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

Autor: 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, Ali Sadeghi-Naini
Rok vydání: 2022
Předmět:
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
Zdroj: Cancer Medicine, Vol 9, Iss 16, Pp 5798-5806 (2020)
Cancer Medicine
Popis: 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.
Databáze: OpenAIRE
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