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Radiomics-based machine learning models to distinguish between metastatic and healthy bone using lesion-center-based geometric regions of interest

Radiomics-based machine learning classifiers have shown potential for detecting bone metastases (BM) and for evaluating BM response to radiotherapy (RT). However, current radiomics models require large datasets of images with expert-segmented 3D regions of interest (ROIs). Full ROI segmentation is t...

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Autores principales: Naseri, Hossein, Skamene, Sonia, Tolba, Marwan, Faye, Mame Daro, Ramia, Paul, Khriguian, Julia, Patrick, Haley, Andrade Hernandez, Aixa X., David, Marc, Kildea, John
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9198102/
https://www.ncbi.nlm.nih.gov/pubmed/35701461
http://dx.doi.org/10.1038/s41598-022-13379-8
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author Naseri, Hossein
Skamene, Sonia
Tolba, Marwan
Faye, Mame Daro
Ramia, Paul
Khriguian, Julia
Patrick, Haley
Andrade Hernandez, Aixa X.
David, Marc
Kildea, John
author_facet Naseri, Hossein
Skamene, Sonia
Tolba, Marwan
Faye, Mame Daro
Ramia, Paul
Khriguian, Julia
Patrick, Haley
Andrade Hernandez, Aixa X.
David, Marc
Kildea, John
author_sort Naseri, Hossein
collection PubMed
description Radiomics-based machine learning classifiers have shown potential for detecting bone metastases (BM) and for evaluating BM response to radiotherapy (RT). However, current radiomics models require large datasets of images with expert-segmented 3D regions of interest (ROIs). Full ROI segmentation is time consuming and oncologists often outline just RT treatment fields in clinical practice. This presents a challenge for real-world radiomics research. As such, a method that simplifies BM identification but does not compromise the power of radiomics is needed. The objective of this study was to investigate the feasibility of radiomics models for BM detection using lesion-center-based geometric ROIs. The planning-CT images of 170 patients with non-metastatic lung cancer and 189 patients with spinal BM were used. The point locations of 631 BM and 674 healthy bone (HB) regions were identified by experts. ROIs with various geometric shapes were centered and automatically delineated on the identified locations, and 107 radiomics features were extracted. Various feature selection methods and machine learning classifiers were evaluated. Our point-based radiomics pipeline was successful in differentiating BM from HB. Lesion-center-based segmentation approach greatly simplifies the process of preparing images for use in radiomics studies and avoids the bottleneck of full ROI segmentation.
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spelling pubmed-91981022022-06-16 Radiomics-based machine learning models to distinguish between metastatic and healthy bone using lesion-center-based geometric regions of interest Naseri, Hossein Skamene, Sonia Tolba, Marwan Faye, Mame Daro Ramia, Paul Khriguian, Julia Patrick, Haley Andrade Hernandez, Aixa X. David, Marc Kildea, John Sci Rep Article Radiomics-based machine learning classifiers have shown potential for detecting bone metastases (BM) and for evaluating BM response to radiotherapy (RT). However, current radiomics models require large datasets of images with expert-segmented 3D regions of interest (ROIs). Full ROI segmentation is time consuming and oncologists often outline just RT treatment fields in clinical practice. This presents a challenge for real-world radiomics research. As such, a method that simplifies BM identification but does not compromise the power of radiomics is needed. The objective of this study was to investigate the feasibility of radiomics models for BM detection using lesion-center-based geometric ROIs. The planning-CT images of 170 patients with non-metastatic lung cancer and 189 patients with spinal BM were used. The point locations of 631 BM and 674 healthy bone (HB) regions were identified by experts. ROIs with various geometric shapes were centered and automatically delineated on the identified locations, and 107 radiomics features were extracted. Various feature selection methods and machine learning classifiers were evaluated. Our point-based radiomics pipeline was successful in differentiating BM from HB. Lesion-center-based segmentation approach greatly simplifies the process of preparing images for use in radiomics studies and avoids the bottleneck of full ROI segmentation. Nature Publishing Group UK 2022-06-14 /pmc/articles/PMC9198102/ /pubmed/35701461 http://dx.doi.org/10.1038/s41598-022-13379-8 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Naseri, Hossein
Skamene, Sonia
Tolba, Marwan
Faye, Mame Daro
Ramia, Paul
Khriguian, Julia
Patrick, Haley
Andrade Hernandez, Aixa X.
David, Marc
Kildea, John
Radiomics-based machine learning models to distinguish between metastatic and healthy bone using lesion-center-based geometric regions of interest
title Radiomics-based machine learning models to distinguish between metastatic and healthy bone using lesion-center-based geometric regions of interest
title_full Radiomics-based machine learning models to distinguish between metastatic and healthy bone using lesion-center-based geometric regions of interest
title_fullStr Radiomics-based machine learning models to distinguish between metastatic and healthy bone using lesion-center-based geometric regions of interest
title_full_unstemmed Radiomics-based machine learning models to distinguish between metastatic and healthy bone using lesion-center-based geometric regions of interest
title_short Radiomics-based machine learning models to distinguish between metastatic and healthy bone using lesion-center-based geometric regions of interest
title_sort radiomics-based machine learning models to distinguish between metastatic and healthy bone using lesion-center-based geometric regions of interest
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9198102/
https://www.ncbi.nlm.nih.gov/pubmed/35701461
http://dx.doi.org/10.1038/s41598-022-13379-8
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