MRI texture features from tumor core and margin in the prediction of response to neoadjuvant chemotherapy in patients with locally advanced breast cancer
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Christopher Kolios1,2,3,4, Lakshmanan Sannachi1,2,3,4, Archya Dasgupta1,2,4, Harini Suraweera1,2, Daniel DiCenzo1,2, Gregory Stanisz1,3, Arjun Sahgal2,4, Frances Wright5,6, Nicole Look-Hong5,6, Belinda Curpen7,8, Ali Sadeghi-Naini9, Maureen Trudeau10,11, Sonal Gandhi10,11, Michael C. Kolios12 and Gregory J. Czarnota1,2,3,4,12
1 Physical Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, Canada
2 Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada
3 Department of Medical Biophysics, University of Toronto, Toronto, Canada
4 Department of Radiation Oncology, University of Toronto, Toronto, Canada
5 Department of Surgery, Sunnybrook Health Sciences Centre, Toronto, Canada
6 Department of Surgery, University of Toronto, Toronto, Canada
7 Department of Medical Imaging, Sunnybrook Health Sciences Centre, Toronto, Canada
8 Department of Medical Imaging, University of Toronto, Toronto, Canada
9 Department of Electrical and Computer Engineering, York University, North York, Canada
10 Division of Medical Oncology, Department of Medicine, Sunnybrook Health Sciences Centre, Toronto, Canada
11 Department of Medicine, University of Toronto, Toronto, Canada
12 Department of Physics, Ryerson University, Toronto, Canada
|Gregory J. Czarnota,||email:||Gregory.Czarnota@sunnybrook.ca|
Keywords: radiomics; MRI; breast cancer; neoadjuvant chemotherapy; biomarkers
Received: April 05, 2021 Accepted: June 11, 2021 Published: July 06, 2021
Background: Radiomics involving quantitative analysis of imaging has shown promises in oncology to serve as non-invasive biomarkers. We investigated whether pre-treatment T2-weighted magnetic resonance imaging (MRI) can be used to predict response to neoadjuvant chemotherapy (NAC) in breast cancer.
Materials and Methods: MRI scans were obtained for 102 patients with locally advanced breast cancer (LABC). All patients were treated with standard regimens of NAC as decided by the treating oncologist, followed by surgery and adjuvant treatment according to standard institutional practice. The primary tumor was segmented, and 11 texture features were extracted using the grey-level co-occurrence matrices analysis of the T2W-images from tumor cores and margins. Response assessment was done using clinical-pathological responses with patients classified into binary groups: responders and non-responders. Machine learning classifiers were used to develop a radiomics model, and a leave-one-out cross-validation technique was used to assess the performance.
Results: 7 features were significantly (p < 0.05) different between the two response groups. The best classification accuracy was obtained using a k-nearest neighbor (kNN) model with sensitivity, specificity, accuracy, and area under curve of 63, 93, 87, and 0.78, respectively.
Conclusions: Pre-treatment T2-weighted MRI texture features can predict NAC response with reasonable accuracy.
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