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Frequency Ranking of Imaging Biomarkers for Lung Cancer Risk Stratification Using a Hybrid Elastic Net Method.

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  • 1Department of Mathematics and Systems Engineering, Florida Institute of Technology, Melbourne, FL 32901, USA.

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Summary
This summary is machine-generated.

Radiomic imaging biomarkers, specifically the texture feature Busyness, significantly improve lung cancer survival prediction compared to traditional factors like stage and age. This offers a promising noninvasive tool for personalized cancer care.

Keywords:
Busyness featureSMOTE balancinglung cancer stratificationmachine learning in oncologyprognostic modelingradiomic biomarkerssurvival analysis

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Area of Science:

  • Oncology
  • Radiology
  • Biomarker Discovery

Background:

  • Lung cancer is a leading cause of cancer mortality globally.
  • Current prognostic factors (stage, age, sex) have limited predictive power.
  • Novel biomarkers are crucial for precision oncology in lung cancer.

Purpose of the Study:

  • To evaluate the prognostic utility of radiomic imaging biomarkers in lung cancer.
  • To compare the performance of radiomic features against conventional clinical predictors.
  • To investigate the texture-based feature 'Busyness' as a prognostic marker.

Main Methods:

  • Radiomic analysis of medical imaging to extract quantitative features.
  • Survival analysis to compare prognostic performance of imaging biomarkers and clinical factors.
  • Application of Synthetic Minority Over-sampling Technique (SMOTE) to enhance model robustness.

Main Results:

  • The radiomic feature 'Busyness' demonstrated significantly better survival outcome discrimination than tumor stage, age, or sex.
  • 'Busyness' consistently predicted survival across different age and sex subgroups.
  • SMOTE application supported the stability and robustness of the radiomic biomarker findings.

Conclusions:

  • Radiomic imaging biomarkers, particularly 'Busyness', show strong potential as noninvasive prognostic tools in lung cancer.
  • These biomarkers can enhance prognostic accuracy beyond traditional clinical variables.
  • Integration into clinical workflows could improve lung cancer patient management and guide precision oncology.