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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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Mitigating Interobserver Variability in Radiomics with ComBat: A Feasibility Study.

Alessia D'Anna1, Giuseppe Stella1, Anna Maria Gueli1

  • 1Department of Physics and Astronomy "E. Majorana", University of Catania, Via Santa Sofia 64, 95123 Catania, Italy.

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|November 26, 2024
PubMed
Summary

Intraobserver Features Variability (IFV) in radiomics can be reduced using ComBat harmonization. This method improves the reliability of radiomics analysis across different centers and physicians.

Keywords:
batch correctionclinical imagingmulticenter studiesprecision medicineradiomicssegmentation

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Area of Science:

  • Radiomics
  • Medical Imaging Analysis
  • Biostatistics

Background:

  • Radiomics studies face challenges with Intraobserver Features Variability (IFV).
  • Variability arises from differences in manual or semi-automated segmentation by physicians.
  • Standardizing radiomics data is crucial for multicenter studies.

Purpose of the Study:

  • To investigate IFV in radiomics.
  • To assess the effectiveness of ComBat harmonization in reducing IFV.
  • To evaluate the impact of segmentation methods and physician experience on radiomics outcomes.

Main Methods:

  • Utilized the NSCLC-Radiomics-Interobserver1 dataset with CT scans from 22 Non-Small Cell Lung Cancer patients.
  • Performed manual ('vis') and semi-automated ('auto') Gross Tumor Volume (GTV) segmentations by five radiation oncologists.
  • Extracted 1229 radiomic features from original and filtered images before and after ComBat harmonization.

Main Results:

  • ComBat harmonization significantly reduced the percentage of statistically significant features attributed to IFV.
  • For manual segmentation, significant features decreased from 83% to 34%.
  • For semi-automated segmentation, significant features decreased from 75% to 33%.

Conclusions:

  • ComBat harmonization effectively mitigates IFV in radiomics.
  • This harmonization enhances the feasibility of multicenter radiomics research.
  • Physician experience significantly impacts radiomics analysis outcomes.