PET radiomics-based lymphovascular invasion prediction in lung cancer using multiple segmentation and multi-machine learning algorithms

Aug 5, 2026

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The study predicts lymphovascular invasion (LVI) in non-small cell lung cancer (NSCLC) using multisegmentation PET radiomics combined with multiple machine learning algorithms to support personalized treatment strategies and improve patient outcomes. A cohort of 126 NSCLC patients underwent automated and semi-automated segmentation using methods including LAC, FCM, K-means, watershed, region growing, and iterative thresholding with varying thresholds. One hundred five radiomic features per ROI were extracted, with MRMR, RFE, and Boruta for feature selection and MLP, LR, XGBoost, Naive Bayes, and Random Forest for classification. Synthetic minority oversampling (SMOTE) was evaluated for effects on AUC, accuracy, sensitivity, and specificity.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages14

Reading Time35 min

AccessFree to download

Abstract

Introduction

Methods and Pipeline

Image segmentation

Radiomic feature extraction

Feature selection and classifiers

Results and Performance Metrics

Discussion and Clinical Implications

University of Groningen PET radiomics-based lymphovascular invasion prediction in lung cancer using multiple segmentation and multi-machine learning algorithms Hosseini, Seyyed Ali; Hajianfar, Ghasem; Ghaffarian, Pardis; Seyfi, Milad; Hosseini, Elahe; Aval, Atlas Haddadi; Servaes, Stijn; Hanaoka, Mauro; Rosa-Neto, Pedro; Chawla, Sanjeev Published in: Physical and Engineering Sciences in Medicine DOI: 10.1007/s13246-024-01475-0 IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document version below. Document Version Publisher's PDF, also known as Version of record Publication date: 2024 Link to publication in University of Groningen/UMCG research database Citation for published version (APA): Hosseini, S. A. , Hajianfar, G. , Ghaffarian, P. , Seyfi, M. , Hosseini, E. , Aval, A. H. , Servaes, S. , Hanaoka, M. , Rosa-Neto, P. , Chawla, S. , Zaidi, H. , & Ay, M. R. (2024) . PET radiomics-based lymphovascular invasion prediction in lung cancer using multiple segmentation and multi-machine learning algorithms. Physical and Engineering Sciences in Medicine, 47, 1613–1625 . [https://doi.org/10.1007/s13246-024-01475-0](https://doi.org/10.1007/s13246-024-01475-0) Copyright Other than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons) . The publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license. More information can be found on the University of Groningen website: [https://www.rug.nl/library/open-access/self-archiving-pure/taverne](https://www.rug.nl/library/open-access/self-archiving-pure/taverne)amendment. Take-down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Downloaded from the University of Groningen/U MCG research database (Pure): [http://www.rug. nl/research/portal. For technical reasons the](http://www.rug. nl/research/portal. For technical reasons the)[ ](http://www.rug. nl/research/portal. For technical reasons the)[number of authors shown on this cover page is limited to 10 maximum.](number of authors shown on this cover page is limited to 10 maximum.) Download date: 01-01-2026 Physical and Engineering Sciences in Medicine (2024) 47:1613–1625 [https://doi.org/10.1007/s13246-024-01475-0](https://doi.org/10.1007/s13246-024-01475-0) SCIENTIFIC PAPER PET radiomics-based lymphovascular invasion prediction in lung cancer using multiple segmentation and multi-machine learning algorithms Seyyed Ali Hosseini1,2 · Ghasem Hajianfar3 · Pardis Ghaffarian4,5 · Milad Seyfi6,7 · Elahe Hosseini8 · Atlas Haddadi Aval9 · Stijn Servaes1,2 · Mauro Hanaoka10 · Pedro Rosa-Neto1,2 · Sanjeev Chawla10 · Habib Zaidi11,12,13,14 · Mohammad Reza Ay6,7 Received: 22 February 2024 / Accepted: 6 August 2024 / Published online: 3 September 2024 © The Author(s) 2024 Abstract The current study aimed to predict lymphovascular invasion (LVI) using multiple machine learning algorithms and multisegmentation positron emission tomography (PET) radiomics in non-small cell lung cancer (NSCLC) patients, offering new avenues for personalized treatment strategies and improving patient outcomes. One hundred and twenty-six patients with NSCLC were enrolled in this study. Various automated and semi-automated PET image segmentation methods were applied, including Local Active Contour (LAC), Fuzzy-C-mean (FCM), K-means (KM), Watershed, Region Growing (RG), and Iterative thresholding (IT) with different percentages of the threshold. One hundred five radiomic features were extracted from each region of interest (ROI) . Multiple feature selection meth

FAQs

Which combination delivered the best reported performance?

SMOTE, iterative thresholding with a 45% threshold, RFE feature selection, and logistic regression showed the best performance, with AUC 0.93, ACC 0.84, SEN 0.85, and SPE 0.84.

Aug 5, 2026

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