Quantitative ultrasound radiomics in predicting response to neoadjuvant chemotherapy in patients with locally advanced breast cancer: Results from multi‐institutional study

• Main Authors: Daniel DiCenzo, Karina Quiaoit, Kashuf Fatima, Divya Bhardwaj, Lakshmanan Sannachi, Mehrdad Gangeh, Ali Sadeghi‐Naini, Archya Dasgupta, Michael C. Kolios, Maureen Trudeau, Sonal Gandhi, Andrea Eisen, Frances Wright, Nicole Look Hong, Arjun Sahgal, Greg Stanisz, Christine Brezden, Robert Dinniwell, William T. Tran, Wei Yang, Belinda Curpen, Gregory J. Czarnota
• Format: Article
• Language: English
• Published: Wiley 2020-08-01
• Series: Cancer Medicine
• Subjects: imaging biomarker; locally advanced breast cancer; machine learning; neoadjuvant chemotherapy; quantitative ultrasound; radiomics
• Online Access: https://doi.org/10.1002/cam4.3255

Abstract Background This study was conducted in order to develop a model for predicting response to neoadjuvant chemotherapy (NAC) in patients with locally advanced breast cancer (LABC) using pretreatment quantitative ultrasound (QUS) radiomics. Methods This was a multicenter study involving four sites across North America, and appropriate approval was obtained from the individual ethics committees. Eighty‐two patients with LABC were included for final analysis. Primary tumors were scanned using a clinical ultrasound system before NAC was started. The tumors were contoured, and radiofrequency data were acquired and processed from whole tumor regions of interest. QUS spectral parameters were derived from the normalized power spectrum, and texture analysis was performed based on six QUS features using a gray level co‐occurrence matrix. Patients were divided into responder or nonresponder classes based on their clinical‐pathological response. Classification analysis was performed using machine learning algorithms, which were trained to optimize classification accuracy. Cross‐validation was performed using a leave‐one‐out cross‐validation method. Results Based on the clinical outcomes of NAC treatment, there were 48 responders and 34 nonresponders. A K‐nearest neighbors (K‐NN) approach resulted in the best classifier performance, with a sensitivity of 91%, a specificity of 83%, and an accuracy of 87%. Conclusion QUS‐based radiomics can predict response to NAC based on pretreatment features with acceptable accuracy.