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Assessing Patient Health Dynamics by Comparative CT Analysis: An Automatic Approach to Organ and Body Feature Evaluation. Diagnostics 2024, 14, 2760. [https://](https://)[ ](https://)[doi.org/10.3390/diagnostics14232760](doi.org/10.3390/diagnostics14232760)  \nAcademic Editor: Ayman El-Baz  \nReceived: 23 October 2024  \nRevised: 24 November 2024  \nAccepted: 5 December 2024  \nPublished: 8 December 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 IT-Infrastructure for Translational Medical Research, University of Augsburg, 86159 Augsburg, Germany  \n2 Institute for Digital Medicine, University Hospital Augsburg, 86156 Augsburg, Germany  \n3 Institute for Medical Informatics and Statistics, Kiel University and University Hospital Schleswig-Holstein, 24105 Kiel, Germany  \n4 Department of Cardiology, Kiel University and University Hospital Schleswig-Holstein, 24105 Kiel, Germany  \n5 German Centre for Cardiovascular Research, Partner Site Hamburg/Kiel/Lübeck, 24103 Kiel, Germany  \n6 Medical Data Integration Center, University Hospital Schleswig-Holstein, 24105 Kiel, Germany  \n* Correspondence: [dominik.mueller@informatik.uni-augsburg.de](dominik.mueller@informatik.uni-augsburg.de)  \nAbstract: Background/Objectives: The integration of machine learning into the domain of radiomics has revolutionized the approach to personalized medicine, particularly in oncology. Our research presents RadTA (RADiomics Trend Analysis), a novel framework developed to facilitate the automatic analysis of quantitative imaging biomarkers (QIBs) from time-series CT volumes. Methods: RadTA is designed to bridge a technical gap for medical experts and enable sophisticated radiomic analyses without deep learning expertise. The core of RadTA includes an automated command line interface, streamlined image segmentation, comprehensive feature extraction, and robust evaluation mechanisms. RadTA utilizes advanced segmentation models, specifically TotalSegmentator and Body Composition Analysis (BCA), to accurately delineate anatomical structures from CT scans. These models enable the extraction of a wide variety of radiomic features, which are subsequently processed and compared to assess health dynamics across timely corresponding CT series. Results: The effectiveness of RadTA was tested using the HNSCC-3DCT-RT dataset, which includes CT scans from oncological patients undergoing radiation therapy. The results demonstrate significant changes in tissue composition and provide insights into the physical effects of the treatment. Conclusions: RadTA demonstrates a step of clinical adoption in the field of radiomics, offering a user-friendly, robust, and effective tool for the analysis of patient health dynamics. It can potentially also be used for other medical specialties.  \nKeywords: radiomics; diagnostic imaging; computer tomography; health dynamics  \n1. Introduction  \nRadiomics has experienced rapid growth in the last decade and empowered personalized medicine [1,2], especially in the field of oncology [3], by bridging the gap between radiology and cancer treatment [2,4–8] . Currently, it is extending its influence to various other medical fields [9] . The image-based computation of biomarkers has the potential to uncover biolog","cbCaiq7mrakdhZI0","https://ap.wps.com/l/cbCaiq7mrakdhZI0","pdf",2320982,"English","# Abstract\n# 1. Introduction\n# 2. Materials and Methods","[{\"question\":\"What problem does RadTA address in radiomics workflows?\",\"answer\":\"It targets the technical barrier that existing tools pose for medical experts, especially around setup, interpretation, and trust. RadTA enables automatic calculation and comparison of time-series quantitative imaging biomarkers with low technical barriers.\"},{\"question\":\"How does RadTA perform organ and body feature evaluation?\",\"answer\":\"It uses an automated command-line workflow and segmentation models—TotalSegmentator and Body Composition Analysis—to delineate anatomical structures. Radiomic features are then extracted and processed for comparison across pre- and post-condition CT series.\"},{\"question\":\"What evidence is used to test RadTA’s effectiveness?\",\"answer\":\"RadTA is evaluated using the HNSCC-3DCT-RT dataset containing CT scans of oncological patients undergoing radiation therapy. The results show significant changes in tissue composition and insights into treatment effects.\"}]","Assessing Patient Health Dynamics by Comparative CT Analysis - An Automatic Approach to Organ and Body Feature Evaluation | PDF",23]