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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Machine Learning-Based Classification of Cervical Lymph Nodes in HNSCC: A Radiomics Approach with Feature Selection

Sara Naccour¹, Assaad Moawad², Matthias Santer¹

¹Department of Otorhinolaryngology-Head and Neck Surgery, Medical University of Innsbruck, 6020 Innsbruck, Austria.

|August 28, 2025

Summary

This summary is machine-generated.

Radiomics analysis of CT scans accurately classifies lymph node (LN) metastasis in head and neck squamous cell carcinoma (HNSCC). This quantitative imaging approach enhances diagnostic accuracy for HNSCC LN staging, potentially reducing the need for invasive procedures.

Keywords:

XGBoost computed tomography ensemble method genetic algorithms head and neck squamous carcinoma radiomics feature random forest recursive feature elimination support vector machine

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Area of Science:

Radiology
Oncology
Medical Imaging Analysis

Background:

Computed tomography (CT) is vital for head and neck squamous cell carcinoma (HNSCC) staging, but conventional lymph node (LN) evaluation lacks sensitivity.
Morphological criteria in CT are subjective and may miss early metastatic disease, impacting prognosis and treatment planning.

Purpose of the Study:

To leverage radiomics for quantitative feature extraction from CT images to improve LN classification accuracy in HNSCC.
To develop a noninvasive method for LN assessment, reducing reliance on histopathology.

Main Methods:

Analysis of 234 LNs from 27 HNSCC patients, extracting 120 radiomic features per node.
Systematic optimization of machine learning models, feature selection, and hyperparameters, including ensemble methods.
Application of a Pareto front strategy for feature reduction and accuracy optimization.

Main Results:

An optimal radiomics model achieved a balanced accuracy of 0.90, AUC of 0.93, and F1-score of 0.88.
The high performance was obtained using only five selected radiomics features.
The model was validated using 5-fold cross-validation.

Conclusions:

The developed radiomics approach offers an interpretable and accurate method for noninvasive LN classification in HNSCC.
This technique shows significant potential for integration into clinical radiology practice for improved HNSCC management.