC) prior to therapy initiation.

Materials and Methods: QUS Spectroscopy provided parametric images of mid-band-fit (MBF), spectral-slope (SS), spectral-intercept (SI), average-scatterer-diameter (ASD), and average-acoustic-concentration (AAC) in 78 patients with locally advanced breast cancer (LABC) undergoing NAC. Ultrasound radiofrequency data were collected from Sunnybrook Health Sciences Center (SHSC), University of Texas MD Anderson Cancer Center (MD-ACC), and St. Michaels Hospital (SMH) using two different systems. Texture analysis was used to quantify heterogeneities of QUS parametric images. Further, a second-pass texture analysis was applied to obtain texture-derivate features. QUS, texture- and texture-derivate parameters were determined from both tumour core and a 5-mm tumour margin and were used in comparison to histopathological analysis for developing a response predictive model to classify responders versus non-responders. Model performance was assessed using leave-one-out cross-validation. Three standard classification algorithms including a linear discriminant analysis (LDA), k-nearest-neighbors (KNN), and support vector machines-radial basis function (SVM-RBF) were evaluated.

Results: A combination of tumour core and margin classification resulted in a peak response prediction performance of 88% sensitivity, 78% specificity, 84% accuracy, 0.86 AUC, 84% PPV, and 83% NPV, achieved using the SVM-RBF classification algorithm. Other parameters and classifiers performed less well running from 66% to 80% accuracy.

Conclusions: A QUS-based framework and novel texture-derivative method enabled accurate prediction of responses to NAC. Multi-centric response predictive model provides indications of the robustness of the approach to variations due to different ultrasound systems and acquisition parameters.

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