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Updated: Oct 13, 2025

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Radiomics for Predicting Lung Cancer Outcomes Following Radiotherapy: A Systematic Review.

G M Walls1, S O S Osman1, K H Brown2

  • 1Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, UK; Cancer Centre Belfast City Hospital, Belfast Health & Social Care Trust, Belfast, UK.

Clinical Oncology (Royal College of Radiologists (Great Britain))
|November 12, 2021
PubMed
Summary
This summary is machine-generated.

Radiomics analysis of lung cancer imaging may offer non-invasive biomarkers for treatment response and survival. Further validation is needed to overcome implementation barriers for radical radiotherapy decision-making.

Keywords:
Biomarkerdeep learninglung cancerradiomicsradiotherapy

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Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics
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Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics

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Area of Science:

  • Oncology
  • Medical Imaging
  • Radiomics

Background:

  • Lung cancer has a high mortality rate with no validated biomarkers for personalized radical radiotherapy.
  • Radiomics offers potential for non-invasive biomarkers to guide treatment decisions.

Purpose of the Study:

  • To systematically review radiomics studies in lung cancer to assess their potential for informing radical radiotherapy.
  • To identify imaging features associated with clinical outcomes and evaluate the quality of existing research.

Main Methods:

  • A PRISMA-guided systematic review of MEDLINE was conducted using terms 'radiomics' and 'lung cancer'.
  • Studies analyzing at least 10 quantifiable imaging features and reporting on disease control, survival, or toxicity after radical radiotherapy were included.
  • The Radiomics Quality Score was applied to assess study quality.

Main Results:

  • 44 studies met eligibility criteria, frequently reporting on overall survival (79%) and distant control (31%).
  • Texture features (Gray level run length matrix, Gray level co-occurrence matrix, kurtosis) were strongly associated with outcomes.
  • The median Radiomics Quality Score was low (11%), with limited external validation and no patient-reported outcomes.

Conclusions:

  • Radiomics-derived indices show promise as non-invasive biomarkers for predicting treatment response, failure patterns, toxicity, and survival in lung cancer.
  • Standardization, reporting quality, and external validation in prospective trials are crucial for clinical implementation.