Multiparametric Radiomics for Predicting the Aggressiveness of Papillary Thyroid Carcinoma Using Hyperspectral Images

• Type of Material: Article
• Authors: Ka'Toria Edwards, University of Texas Dallas; Martin Halicek, University of Texas Dallas; James Little, Emory University; Amy Chen, Emory University; Baowei Fei, Emory University
• Language: English
• Date: 2021-01-01
• Publisher: SPIE-INT SOC OPTICAL ENGINEERING
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © (2021) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).
• Final Published Version (URL): http://dx.doi.org/10.1117/12.2582147
• Title of Journal or Parent Work: MEDICAL IMAGING 2021: COMPUTER-AIDED DIAGNOSIS
• Volume: 11597
• Grant/Funding Information: This research was supported in part by the U.S. National Institutes of Health (NIH) grants (R01CA156775, R01CA204254, R01HL140325, and R21CA231911) and by the Cancer Prevention and Research Institute of Texas (CPRIT) grant RP190588.
• Keywords: Tissues; Science & Technology; Engineering; CANCER; Neck; Imaging Science & Photographic Technology; Engineering, Biomedical; Radiomics; Physical Sciences; Technology; Hyperspectral Imaging; Tumor Aggression; Head; Optics; Cancer

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.