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Imaging Studies III: Computed Tomography01:27

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DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
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Using Micro-computed Tomography for the Assessment of Tumor Development and Follow-up of Response to Treatment in a Mouse Model of Lung Cancer
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Interchangeability between real and three-dimensional simulated lung tumors in computed tomography: an interalgorithm

Marthony Robins1,2,3, Justin Solomon1,2,3, Jocelyn Hoye1,2,3

  • 1Carl E. Ravin Advanced Imaging Laboratories, Durham, North Carolina, United States.

Journal of Medical Imaging (Bellingham, Wash.)
|March 7, 2019
PubMed
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Simulated lung tumors in computed tomography (CT) scans closely match real tumors volumetrically. This validates using computational models with patient CT data as effective surrogates for research and development.

Keywords:
CT simulationlung tumorquantitativesegmentationvolume

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Area of Science:

  • Medical imaging
  • Computational modeling
  • Radiology

Background:

  • Accurate lung tumor volumetry is crucial for treatment assessment.
  • Developing realistic computational tumor models for CT imaging is challenging.
  • Hybrid datasets combining real patient data with simulated tumors offer a novel approach.

Purpose of the Study:

  • To establish volumetric interchangeability between real and computational lung tumors in CT scans.
  • To evaluate the performance of commercial segmentation tools in measuring simulated versus real tumors.
  • To assess the influence of tumor characteristics and local environment on volume measurements.

Main Methods:

  • Hybrid datasets were created using patient CT images with digitally inserted computational tumors.
  • Real tumors from 30 thoracic cases (RIDER database) informed simulated tumor size and shape.
  • Four readers used three commercial tools to measure volumes of real and simulated tumors.
  • Statistical analyses included direct volume comparison, multivariate analysis, and intraclass correlation.

Main Results:

  • A consistent 9% volumetric difference was observed between real and simulated tumors across all tools and readers.
  • An intraclass correlation coefficient of 0.99 indicated strong agreement between simulated and real tumor volumes.
  • The local tumor environment showed a segmentation tool-dependent influence on volume measurement.

Conclusions:

  • Volumetrically, simulated tumors embedded in patient CT data serve as reliable surrogates for real patient tumors.
  • This validation supports the use of hybrid datasets for developing and testing imaging biomarkers and AI algorithms.
  • Computational tumor models can enhance research by providing standardized, reproducible datasets.