Oncotarget | Pre-Treatment Ultrasound Used to Predict Breast Cancer Recurrence
Channel: Oncotarget
Date: 2021-12-22

Oncotarget published this trending research paper in Volume 12, Issue 25, entitled, "Radiomics in predicting recurrence for patients with locally advanced breast cancer using quantitative ultrasound" by researchers from the Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Canada; Physical Sciences, Sunnybrook Research Institute, Toronto, Canada; Department of Medical Oncology, Department of Medicine, Sunnybrook Health Sciences Centre, Toronto, Canada; Department of Medicine, University of Toronto, Toronto, Canada; Department of Surgical Oncology, Department of Surgery, Sunnybrook Health Sciences Centre, Toronto, Canada; Department of Surgery, University of Toronto, Toronto, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Canada; Department of Electrical Engineering and Computer Sciences, Lassonde School of Engineering, York University, Toronto, Canada; Department of Medical Imaging, Sunnybrook Health Sciences Centre, Toronto, Canada; Department of Medical Imaging, University of Toronto, Toronto, Canada; Department of Physics, Ryerson University, Toronto, Canada.

Abstract:
Background: The purpose of the study was to investigate the role of pre-treatment quantitative ultrasound (QUS)-radiomics in predicting recurrence for patients with locally advanced breast cancer (LABC).

Materials and Methods: A prospective study was conducted in patients with LABC (n = 83). Primary tumours were scanned using a clinical ultrasound device before starting treatment. Ninety-five imaging features were extracted-spectral features, texture, and texture-derivatives. Patients were determined to have recurrence or no recurrence based on clinical outcomes. Machine learning classifiers with k-nearest neighbour (KNN) and support vector machine (SVM) were evaluated for model development using a maximum of 3 features and leave-one-out cross-validation.

Results: With a median follow up of 69 months (range 7–118 months), 28 patients had disease recurrence (local or distant). The best classification results were obtained using an SVM classifier with a sensitivity, specificity, accuracy and area under curve of 71%, 87%, 82%, and 0.76, respectively. Using the SVM model for the predicted non-recurrence and recurrence groups, the estimated 5-year recurrence-free survival was 83% and 54% (p = 0.003), and the predicted 5-year overall survival was 85% and 74% (p = 0.083), respectively.

Conclusions: A QUS-radiomics model using higher-order texture derivatives can identify patients with LABC at higher risk of disease recurrence before starting treatment.

Press release - https://www.oncotarget.com/news/pr/radiomics-for-patients-with-breast-cancer-using-ultrasound/

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DOI - https://doi.org/10.18632/oncotarget.28139

Full text - https://www.oncotarget.com/article/28139/text/

Correspondence to - Gregory J. Czarnota - gregory.czarnota@sunnybrook.ca

Keywords - radiomics, breast cancer, quantitative ultrasound, recurrence, machine learning

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