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      Combined clinical variable and radiomics of post-treatment total body scan for prediction of successful I-131 ablation in low-risk papillary thyroid carcinoma patients

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          Abstract

          To explore the feasibility of combined radiomics of post-treatment I-131 total body scan (TBS) and clinical parameter to predict successful ablation in low-risk papillary thyroid carcinoma (PTC) patients. Data of low-risk PTC patients who underwent total/near total thyroidectomy and I-131 ablation 30 mCi between April 2015 and July 2021 were retrospectively reviewed. The clinical factors studied included age, sex, and pre-ablative serum thyroglobulin (Tg). Radiomic features were extracted via PyRadiomics, and radiomic feature selection was performed. The predictive performance for successful ablation of the clinical parameter, radiomic, and combined models (radiomics combined with clinical parameter) was calculated using the area under the receiver operating characteristic curve (AUC). One hundred and thirty patients were included. Successful ablation was achieved in 77 patients (59.2%). The mean pre-ablative Tg in the unsuccessful group (15.50 ± 18.04 ng/ml) was statistically significantly higher than those in the successful ablation group (7.12 ± 7.15 ng/ml). The clinical parameter, radiomic, and combined models produced AUCs of 0.66, 0.77, and 0.87 in the training sets, and 0.65, 0.69, and 0.78 in the validation sets, respectively. The combined model produced a significantly higher AUC than that of the clinical parameter (p < 0.05). Radiomic analysis of the post-treatment TBS combined with pre-ablative serum Tg showed a significant improvement in the predictive performance of successful ablation in low-risk PTC patients compared to the use of clinical parameter alone.

          Thai Clinical Trials Registry TCTR identification number is TCTR20230816004 ( https://www.thaiclinicaltrials.org/show/TCTR20230816004).

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          Most cited references42

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          2015 American Thyroid Association Management Guidelines for Adult Patients with Thyroid Nodules and Differentiated Thyroid Cancer: The American Thyroid Association Guidelines Task Force on Thyroid Nodules and Differentiated Thyroid Cancer.

          Thyroid nodules are a common clinical problem, and differentiated thyroid cancer is becoming increasingly prevalent. Since the American Thyroid Association's (ATA's) guidelines for the management of these disorders were revised in 2009, significant scientific advances have occurred in the field. The aim of these guidelines is to inform clinicians, patients, researchers, and health policy makers on published evidence relating to the diagnosis and management of thyroid nodules and differentiated thyroid cancer.
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            Computational Radiomics System to Decode the Radiographic Phenotype

            Radiomics aims to quantify phenotypic characteristics on medical imaging through the use of automated algorithms. Radiomic artificial intelligence (AI) technology, either based on engineered hard-coded algorithms or deep learning methods, can be used to develop non-invasive imaging-based biomarkers. However, lack of standardized algorithm definitions and image processing severely hampers reproducibility and comparability of results. To address this issue, we developed PyRadiomics , a flexible open-source platform capable of extracting a large panel of engineered features from medical images. PyRadiomics is implemented in Python and can be used standalone or using 3D-Slicer. Here, we discuss the workflow and architecture of PyRadiomics and demonstrate its application in characterizing lung-lesions. Source code, documentation, and examples are publicly available at www.radiomics.io . With this platform, we aim to establish a reference standard for radiomic analyses, provide a tested and maintained resource, and to grow the community of radiomic developers addressing critical needs in cancer research.
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              3D Slicer as an image computing platform for the Quantitative Imaging Network.

              Quantitative analysis has tremendous but mostly unrealized potential in healthcare to support objective and accurate interpretation of the clinical imaging. In 2008, the National Cancer Institute began building the Quantitative Imaging Network (QIN) initiative with the goal of advancing quantitative imaging in the context of personalized therapy and evaluation of treatment response. Computerized analysis is an important component contributing to reproducibility and efficiency of the quantitative imaging techniques. The success of quantitative imaging is contingent on robust analysis methods and software tools to bring these methods from bench to bedside. 3D Slicer is a free open-source software application for medical image computing. As a clinical research tool, 3D Slicer is similar to a radiology workstation that supports versatile visualizations but also provides advanced functionality such as automated segmentation and registration for a variety of application domains. Unlike a typical radiology workstation, 3D Slicer is free and is not tied to specific hardware. As a programming platform, 3D Slicer facilitates translation and evaluation of the new quantitative methods by allowing the biomedical researcher to focus on the implementation of the algorithm and providing abstractions for the common tasks of data communication, visualization and user interface development. Compared to other tools that provide aspects of this functionality, 3D Slicer is fully open source and can be readily extended and redistributed. In addition, 3D Slicer is designed to facilitate the development of new functionality in the form of 3D Slicer extensions. In this paper, we present an overview of 3D Slicer as a platform for prototyping, development and evaluation of image analysis tools for clinical research applications. To illustrate the utility of the platform in the scope of QIN, we discuss several use cases of 3D Slicer by the existing QIN teams, and we elaborate on the future directions that can further facilitate development and validation of imaging biomarkers using 3D Slicer. Copyright © 2012 Elsevier Inc. All rights reserved.
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                Author and article information

                Contributors
                aueng_tw45@hotmail.com
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                29 February 2024
                29 February 2024
                2024
                : 14
                : 5001
                Affiliations
                [1 ]Division of Nuclear Medicine, Department of Radiology, Faculty of Medicine, Chulalongkorn University, ( https://ror.org/028wp3y58) Bangkok, Thailand
                [2 ]Division of Nuclear Medicine, Department of Radiology, King Chulalongkorn Memorial Hospital, The Thai Red Cross Society, Bangkok, Thailand
                [3 ] Division of Nuclear Medicine, Department of Radiology, Surin Hospital, ( https://ror.org/056ezdx45) Surin, Thailand
                [4 ]Chulalongkorn University Biomedical Imaging Group, Department of Radiology, Faculty of Medicine, Chulalongkorn University, ( https://ror.org/028wp3y58) Bangkok, Thailand
                [5 ]Biomedical Engineering Program, Faculty of Engineering, Chulalongkorn University, ( https://ror.org/028wp3y58) Bangkok, Thailand
                [6 ]GRID grid.512982.5, ISNI 0000 0004 7598 2416, School of Radiological Technology, Faculty of Health Science Technology, , Chulabhorn Royal Academy, ; Bangkok, Thailand
                Article
                55755
                10.1038/s41598-024-55755-6
                10904821
                38424177
                63338eb8-d22e-4a51-a08a-3ae1580e28f9
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 12 October 2023
                : 27 February 2024
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                © Springer Nature Limited 2024

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                radiomics,artificial intelligence,thyroid cancer,total body scan,successful ablation,biomarkers,diseases,endocrinology,health care,medical research,molecular medicine,oncology,mathematics and computing

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