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, 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.
Abstract
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. Copyright:
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. Copyright: