Tehran University of Medical Sciences

Science Communicator Platform

Stay connected! Follow us on X network (Twitter):
Share this content! On (X network) By
Lymphovascular Invasion Prediction in Lung Cancer Using Multi-Segmentation Pet Radiomics and Multi-Machine Learning Algorithms Publisher



Hosseini SA1 ; Hajianfar G2 ; Shiri I3 ; Zaidi H1, 4, 5, 6, 7
Authors
Show Affiliations
Authors Affiliations
  1. 1. Tehran University of Medical Sciences, Department of Medical Physics and Biomedical Engineering, Tehran, Iran
  2. 2. Iran University of Medical Science, Rajaie Cardiovascular Medical and Research Center, Tehran, Iran
  3. 3. Geneva University Hospital, Division of Nuclear Medicine and Molecular Imaging, Department of Medical Imaging, Geneva 4, CH-1211, Switzerland
  4. 4. Geneva University, Geneva University Neurocenter, Geneva, CH-1205, Switzerland
  5. 5. University of Groningen, Department of Nuclear Medicine and Molecular Imaging, Netherlands
  6. 6. University Medical Center, Groningen, 9700, Netherlands
  7. 7. University of Southern Denmark, Department of Nuclear Medicine, Odense, DK-500, Denmark

Source: 2021 IEEE Nuclear Science Symposium and Medical Imaging Conference Record# NSS/MIC 2021 and 28th International Symposium on Room-Temperature Semiconductor Detectors# RTSD 2022 Published:2021


Abstract

The current study aimed to predict the lymphovascular invasion in lung cancer patients using PET radiomics features and multivariate analysis of various segmentation techniques. To this end, 126 patients with non-small cell lung cancer (NSCLC) that underwent PET/CT examinations were enrolled in this study protocol. Multiple segmentations were applied to the images, including k-Means, iterative thresholding with different thresholds, local active contour, watershed, and manual contouring of lesions. For each dataset, a total 105 features belonging to shape and first-order statistics and texture features were extracted. Three feature selection and ten classifiers with 100 bootstraps were utilized through various segmentation methods using machine learning algorithms. The results demonstrated that Local Active Contour (LAC) methods with recursive Feature Elimination (RFE) feature selection and Naive Bayes (NB) classifier had the highest performance (AUC = 0.94), followed by K-means method with the Minimum Redundancy Maximum Relevance (MRMR) feature selection and NB classifier (AUC = 0.93). Different segmentation algorithms achieve different performance. The radiomics features extracted from ROIs of various segmentation algorithms are suitable biomarkers for predicting the lymphovascular invasion in patients with NSCLC. © 2021 IEEE.
Related Docs
View other Related Docs
1. Lung Cancer Recurrence Prediction Using Radiomics Features of Pet Tumor Sub-Volumes and Multi-Machine Learning Algorithms, 2021 IEEE Nuclear Science Symposium and Medical Imaging Conference Record# NSS/MIC 2021 and 28th International Symposium on Room-Temperature Semiconductor Detectors# RTSD 2022 (2021)
2. Pet Image Radiomics Feature Variability in Lung Cancer: Impact of Image Segmentation, 2021 IEEE Nuclear Science Symposium and Medical Imaging Conference Record# NSS/MIC 2021 and 28th International Symposium on Room-Temperature Semiconductor Detectors# RTSD 2022 (2021)
3. Cardiac Pattern Recognition From Spect Images Using Machine Learning Algorithms, 2021 IEEE Nuclear Science Symposium and Medical Imaging Conference Record# NSS/MIC 2021 and 28th International Symposium on Room-Temperature Semiconductor Detectors# RTSD 2022 (2021)
4. Combat Harmonization of Image Reconstruction Parameters to Improve the Repeatability of Radiomics Features, 2021 IEEE Nuclear Science Symposium and Medical Imaging Conference Record# NSS/MIC 2021 and 28th International Symposium on Room-Temperature Semiconductor Detectors# RTSD 2022 (2021)
5. Covid-19 Prognostic Modeling Using Ct Radiomic Features and Machine Learning Algorithms: Analysis of a Multi-Institutional Dataset of 14,339 Patients: Covid-19 Prognostic Modeling Using Ct Radiomics and Machine Learning, Computers in Biology and Medicine (2022)
6. [18F]Fdg-Pet/Ct Radiomics and Artificial Intelligence in Lung Cancer: Technical Aspects and Potential Clinical Applications, Seminars in Nuclear Medicine (2022)
7. Overall Survival Prediction in Renal Cell Carcinoma Patients Using Computed Tomography Radiomic and Clinical Information, Journal of Digital Imaging (2021)
8. Non-Small Cell Lung Carcinoma Histopathological Subtype Phenotyping Using High-Dimensional Multinomial Multiclass Ct Radiomics Signature, Computers in Biology and Medicine (2021)
Experts (# of related papers)
View all Related Experts
Parham Geramifar (5)
Amir Reza Radmard (1)
Other Related Docs
9. Repeatability of Radiomic Features in Magnetic Resonance Imaging of Glioblastoma: Test–Retest and Image Registration Analyses, Medical Physics (2020)
10. Information Fusion for Fully Automated Segmentation of Head and Neck Tumors From Pet and Ct Images, Medical Physics (2024)
11. Ct Imaging Markers to Improve Radiation Toxicity Prediction in Prostate Cancer Radiotherapy by Stacking Regression Algorithm, Radiologia Medica (2020)
12. Machine Learning-Based Overall Survival Prediction in Gbm Patients Using Mri Radiomics, 2022 IEEE NSS/MIC RTSD - IEEE Nuclear Science Symposium# Medical Imaging Conference and Room Temperature Semiconductor Detector Conference (2022)
13. Radiomics Predictive Modeling From Dual-Time-Point Fdg Pet Ki Parametric Maps: Application to Chemotherapy Response in Lymphoma, EJNMMI Research (2023)
14. Eltirads Framework for Thyroid Nodule Classification Integrating Elastography, Tirads, and Radiomics With Interpretable Machine Learning, Scientific Reports (2025)
15. Prediction of the Gleason Score of Prostate Cancer Patients Using 68Ga-Psma-Pet/Ct Radiomic Models, Journal of Medical and Biological Engineering (2024)
16. Robust Versus Non-Robust Radiomic Features: Machine Learning Based Models for Nsclc Lymphovascular Invasion, 2022 IEEE NSS/MIC RTSD - IEEE Nuclear Science Symposium# Medical Imaging Conference and Room Temperature Semiconductor Detector Conference (2022)