@inproceedings{d9a79dc1babe4cdfa926c2da8ac28661,
title = "Multiparametric radiomics for predicting the aggressiveness of papillary thyroid carcinoma using hyperspectral images",
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.",
keywords = "Cancer, Head, Hyperspectral Imaging, Neck, Radiomics, Tissues, Tumor Aggression",
author = "Ka'Toria Edwards and Martin Halicek and Little, {James V.} and Chen, {Amy Y.} and Baowei Fei",
note = "Publisher Copyright: {\textcopyright} COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.; Medical Imaging 2021: Computer-Aided Diagnosis ; Conference date: 15-02-2021 Through 19-02-2021",
year = "2021",
doi = "10.1117/12.2582147",
language = "English (US)",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Mazurowski, {Maciej A.} and Karen Drukker",
booktitle = "Medical Imaging 2021",
}