TY - GEN
T1 - Analysis of Microwave Scans of Cancer Patients for Classification of Treated and Untreated Tissue
AU - Garland, Anita
AU - Oliveira, Helder
AU - Yanushkevich, Svetlana
AU - Smith, Katrin
AU - Bourqui, Jeremie
AU - Fear, Elise
AU - Quirk, Sarah
AU - Roumeliotis, Michael
AU - Grendarova, Petra
AU - Pinilla, James
AU - Lesiuk, Mark
AU - Gourley, Alison
N1 - Funding Information:
ACKNOWLEDGMENT We are grateful to the generous funding from the National Sciences and Engineering Research Council of Canada (NSERC) and the Alberta Cancer Foundation, the staff at the Tom Baker Cancer Centre who dedicated their time to collect patient data as well as the patients who participated in this study.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - This paper examines the feasibility of detecting short-term changes in tissue related to cancer treatment. 15 female patients enrolled in a clinical trial, who underwent lumpectomy and accelerated partial breast radiotherapy, were recruited. The patients were scanned using a microwave imaging system at 2 time points-the first scan is at least 8 weeks after surgery but before radiotherapy began, and the second scan is at 6 weeks after the conclusion of radiotherapy. The permittivity or the dielectric property of breast tissue was estimated from the scan data and used for initial analysis. Next, wavelet transforms were applied to the time-domain response to extract features that were then classified. The Discrete Wavelet Transform (DWT) and the Dual Tree Complex Wavelet transform (DTCWT) were used to extract meaningful features that were evaluated using the Support Vector Machine (SVM), Random Forest (RF) and the Gradient Boosting classifier (GBC). After hyperparameter tuning of the classifiers, the experimental results show good accuracy, with SVM yielding the best results. In conclusion, the classification pipeline proposed for microwave imaging shows potential to track short-term changes in tissue as a result of cancer treatment.
AB - This paper examines the feasibility of detecting short-term changes in tissue related to cancer treatment. 15 female patients enrolled in a clinical trial, who underwent lumpectomy and accelerated partial breast radiotherapy, were recruited. The patients were scanned using a microwave imaging system at 2 time points-the first scan is at least 8 weeks after surgery but before radiotherapy began, and the second scan is at 6 weeks after the conclusion of radiotherapy. The permittivity or the dielectric property of breast tissue was estimated from the scan data and used for initial analysis. Next, wavelet transforms were applied to the time-domain response to extract features that were then classified. The Discrete Wavelet Transform (DWT) and the Dual Tree Complex Wavelet transform (DTCWT) were used to extract meaningful features that were evaluated using the Support Vector Machine (SVM), Random Forest (RF) and the Gradient Boosting classifier (GBC). After hyperparameter tuning of the classifiers, the experimental results show good accuracy, with SVM yielding the best results. In conclusion, the classification pipeline proposed for microwave imaging shows potential to track short-term changes in tissue as a result of cancer treatment.
KW - breast cancer
KW - classification
KW - machine learning
KW - microwave imaging
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U2 - 10.1109/CIBCB55180.2022.9863053
DO - 10.1109/CIBCB55180.2022.9863053
M3 - Conference contribution
AN - SCOPUS:85137874634
T3 - 2022 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2022
BT - 2022 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2022
Y2 - 15 August 2022 through 17 August 2022
ER -