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Related Concept Videos

Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
Radiological Investigation I: X-ray and CT01:30

Radiological Investigation I: X-ray and CT

Radiological investigations, including X-rays and computed tomography (CT) scans, are critical for diagnosing and evaluating various medical conditions. These imaging techniques provide valuable insights into the body's internal structures, aiding in the detection of abnormalities, assessment of disease progression, and development of treatment strategies. This article delves into two primary radiological investigations, chest X-rays and CT scans, outlining their purpose, procedures, and the...
Radiological Investigation II: MRI and Ventilation Perfusion Scan01:30

Radiological Investigation II: MRI and Ventilation Perfusion Scan

Description
Magnetic Resonance Imaging (MRI) and Ventilation Perfusion Scans are two radiological investigations that offer detailed diagnostic images of the body, particularly lung structures.
MRI
MRI uses magnetic fields and radiofrequency signals to distinguish between normal and abnormal tissues. This technology provides a more detailed diagnostic image than CT scans, enabling it to characterize pulmonary nodules, stage bronchogenic carcinoma, and evaluate inflammatory activity in...
X-ray Imaging01:24

X-ray Imaging

German physicist Wilhelm Röntgen (1845–1923) was experimenting with electrical current when he discovered that a mysterious and invisible "ray" would pass through his flesh but leave an outline of his bones on a screen coated with a metal compound. In 1895, Röntgen made the first durable record of the internal parts of a living human: an "X-ray" image (as it came to be called) of his wife’s hand. Scientists worldwide quickly began their own experiments with X-rays, and by 1900, X-ray was widely...
Imaging Studies for Cardiovascular System IV: CMRI01:21

Imaging Studies for Cardiovascular System IV: CMRI

Cardiovascular magnetic resonance imaging, or CMRI, is a non-invasive diagnostic test that employs a magnetic field and radiofrequency waves to create precise images of the heart and arteries. It provides comprehensive information about cardiac anatomy, function, perfusion, and tissue characterization without ionizing radiation.IndicationsCMRI diagnoses various heart conditions, including tissue damage from heart attacks, ischemic heart disease, myocarditis, aortic issues (tears, aneurysms,...
Computed Tomography01:10

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
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Guidelines and Experience Using Imaging Biomarker Explorer (IBEX) for Radiomics
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Radiomics in Medical Imaging: Methods, Applications, and Challenges.

Fnu Neha1, Deepak Kumar Shukla2

  • 1Department of Computer Science, Kent State University, Kent, OH 44242, USA.

Journal of Imaging
|June 25, 2026
PubMed
Summary

Radiomics, a quantitative medical imaging analysis, faces challenges in stability and translation. This survey analyzes radiomics pipelines end-to-end to improve robustness and clinical applicability.

Keywords:
artificial intelligence (AI)automated diagnosiscomputational radiologydigital healthmachine learningmedical image processingradiomics

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Area of Science:

  • Medical Imaging
  • Radiomics
  • Quantitative Analysis

Background:

  • Radiomics offers quantitative medical image analysis for predictive modeling.
  • Despite progress, radiomics faces challenges in feature stability, reproducibility, and clinical translation.
  • Current reviews often overlook how interdependent design choices impact radiomics robustness.

Purpose of the Study:

  • To provide an end-to-end analysis of radiomics pipelines.
  • To examine how methodological decisions influence feature stability, model reliability, and translational validity.
  • To identify challenges and future directions for radiomics standardization and clinical deployment.

Main Methods:

  • Review of radiomic feature extraction, selection, and dimensionality reduction.
  • Analysis of classical machine learning and deep learning-based modeling approaches.
  • Emphasis on validation protocols, data leakage prevention, and statistical reliability.

Main Results:

  • Methodological decisions across the radiomics pipeline significantly impact feature stability and model reliability.
  • Evaluation rigor in clinical applications is crucial for translational validity.
  • Open challenges include standardization, domain shift, and clinical deployment.

Conclusions:

  • Addressing interdependent design choices in radiomics pipelines is key to enhancing robustness and generalizability.
  • Future directions include hybrid models, multimodal fusion, federated learning, and standardized benchmarking.
  • Improving radiomics reliability is essential for successful clinical translation.