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Benchmarking Eliminative Radiomic Feature Selection for Head and Neck Lymph Node Classification.

Zoltan R Bardosi1, Daniel Dejaco1, Matthias Santer1

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Summary
This summary is machine-generated.

Radiomics feature selection using sparse discriminant analysis and genetic optimization significantly reduced complexity for classifying head and neck squamous cell carcinoma lymph nodes. This approach maintained high accuracy with fewer features, aiding clinical application.

Keywords:
computed-tomographyextracapsular spreadfeature extractiongenetic algorithmshead and neck squamous carcinomalymph nodesradiomicsrecursive feature eliminationsparse discriminant analysis

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Area of Science:

  • Oncology
  • Radiology
  • Medical Imaging
  • Data Science

Background:

  • Pathologic cervical lymph nodes (LN) are critical negative predictors in head and neck squamous cell carcinoma (HNSCC).
  • Current contrast-enhanced computed-tomography (contrast-CT) criteria for LN classification are shape-based, limiting quantitative analysis.
  • Radiomics offers a data-driven approach to extract quantitative features from medical images, but extracted features are often redundant and complex for clinical use.

Purpose of the Study:

  • To explore effective eliminative feature selection (EFS) algorithms for identifying minimal yet accurate feature sets for LN classification in HNSCC.
  • To reduce the complexity of radiomic features extracted from contrast-CT scans for improved clinical applicability.

Main Methods:

  • A retrospective cohort study adhering to STROBE guidelines analyzed 252 LNs classified by expert radiologists.
  • Radiomics features were extracted from contrast-CT scans.
  • A combination of sparse discriminant analysis and genetic optimization was employed as an EFS algorithm.

Main Results:

  • The developed EFS approach retained up to 90% of classification accuracy while using only 10% of the original features.
  • The selected radiomic features were deemed plausible and potentially capable of accurate LN classification from a clinical standpoint.

Conclusions:

  • Effective eliminative feature selection can significantly simplify radiomic feature sets for LN classification in HNSCC.
  • The identified EFS algorithm and features show promise for prospective validation in classifying HNSCC LNs.