Quantitative ultrasound assessment of breast tumor response to chemotherapy using a multi-parameter approach
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Hadi Tadayyon1,2, Lakshmanan Sannachi1,2,3,4, Mehrdad Gangeh1,2,3,4, Ali Sadeghi-Naini1,2,3,4, William Tran3, Maureen E. Trudeau5, Kathleen Pritchard5, Sonal Ghandi5, Sunil Verma5, Gregory J. Czarnota1,2,3,4
1Physical Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
2Department of Medical Biophysics, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
3Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
4Department of Radiation Oncology, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
5Division of Medical Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
Gregory J. Czarnota, email: Gregory.email@example.com
Keywords: quantitative ultrasound, tissue characterization, breast cancer, breast cancer chemotherapy, tumor response assessment
Received: September 18, 2015 Accepted: March 28, 2016 Published: April 20, 2016
Purpose: This study demonstrated the ability of quantitative ultrasound (QUS) parameters in providing an early prediction of tumor response to neoadjuvant chemotherapy (NAC) in patients with locally advanced breast cancer (LABC).
Methods: Using a 6-MHz array transducer, ultrasound radiofrequency (RF) data were collected from 58 LABC patients prior to NAC treatment and at weeks 1, 4, and 8 of their treatment, and prior to surgery. QUS parameters including midband fit (MBF), spectral slope (SS), spectral intercept (SI), spacing among scatterers (SAS), attenuation coefficient estimate (ACE), average scatterer diameter (ASD), and average acoustic concentration (AAC) were determined from the tumor region of interest. Ultrasound data were compared with the ultimate clinical and pathological response of the patient’s tumor to treatment and patient recurrence-free survival.
Results: Multi-parameter discriminant analysis using the κ-nearest-neighbor classifier demonstrated that the best response classification could be achieved using the combination of MBF, SS, and SAS, with an accuracy of 60 ± 10% at week 1, 77 ± 8% at week 4 and 75 ± 6% at week 8. Furthermore, when the QUS measurements at each time (week) were combined with pre-treatment (week 0) QUS values, the classification accuracies improved (70 ± 9% at week 1, 80 ± 5% at week 4, and 81 ± 6% at week 8). Finally, the multi-parameter QUS model demonstrated a significant difference in survival rates of responding and non-responding patients at weeks 1 and 4 (p=0.035, and 0.027, respectively).
Conclusion: This study demonstrated for the first time, using new parameters tested on relatively large patient cohort and leave-one-out classifier evaluation, that a hybrid QUS biomarker including MBF, SS, and SAS could, with relatively high sensitivity and specificity, detect the response of LABC tumors to NAC as early as after 4 weeks of therapy. The findings of this study also suggested that incorporating pre-treatment QUS parameters of a tumor improved the classification results. This work demonstrated the potential of QUS and machine learning methods for the early assessment of breast tumor response to NAC and providing personalized medicine with regards to the treatment planning of refractory patients.
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