Prediction of treatment response to transarterial radioembolization of liver metastases: Radiomics analysis of pre-treatment cone-beam CT: A proof of concept study
Purpose: To investigate the potential of texture analysis and machine learning to predict treatment response to transarterial radioembolization (TARE) on pre-interventional cone-beam computed tomography (CBCT) images in patients with liver metastases. Materials and Methods: In this IRB-approved retr...
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doaj-30bac4abda0041aa8aca73ecd806cb342021-09-01T04:21:25ZengElsevierEuropean Journal of Radiology Open2352-04772021-01-018100375Prediction of treatment response to transarterial radioembolization of liver metastases: Radiomics analysis of pre-treatment cone-beam CT: A proof of concept studyAdrian Kobe0Juliana Zgraggen1Florian Messmer2Gilbert Puippe3Thomas Sartoretti4Hatem Alkadhi5Thomas Pfammatter6Manoj Mannil7Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland; Corresponding author at: Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Rämistrasse 100, CH-8091, Zurich, Switzerland.Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich, SwitzerlandInstitute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich, SwitzerlandInstitute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich, SwitzerlandInstitute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich, SwitzerlandInstitute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich, SwitzerlandInstitute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich, SwitzerlandInstitute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland; Clinic of Radiology, University Hospital Münster, University of Münster, Münster, GermanyPurpose: To investigate the potential of texture analysis and machine learning to predict treatment response to transarterial radioembolization (TARE) on pre-interventional cone-beam computed tomography (CBCT) images in patients with liver metastases. Materials and Methods: In this IRB-approved retrospective single-center study 36 patients with a total of 104 liver metastases (56 % male, mean age 61.1 ± 13 years) underwent CBCT prior to TARE and follow-up imaging 6 months after therapy. Treatment response was evaluated according to RECIST version 1.1 and dichotomized into disease control (partial response/stable disease) versus disease progression (progressive disease). After target lesion segmentation, 104 radiomics features corresponding to seven different feature classes were extracted with the pyRadiomics package. After dimension reduction machine learning classifications were performed on a custom artificial neural network (ANN). Ten-fold cross validation on a previously unseen test data set was performed. Results: The average administered cumulative activity from TARE was 1.6 Gbq (± 0.5 Gbq). At a mean follow-up of 5.9 ± 0.8 months disease control was achieved in 82 % of metastases. After dimension reduction, 15 of 104 (15 %) texture analysis features remained for further analysis. On a previously unseen set of liver metastases the Multilayer Perceptron ANN yielded a sensitivity of 94.2 %, specificity of 67.7 % and an area-under-the receiver operating characteristics curve of 0.85. Conclusion: Our study indicates that texture analysis-based machine learning may has potential to predict treatment response to TARE using pre-treatment CBCT images of patients with liver metastases with high accuracy.http://www.sciencedirect.com/science/article/pii/S2352047721000551RadiomicsTransarterial radioembolizationMachine learningCone-Beam CT |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Adrian Kobe Juliana Zgraggen Florian Messmer Gilbert Puippe Thomas Sartoretti Hatem Alkadhi Thomas Pfammatter Manoj Mannil |
spellingShingle |
Adrian Kobe Juliana Zgraggen Florian Messmer Gilbert Puippe Thomas Sartoretti Hatem Alkadhi Thomas Pfammatter Manoj Mannil Prediction of treatment response to transarterial radioembolization of liver metastases: Radiomics analysis of pre-treatment cone-beam CT: A proof of concept study European Journal of Radiology Open Radiomics Transarterial radioembolization Machine learning Cone-Beam CT |
author_facet |
Adrian Kobe Juliana Zgraggen Florian Messmer Gilbert Puippe Thomas Sartoretti Hatem Alkadhi Thomas Pfammatter Manoj Mannil |
author_sort |
Adrian Kobe |
title |
Prediction of treatment response to transarterial radioembolization of liver metastases: Radiomics analysis of pre-treatment cone-beam CT: A proof of concept study |
title_short |
Prediction of treatment response to transarterial radioembolization of liver metastases: Radiomics analysis of pre-treatment cone-beam CT: A proof of concept study |
title_full |
Prediction of treatment response to transarterial radioembolization of liver metastases: Radiomics analysis of pre-treatment cone-beam CT: A proof of concept study |
title_fullStr |
Prediction of treatment response to transarterial radioembolization of liver metastases: Radiomics analysis of pre-treatment cone-beam CT: A proof of concept study |
title_full_unstemmed |
Prediction of treatment response to transarterial radioembolization of liver metastases: Radiomics analysis of pre-treatment cone-beam CT: A proof of concept study |
title_sort |
prediction of treatment response to transarterial radioembolization of liver metastases: radiomics analysis of pre-treatment cone-beam ct: a proof of concept study |
publisher |
Elsevier |
series |
European Journal of Radiology Open |
issn |
2352-0477 |
publishDate |
2021-01-01 |
description |
Purpose: To investigate the potential of texture analysis and machine learning to predict treatment response to transarterial radioembolization (TARE) on pre-interventional cone-beam computed tomography (CBCT) images in patients with liver metastases. Materials and Methods: In this IRB-approved retrospective single-center study 36 patients with a total of 104 liver metastases (56 % male, mean age 61.1 ± 13 years) underwent CBCT prior to TARE and follow-up imaging 6 months after therapy. Treatment response was evaluated according to RECIST version 1.1 and dichotomized into disease control (partial response/stable disease) versus disease progression (progressive disease). After target lesion segmentation, 104 radiomics features corresponding to seven different feature classes were extracted with the pyRadiomics package. After dimension reduction machine learning classifications were performed on a custom artificial neural network (ANN). Ten-fold cross validation on a previously unseen test data set was performed. Results: The average administered cumulative activity from TARE was 1.6 Gbq (± 0.5 Gbq). At a mean follow-up of 5.9 ± 0.8 months disease control was achieved in 82 % of metastases. After dimension reduction, 15 of 104 (15 %) texture analysis features remained for further analysis. On a previously unseen set of liver metastases the Multilayer Perceptron ANN yielded a sensitivity of 94.2 %, specificity of 67.7 % and an area-under-the receiver operating characteristics curve of 0.85. Conclusion: Our study indicates that texture analysis-based machine learning may has potential to predict treatment response to TARE using pre-treatment CBCT images of patients with liver metastases with high accuracy. |
topic |
Radiomics Transarterial radioembolization Machine learning Cone-Beam CT |
url |
http://www.sciencedirect.com/science/article/pii/S2352047721000551 |
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