Open AccessQuantitative ultrasound radiomics in predicting response to neoadjuvant chemotherapy in patients with locally advanced breast cancer: Results from multi‐institutional study
Author(s) -
DiCenzo Daniel,
Quiaoit Karina,
Fatima Kashuf,
Bhardwaj Divya,
Sannachi Lakshmanan,
Gangeh Mehrdad,
SadeghiNaini Ali,
Dasgupta Archya,
Kolios Michael C.,
Trudeau Maureen,
Gandhi Sonal,
Eisen Andrea,
Wright Frances,
Look Hong Nicole,
Sahgal Arjun,
Stanisz Greg,
Brezden Christine,
Dinniwell Robert,
Tran William T.,
Yang Wei,
Curpen Belinda,
Czarnota Gregory J.
Publication year - 2020
Publication title -
cancer medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.403
H-Index - 53
ISSN - 2045-7634
DOI - 10.1002/cam4.3255
Subject(s) - radiomics , medicine , breast cancer , ultrasound , neoadjuvant therapy , cross validation , radiology , cancer , artificial intelligence , computer science
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.