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15 February 2021 Multiparametric radiomics for predicting the aggressiveness of papillary thyroid carcinoma using hyperspectral images
Ka'Toria Edwards, Martin Halicek, James V. Little, Amy Y. Chen, Baowei Fei
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Proceedings Volume 11597, Medical Imaging 2021: Computer-Aided Diagnosis; 1159728 (2021) https://doi.org/10.1117/12.2582147
Event: SPIE Medical Imaging, 2021, Online Only
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Abstract
Papillary thyroid carcinoma (PTC) is primarily treated by surgical resection. During surgery, surgeons often need intraoperative frozen analysis and pathologic consultation in order to detect PTC. In some cases pathologists cannot determine if the tumor is aggressive until the operation has been completed. In this work, we have taken tumor classification a step further by determining the tumor aggressiveness of fresh surgical specimens. We employed hyperspectral imaging (HSI) in combination with multiparametric radiomic features to complete this task. The study cohort includes 72 ex-vivo tissue specimens from 44 patients with pathology-confirmed PTC. A total of 67 features were extracted from this data. Using machine learning classification methods, we were able to achieve an AUC of 0.85. Our study shows that hyperspectral imaging and multiparametric radiomic features could aid in the pathological detection of tumor aggressiveness using fresh surgical spemens obtained during surgery.
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Ka'Toria Edwards, Martin Halicek, James V. Little, Amy Y. Chen, and Baowei Fei "Multiparametric radiomics for predicting the aggressiveness of papillary thyroid carcinoma using hyperspectral images", Proc. SPIE 11597, Medical Imaging 2021: Computer-Aided Diagnosis, 1159728 (15 February 2021); https://doi.org/10.1117/12.2582147
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KEYWORDS
Hyperspectral imaging
Tumors
Feature extraction
Machine learning
Feature selection
Image classification
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