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

Names of Authors: Ka’Toria Edwards [1], Martin Halicek [1], James V. Little [2], Amy Y. Chen [3], Baowei Fei [1,4].* Faculty Mentor: Baowei Fei Department, Institution and Institutional Address with Zip: [1] Department of Bioengineering, University of Texas at Dallas, Richardson, TX 75081 [2] Department of Pathology and Laboratory Medicine, Emory University School of Medicine, Atlanta, GA 30322 [3] Department of Otolaryngology, Emory University School of Medicine, Atlanta, GA 30322 [4] Department of Radiology, The University of Texas Southwestern Medical Center, Dallas, TX 75390 *E-mail: bfei@utdallas.edu, website: https://fei-lab.org

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. 




Additional Abstract Information

Presenter: Ka'Toria Edwards

Institution: University of Texas at Dallas

Type: Poster

Subject: Computer Science

Status: Approved


Time and Location

Session: Poster 5
Date/Time: Tue 12:30pm-1:30pm
Session Number: 4056