Machine Learning Model Development for Malignant Prostate Lesion Prediction Using Texture Analysis Features from Ultrasound Shear-Wave Elastography - Research summary

Aug 5, 2026

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The study develops machine learning (ML) models to predict and classify normal versus malignant prostate tissues using quantitative texture features derived from ultrasound imaging, including B-mode and shear-wave elastography (SWE). First-order and second-order texture features were extracted from reconstructed SWE regions of interest, yielding 94 features spanning intensity and texture matrices such as GLCM, GLDLM, GLRLM, and GLSZM. Five ML models were trained and evaluated with 5-fold cross-validation on data from 62 patients. Support Vector Machines, Random Forest, and Naive Bayes showed the highest performance, with most SWE-derived ROIs exhibiting significant feature differences.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages20

Reading Time50 min

AccessFree to download

Simple Summary

Machine learning for prostate cancer detection

Texture and pattern analysis from ultrasound SWE

Abstract

Introduction

Methodology

Results

[eprints@whiterose.ac.uk](eprints@whiterose.ac.uk)[ ](eprints@whiterose.ac.uk)[https://eprints.whiterose.ac.uk](https://eprints.whiterose.ac.uk) Universities of Leeds, Sheffield and York Deposited via The University of York. White Rose Research Online URL for this paper: [https://eprints.whiterose.ac.uk/id/eprint/237520/](https://eprints.whiterose.ac.uk/id/eprint/237520/) Version: Published Version Article: Jawli, Adel, Nabi, Ghulam, Huang, Zhihong et al. (2025) Machine Learning Model Development for Malignant Prostate Lesion Prediction Using Texture Analysis Features from Ultrasound Shear-Wave Elastography. Cancers. 1358. ISSN: 2072-6694 [https://doi.org/10.3390/cancers17081358](https://doi.org/10.3390/cancers17081358) Reuse This article is distributed under the terms of the Creative Commons Attribution (CC BY) licence. This licence allows you to distribute, remix, tweak, and build upon the work, even commercially, as long as you credit the authors for the original work. More information and the full terms of the licence here: [https://creativecommons.org/licenses/](https://creativecommons.org/licenses/) Takedown If you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing [eprints@whiterose.ac.uk](eprints@whiterose.ac.uk) including the URL of the record and the reason for the withdrawal request. Article Machine Learning Model Development for Malignant Prostate Lesion Prediction Using Texture Analysis Features from Ultrasound Shear-Wave Elastography Adel Jawli 1, *, Ghulam Nabi 2, Zhihong Huang 3, Abeer J. Alhusaini 2, Cheng Wei 1 and Benjie Tang 4, * Academic Editor: Dania Cioni Received: 21 February 2025 Revised: 13 April 2025 Accepted: 15 April 2025 Published: 18 April 2025 Citation: Jawli, A.; Nabi, G.; Huang, Z.; Alhusaini, A.J.; Wei, C.; Tang, B. Machine Learning Model Development for Malignant Prostate Lesion Prediction Using Texture Analysis Features from Ultrasound Shear-Wave Elastography. Cancers 2025, 17, 1358. [https://](https://)[ ](https://)[doi.org/10.3390/cancers17081358](doi.org/10.3390/cancers17081358) [Copyright:](Copyright:) © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/](https://creativecommons.org/)[ ](https://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/)) . 1 Biomedical Engineering, School of Science and Engineering, Fulton Building, University of Dundee, Dundee DD1 4HN, UK 2 Division of Imaging Sciences and Technology, School of Medicine, Ninewells Hospital, University of Dundee, Dundee DD1 9SY, UK 3 School of Physics, Engineering and Technology, University of York, Heslington, York YO10 5DD, UK 4 Surgical Skills Centre, Dundee Institute for Healthcare Simulation Respiratory Medicine and Gastroenterology, School of Medicine, Ninewells Hospital and Medical School, University of Dundee, Dundee DD1 9SY, UK * [Correspondence: ajawli@dundee.ac.uk](Correspondence: ajawli@dundee.ac.uk) (A.J.); [b.tang@dundee.ac.uk](b.tang@dundee.ac.uk) (B.T.) Simple Summary: Prostate cancer remains one of the most prevalent cancers affecting men globally, making early detection critical for improved treatment outcomes. Traditional imaging techniques often face challenges in clearly distinguishing between normal and cancerous tissues. In this study, we employed artificial intelligence and machine learning to analyze prostate tissue images acquired from ultrasound and a specialized method known as shear-wave elastography (SWE) . By exploring patterns and textures in these images, we trained machine learning models to accurately differentiate between healthy and malignant tissues. Our results demonstrated that machine learning models, particularly Support Vector Machines, Random Forest, and Naïve Bayes, excelled in detecting prostate cance

FAQs

What imaging data and texture features are used for the prostate lesion prediction models?

The models use ultrasound B-mode and shear-wave elastography (SWE) images. They extract first-order and second-order texture features, including intensity features and matrix-based features such as GLCM, GLDLM, GLRLM, and GLSZM.

How were the machine learning models developed and evaluated?

Five ML models were developed using 5-fold cross-validation to predict normal versus malignant prostate tissues based on the extracted texture features.

Which models performed best in classifying normal versus malignant tissues?

Support Vector Machines (SVM), Random Forest (RF), and Naive Bayes (NB) achieved the highest performance across the evaluated regions of interest.

Aug 5, 2026

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