Assessing Patient Health Dynamics by Comparative CT Analysis: An Automatic Approach to Organ and Body Feature Evaluation
<b>Background/Objectives:</b> 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 automat...
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MDPI AG
2024-12-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/14/23/2760 |
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| author | Dominik Müller Jakob Christoph Voran Mário Macedo Dennis Hartmann Charlotte Lind Derk Frank Björn Schreiweis Frank Kramer Hannes Ulrich |
| author_facet | Dominik Müller Jakob Christoph Voran Mário Macedo Dennis Hartmann Charlotte Lind Derk Frank Björn Schreiweis Frank Kramer Hannes Ulrich |
| author_sort | Dominik Müller |
| collection | DOAJ |
| description | <b>Background/Objectives:</b> 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. <b>Methods:</b> 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. <b>Results:</b> 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. <b>Conclusions:</b> 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. |
| format | Article |
| id | doaj-art-059c6dd0d37e40b8b41eb711c31b32b9 |
| institution | OA Journals |
| issn | 2075-4418 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
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| series | Diagnostics |
| spelling | doaj-art-059c6dd0d37e40b8b41eb711c31b32b92025-08-20T02:38:43ZengMDPI AGDiagnostics2075-44182024-12-011423276010.3390/diagnostics14232760Assessing Patient Health Dynamics by Comparative CT Analysis: An Automatic Approach to Organ and Body Feature EvaluationDominik Müller0Jakob Christoph Voran1Mário Macedo2Dennis Hartmann3Charlotte Lind4Derk Frank5Björn Schreiweis6Frank Kramer7Hannes Ulrich8IT-Infrastructure for Translational Medical Research, University of Augsburg, 86159 Augsburg, GermanyDepartment of Cardiology, Kiel University and University Hospital Schleswig-Holstein, 24105 Kiel, GermanyInstitute for Medical Informatics and Statistics, Kiel University and University Hospital Schleswig-Holstein, 24105 Kiel, GermanyIT-Infrastructure for Translational Medical Research, University of Augsburg, 86159 Augsburg, GermanyDepartment of Cardiology, Kiel University and University Hospital Schleswig-Holstein, 24105 Kiel, GermanyDepartment of Cardiology, Kiel University and University Hospital Schleswig-Holstein, 24105 Kiel, GermanyInstitute for Medical Informatics and Statistics, Kiel University and University Hospital Schleswig-Holstein, 24105 Kiel, GermanyIT-Infrastructure for Translational Medical Research, University of Augsburg, 86159 Augsburg, GermanyInstitute for Medical Informatics and Statistics, Kiel University and University Hospital Schleswig-Holstein, 24105 Kiel, Germany<b>Background/Objectives:</b> 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. <b>Methods:</b> 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. <b>Results:</b> 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. <b>Conclusions:</b> 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.https://www.mdpi.com/2075-4418/14/23/2760radiomicsdiagnostic imagingcomputer tomographyhealth dynamics |
| spellingShingle | Dominik Müller Jakob Christoph Voran Mário Macedo Dennis Hartmann Charlotte Lind Derk Frank Björn Schreiweis Frank Kramer Hannes Ulrich Assessing Patient Health Dynamics by Comparative CT Analysis: An Automatic Approach to Organ and Body Feature Evaluation Diagnostics radiomics diagnostic imaging computer tomography health dynamics |
| title | Assessing Patient Health Dynamics by Comparative CT Analysis: An Automatic Approach to Organ and Body Feature Evaluation |
| title_full | Assessing Patient Health Dynamics by Comparative CT Analysis: An Automatic Approach to Organ and Body Feature Evaluation |
| title_fullStr | Assessing Patient Health Dynamics by Comparative CT Analysis: An Automatic Approach to Organ and Body Feature Evaluation |
| title_full_unstemmed | Assessing Patient Health Dynamics by Comparative CT Analysis: An Automatic Approach to Organ and Body Feature Evaluation |
| title_short | Assessing Patient Health Dynamics by Comparative CT Analysis: An Automatic Approach to Organ and Body Feature Evaluation |
| title_sort | assessing patient health dynamics by comparative ct analysis an automatic approach to organ and body feature evaluation |
| topic | radiomics diagnostic imaging computer tomography health dynamics |
| url | https://www.mdpi.com/2075-4418/14/23/2760 |
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