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Assessing Patient Health Dynamics by Comparative CT Analysis: An Automatic Approach to Organ and Body Feature

Dominik Müller1,2,3, Jakob Christoph Voran4,5, Mário Macedo3,6

  • 1IT-Infrastructure for Translational Medical Research, University of Augsburg, 86159 Augsburg, Germany.

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RadTA (RADiomics Trend Analysis) automates quantitative imaging biomarker analysis from CT scans, aiding personalized medicine. This framework simplifies radiomics for medical experts, revealing treatment effects in oncology patients.

Keywords:
computer tomographydiagnostic imaginghealth dynamicsradiomics

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Area of Science:

  • Radiomics and Machine Learning
  • Medical Imaging Analysis
  • Personalized Oncology

Background:

  • Machine learning integration in radiomics is transforming personalized medicine, especially in oncology.
  • Quantitative imaging biomarkers (QIBs) from time-series CT scans offer valuable clinical insights.
  • A gap exists in making advanced radiomic analysis accessible to medical experts without deep learning expertise.

Purpose of the Study:

  • To introduce RadTA (RADiomics Trend Analysis), a novel framework for automated radiomic analysis of time-series CT volumes.
  • To enable sophisticated radiomic analyses for medical experts, bypassing the need for deep learning knowledge.
  • To facilitate the automatic analysis of quantitative imaging biomarkers (QIBs) for improved patient care.

Main Methods:

  • RadTA employs an automated command-line interface for streamlined workflow.
  • Utilizes advanced segmentation models like TotalSegmentator and Body Composition Analysis (BCA) for accurate anatomical delineation.
  • Features comprehensive radiomic feature extraction and comparative analysis across time-series CT data.

Main Results:

  • RadTA was validated on the HNSCC-3DCT-RT dataset, including CT scans from patients undergoing radiation therapy.
  • Demonstrated significant changes in tissue composition, providing insights into treatment effects.
  • Highlighted the framework's capability to assess patient health dynamics over time.

Conclusions:

  • RadTA represents a significant step towards clinical adoption of radiomics.
  • Offers a user-friendly, robust, and effective tool for analyzing patient health dynamics.
  • The framework has potential applications beyond oncology in other medical specialties.