A preliminary PET radiomics study of brain metastases using a fully automatic segmentation method
Abstract Background Positron Emission Tomography (PET) is increasingly utilized in radiomics studies for treatment evaluation purposes. Nevertheless, lesion volume identification in PET images is a critical and still challenging step in the process of radiomics, due to the low spatial resolution and...
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doaj-3c0c25b5466040a4ad51374a75d9eb8d2020-11-25T03:55:01ZengBMCBMC Bioinformatics1471-21052020-09-0121S811410.1186/s12859-020-03647-7A preliminary PET radiomics study of brain metastases using a fully automatic segmentation methodAlessandro Stefano0Albert Comelli1Valentina Bravatà2Stefano Barone3Igor Daskalovski4Gaetano Savoca5Maria Gabriella Sabini6Massimo Ippolito7Giorgio Russo8Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR)Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR)Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR)University of PalermoDepartment of Physics and Astronomy, University of CataniaInstitute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR)Medical Physics Unit, Cannizzaro HospitalNuclear Medicine Department, Cannizzaro HospitalInstitute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR)Abstract Background Positron Emission Tomography (PET) is increasingly utilized in radiomics studies for treatment evaluation purposes. Nevertheless, lesion volume identification in PET images is a critical and still challenging step in the process of radiomics, due to the low spatial resolution and high noise level of PET images. Currently, the biological target volume (BTV) is manually contoured by nuclear physicians, with a time expensive and operator-dependent procedure. This study aims to obtain BTVs from cerebral metastases in patients who underwent L-[11C]methionine (11C-MET) PET, using a fully automatic procedure and to use these BTVs to extract radiomics features to stratify between patients who respond to treatment or not. For these purposes, 31 brain metastases, for predictive evaluation, and 25 ones, for follow-up evaluation after treatment, were delineated using the proposed method. Successively, 11C-MET PET studies and related volumetric segmentations were used to extract 108 features to investigate the potential application of radiomics analysis in patients with brain metastases. A novel statistical system has been implemented for feature reduction and selection, while discriminant analysis was used as a method for feature classification. Results For predictive evaluation, 3 features (asphericity, low-intensity run emphasis, and complexity) were able to discriminate between responder and non-responder patients, after feature reduction and selection. Best performance in patient discrimination was obtained using the combination of the three selected features (sensitivity 81.23%, specificity 73.97%, and accuracy 78.27%) compared to the use of all features. Secondly, for follow-up evaluation, 8 features (SUVmean, SULpeak, SUVmin, SULpeak prod-surface-area, SUVmean prod-sphericity, surface mean SUV 3, SULpeak prod-sphericity, and second angular moment) were selected with optimal performance in discriminant analysis classification (sensitivity 86.28%, specificity 87.75%, and accuracy 86.57%) outperforming the use of all features. Conclusions The proposed system is able i) to extract 108 features for each automatically segmented lesion and ii) to select a sub-panel of 11C-MET PET features (3 and 8 in the case of predictive and follow-up evaluation), with valuable association with patient outcome. We believe that our model can be useful to improve treatment response and prognosis evaluation, potentially allowing the personalization of cancer treatment plans.http://link.springer.com/article/10.1186/s12859-020-03647-7CancerActive contourPositron emission tomographyBiological target volumeRadiomics |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Alessandro Stefano Albert Comelli Valentina Bravatà Stefano Barone Igor Daskalovski Gaetano Savoca Maria Gabriella Sabini Massimo Ippolito Giorgio Russo |
spellingShingle |
Alessandro Stefano Albert Comelli Valentina Bravatà Stefano Barone Igor Daskalovski Gaetano Savoca Maria Gabriella Sabini Massimo Ippolito Giorgio Russo A preliminary PET radiomics study of brain metastases using a fully automatic segmentation method BMC Bioinformatics Cancer Active contour Positron emission tomography Biological target volume Radiomics |
author_facet |
Alessandro Stefano Albert Comelli Valentina Bravatà Stefano Barone Igor Daskalovski Gaetano Savoca Maria Gabriella Sabini Massimo Ippolito Giorgio Russo |
author_sort |
Alessandro Stefano |
title |
A preliminary PET radiomics study of brain metastases using a fully automatic segmentation method |
title_short |
A preliminary PET radiomics study of brain metastases using a fully automatic segmentation method |
title_full |
A preliminary PET radiomics study of brain metastases using a fully automatic segmentation method |
title_fullStr |
A preliminary PET radiomics study of brain metastases using a fully automatic segmentation method |
title_full_unstemmed |
A preliminary PET radiomics study of brain metastases using a fully automatic segmentation method |
title_sort |
preliminary pet radiomics study of brain metastases using a fully automatic segmentation method |
publisher |
BMC |
series |
BMC Bioinformatics |
issn |
1471-2105 |
publishDate |
2020-09-01 |
description |
Abstract Background Positron Emission Tomography (PET) is increasingly utilized in radiomics studies for treatment evaluation purposes. Nevertheless, lesion volume identification in PET images is a critical and still challenging step in the process of radiomics, due to the low spatial resolution and high noise level of PET images. Currently, the biological target volume (BTV) is manually contoured by nuclear physicians, with a time expensive and operator-dependent procedure. This study aims to obtain BTVs from cerebral metastases in patients who underwent L-[11C]methionine (11C-MET) PET, using a fully automatic procedure and to use these BTVs to extract radiomics features to stratify between patients who respond to treatment or not. For these purposes, 31 brain metastases, for predictive evaluation, and 25 ones, for follow-up evaluation after treatment, were delineated using the proposed method. Successively, 11C-MET PET studies and related volumetric segmentations were used to extract 108 features to investigate the potential application of radiomics analysis in patients with brain metastases. A novel statistical system has been implemented for feature reduction and selection, while discriminant analysis was used as a method for feature classification. Results For predictive evaluation, 3 features (asphericity, low-intensity run emphasis, and complexity) were able to discriminate between responder and non-responder patients, after feature reduction and selection. Best performance in patient discrimination was obtained using the combination of the three selected features (sensitivity 81.23%, specificity 73.97%, and accuracy 78.27%) compared to the use of all features. Secondly, for follow-up evaluation, 8 features (SUVmean, SULpeak, SUVmin, SULpeak prod-surface-area, SUVmean prod-sphericity, surface mean SUV 3, SULpeak prod-sphericity, and second angular moment) were selected with optimal performance in discriminant analysis classification (sensitivity 86.28%, specificity 87.75%, and accuracy 86.57%) outperforming the use of all features. Conclusions The proposed system is able i) to extract 108 features for each automatically segmented lesion and ii) to select a sub-panel of 11C-MET PET features (3 and 8 in the case of predictive and follow-up evaluation), with valuable association with patient outcome. We believe that our model can be useful to improve treatment response and prognosis evaluation, potentially allowing the personalization of cancer treatment plans. |
topic |
Cancer Active contour Positron emission tomography Biological target volume Radiomics |
url |
http://link.springer.com/article/10.1186/s12859-020-03647-7 |
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