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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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Machine Learning Model Development for Malignant Prostate Lesion Prediction Using Texture Analysis Features from

Adel Jawli¹, Ghulam Nabi², Zhihong Huang³

¹Biomedical Engineering, School of Science and Engineering, Fulton Building, University of Dundee, Dundee DD1 4HN, UK.

|April 26, 2025

Summary

This summary is machine-generated.

Machine learning and texture analysis of ultrasound images effectively distinguish prostate cancer from normal tissue. Support Vector Machines, Random Forest, and Naive Bayes models showed high accuracy in classification.

Keywords:

machine learning prostate cancer shear-wave elastography texture analysis ultrasound

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Area of Science:

Medical imaging analysis
Artificial intelligence in diagnostics
Prostate cancer research

Background:

Machine learning (ML) and artificial intelligence (AI) are increasingly used for medical image analysis to improve diagnostic accuracy.
Texture feature analysis is crucial for ML model development in differentiating benign and malignant tissues.

Purpose of the Study:

To evaluate quantitative texture features from ultrasound B-mode and shear-wave elastography (SWE) imaging for normal and prostate cancer tissues.
To develop and assess ML models for predicting and classifying normal versus malignant prostate tissues.

Main Methods:

Extracted first-order and second-order texture features from B-mode and SWE images, including Gray-Level Co-Occurrence Matrix (GLCM), GLDLM, GLRLM, and GLSZM.
Developed and evaluated five ML models (SVM, RF, NB, etc.) using 5-fold cross-validation on data from 62 patients.
Analyzed 94 texture features across four reconstructed regions of interest (ROIs) from SWE images.

Main Results:

Statistically significant differences in texture features were observed between normal and malignant tissues in all ROIs except B-mode imaging.
Support Vector Machines (SVM), Random Forest (RF), and Naive Bayes (NB) models demonstrated the highest performance.
Grayscale reconstructions (GPSWE and GRRI) achieved high sensitivity (82-98%) and specificity (90-96%) in prostate cancer prediction.

Conclusions:

Texture analysis combined with ML on SWE-US and reconstructed images effectively differentiates malignant from benign prostate lesions.
Key texture features include contrast, entropy, and correlation.
RF, SVM, and NB models exhibited superior classification performance, with grayscale reconstructions enhancing detection accuracy.