Poster + Presentation + Paper
4 April 2022 Detecting aggressive papillary thyroid carcinoma using hyperspectral imaging and radiomic features
Author Affiliations +
Conference Poster
Abstract
Hyperspectral imaging (HSI) and radiomics have the potential to improve the accuracy of tumor malignancy prediction and assessment. In this work, we extracted radiomic features of fresh surgical papillary thyroid carcinoma (PTC) specimen that were imaged with HSI. A total of 107 unique radiomic features were extracted. This study includes 72 ex-vivo tissue specimens from 44 patients with pathology-confirmed PTC. With the dilated hyperspectral images, the shape feature of least axis length was able to predict the tumor aggressiveness with a high accuracy. The HSI-based radiomic method may provide a useful tool to aid oncologists in determining tumors with intermediate to high risk and in clinical decision making.
Conference Presentation
© (2022) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Ka'Toria Leitch, Martin Halicek, Maysam Shahedi, James V. Little, Amy Y. Chen, and Baowei Fei "Detecting aggressive papillary thyroid carcinoma using hyperspectral imaging and radiomic features", Proc. SPIE 12033, Medical Imaging 2022: Computer-Aided Diagnosis, 1203322 (4 April 2022); https://doi.org/10.1117/12.2611842
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KEYWORDS
Tumors

Tissues

Feature selection

Hyperspectral imaging

Feature extraction

Cancer

Lymphatic system

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