Radiomic texture analysis for classification of radiofrequency ablated thyroid nodules

Sidharth K. Sengupta; Ana A. Araujo; Maisie L. Shindo; Brian J. Park

doi:10.1117/12.3008536

2 April 2024 Radiomic texture analysis for classification of radiofrequency ablated thyroid nodules

Sidharth K. Sengupta, Ana A. Araujo, Maisie L. Shindo, Brian J. Park

Proceedings Volume 12931, Medical Imaging 2024: Imaging Informatics for Healthcare, Research, and Applications; 129310S (2024) https://doi.org/10.1117/12.3008536
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

Radiofrequency ablation (RFA) with continuous ultrasonography (US) monitoring is a non-surgical alternative to traditional thyroid surgery for treating benign symptomatic thyroid nodules. Monitoring nodules over time through US imaging is used to determine procedural success, primarily indicated by measured volume reduction. These images also capture other rich clinical characteristics that we believed could be systematically interrogated across patients to better understand and stratify nodule response to RFA. We performed radiomic texture analysis on 56 preoperative and postoperative US thyroid nodule images from patients treated with RFA that generated 767 radiomic feature (RFs) measurements. Using dimensionality reduction and clustering of thyroid nodules by their US image texture features, unique populations of nodules were discovered that suggest these methods combined with radiomics texture analysis as a useful system for stratifying thyroid nodules. Additionally, individual texture features were found to be different between nodules with successful and unsuccessful outcomes, further supporting radiomics features as potential biomarkers for RFA-treated thyroid nodules.

Conference Presentation

Citation Download Citation

Sidharth K. Sengupta, Ana A. Araujo, Maisie L. Shindo, and Brian J. Park "Radiomic texture analysis for classification of radiofrequency ablated thyroid nodules", Proc. SPIE 12931, Medical Imaging 2024: Imaging Informatics for Healthcare, Research, and Applications, 129310S (2 April 2024); https://doi.org/10.1117/12.3008536

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