1. Quantitative ultrasound radiomics in predicting response to neoadjuvant chemotherapy in patients with locally advanced breast cancer: Results from multi-institutional study
- Author
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Michael C. Kolios, Kashuf Fatima, Daniel DiCenzo, Greg J. Stanisz, Nicole J. Look Hong, Lakshmanan Sannachi, Divya Bhardwaj, Andrea Eisen, William T. Tran, Karina Quiaoit, Robert Dinniwell, Belinda Curpen, Frances C. Wright, Sonal Gandhi, Maureen E. Trudeau, Christine B. Brezden, Mehrdad J. Gangeh, Archya Dasgupta, Wei Yang, Gregory J. Czarnota, Arjun Sahgal, and Ali Sadeghi-Naini
- Subjects
Male ,0301 basic medicine ,Cancer Research ,Imaging biomarker ,medicine.medical_treatment ,quantitative ultrasound ,0302 clinical medicine ,Radiomics ,Antineoplastic Combined Chemotherapy Protocols ,Prospective Studies ,texture analysis ,Original Research ,Ultrasonography ,Ethics committee ,Middle Aged ,lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens ,Neoadjuvant Therapy ,3. Good health ,Quantitative ultrasound ,Treatment Outcome ,machine learning ,Oncology ,Chemotherapy, Adjuvant ,radiomics ,030220 oncology & carcinogenesis ,Female ,Radiology ,Algorithms ,neoadjuvant chemotherapy ,Adult ,Canada ,medicine.medical_specialty ,Locally advanced ,Breast Neoplasms ,Sensitivity and Specificity ,lcsh:RC254-282 ,03 medical and health sciences ,locally advanced breast cancer ,Breast cancer ,medicine ,Humans ,imaging biomarker ,Radiology, Nuclear Medicine and imaging ,In patient ,Aged ,Chemotherapy ,business.industry ,Clinical Cancer Research ,medicine.disease ,United States ,030104 developmental biology ,response prediction ,business - 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., This multi‐institutional study investigated the role of radiomics from quantitative ultrasound (QUS) in predicting the final response to neoadjuvant chemotherapy (NAC) for 82 patients with locally advanced breast cancer (LABC). We had shown the QUS‐radiomics model can predict the response to treatment with an accuracy of 87% from spectroscopic features obtained before the start of NAC.
- Published
- 2022
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