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Updated: May 10, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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Patch-Based Texture Feature Extraction Towards Improved Clinical Task Performance.

Tao Lian^1,2,3, Chunyan Deng¹, Qianjin Feng^1,2,3

¹School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.

Bioengineering (Basel, Switzerland)

|April 26, 2025

Summary

This summary is machine-generated.

Novel 3D patch-based texture features improve cancer prediction by capturing spatial heterogeneity in medical images. These radiomics features enhance accuracy in predicting breast cancer metastasis and cervical cancer subtypes.

Keywords:

cancer prediction machine learning medical image processing patch-based texture features radiomics

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Area of Science:

Medical Imaging Analysis
Radiomics
Computational Pathology

Background:

Texture features are crucial for medical image analysis, offering interpretability over deep learning.
Conventional texture features lack the ability to capture spatial heterogeneity between tissue volumes.

Purpose of the Study:

To introduce novel 3D patch-based texture features for enhanced medical image analysis.
To develop a radiomics framework to validate the efficacy of these new features.

Main Methods:

Utilized multi-scale 3D patches and k-means clustering to create patch patterns.
Discretized multi-resolution images based on pattern labels for texture feature extraction.
Quantified spatial heterogeneity between patches and evaluated 25 feature selection/classifier models.

Main Results:

Achieved an AUC of 0.76 for breast cancer lymph node metastasis prediction.
Attained an AUC of 0.94 for cervical cancer histological subtype prediction.
Outperformed conventional texture features (AUC 0.74 and 0.83, respectively).

Conclusions:

Proposed 3D patch-based texture features effectively capture multi-scale, patch-level texture representations.
These features enhance the application of imaging biomarkers for precise cancer prediction.
Potential to improve personalized therapeutic interventions through improved diagnostic accuracy.