An artificial neural networks approach for assessment treatment response in oncological patients using PET/CT images

Background Positron Emission Tomography – Computed Tomography (PET/CT) imaging is the basis for the evaluation of response-to-treatment of several oncological diseases. In practice, such evaluation is manually performed by specialists, which is rather complex and time-consuming. Evaluation measures...
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Autor*in:	Nogueira, Mariana A. [verfasserIn] Abreu, Pedro H. Martins, Pedro Machado, Penousal Duarte, Hugo Santos, João

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2017

Schlagwörter:	Artificial neural networks Images descriptors PET/CT images Treatment response assessment

Anmerkung:	© The Author(s) 2017

Übergeordnetes Werk:	Enthalten in: BMC medical imaging - London : BioMed Central, 2001, 17(2017), 1 vom: 13. Feb.
Übergeordnetes Werk:	volume:17 ; year:2017 ; number:1 ; day:13 ; month:02

Links:	Volltext

DOI / URN:	10.1186/s12880-017-0181-0

Katalog-ID:	SPR027476901

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520			\|a Background Positron Emission Tomography – Computed Tomography (PET/CT) imaging is the basis for the evaluation of response-to-treatment of several oncological diseases. In practice, such evaluation is manually performed by specialists, which is rather complex and time-consuming. Evaluation measures have been proposed, but with questionable reliability. The usage of before and after-treatment image descriptors of the lesions for treatment response evaluation is still a territory to be explored. Methods In this project, Artificial Neural Network approaches were implemented to automatically assess treatment response of patients suffering from neuroendocrine tumors and Hodgkyn lymphoma, based on image features extracted from PET/CT. Results The results show that the considered set of features allows for the achievement of very high classification performances, especially when data is properly balanced. Conclusions After synthetic data generation and PCA-based dimensionality reduction to only two components, LVQNN assured classification accuracies of 100%, 100%, 96.3% and 100% regarding the 4 response-to-treatment classes.
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allfields	10.1186/s12880-017-0181-0 doi (DE-627)SPR027476901 (SPR)s12880-017-0181-0-e DE-627 ger DE-627 rakwb eng Nogueira, Mariana A. verfasserin aut An artificial neural networks approach for assessment treatment response in oncological patients using PET/CT images 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2017 Background Positron Emission Tomography – Computed Tomography (PET/CT) imaging is the basis for the evaluation of response-to-treatment of several oncological diseases. In practice, such evaluation is manually performed by specialists, which is rather complex and time-consuming. Evaluation measures have been proposed, but with questionable reliability. The usage of before and after-treatment image descriptors of the lesions for treatment response evaluation is still a territory to be explored. Methods In this project, Artificial Neural Network approaches were implemented to automatically assess treatment response of patients suffering from neuroendocrine tumors and Hodgkyn lymphoma, based on image features extracted from PET/CT. Results The results show that the considered set of features allows for the achievement of very high classification performances, especially when data is properly balanced. Conclusions After synthetic data generation and PCA-based dimensionality reduction to only two components, LVQNN assured classification accuracies of 100%, 100%, 96.3% and 100% regarding the 4 response-to-treatment classes. Artificial neural networks (dpeaa)DE-He213 Images descriptors (dpeaa)DE-He213 PET/CT images (dpeaa)DE-He213 Treatment response assessment (dpeaa)DE-He213 Abreu, Pedro H. aut Martins, Pedro aut Machado, Penousal aut Duarte, Hugo aut Santos, João aut Enthalten in BMC medical imaging London : BioMed Central, 2001 17(2017), 1 vom: 13. Feb. (DE-627)33679911X (DE-600)2061975-3 1471-2342 nnns volume:17 year:2017 number:1 day:13 month:02 https://dx.doi.org/10.1186/s12880-017-0181-0 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 17 2017 1 13 02
spelling	10.1186/s12880-017-0181-0 doi (DE-627)SPR027476901 (SPR)s12880-017-0181-0-e DE-627 ger DE-627 rakwb eng Nogueira, Mariana A. verfasserin aut An artificial neural networks approach for assessment treatment response in oncological patients using PET/CT images 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2017 Background Positron Emission Tomography – Computed Tomography (PET/CT) imaging is the basis for the evaluation of response-to-treatment of several oncological diseases. In practice, such evaluation is manually performed by specialists, which is rather complex and time-consuming. Evaluation measures have been proposed, but with questionable reliability. The usage of before and after-treatment image descriptors of the lesions for treatment response evaluation is still a territory to be explored. Methods In this project, Artificial Neural Network approaches were implemented to automatically assess treatment response of patients suffering from neuroendocrine tumors and Hodgkyn lymphoma, based on image features extracted from PET/CT. Results The results show that the considered set of features allows for the achievement of very high classification performances, especially when data is properly balanced. Conclusions After synthetic data generation and PCA-based dimensionality reduction to only two components, LVQNN assured classification accuracies of 100%, 100%, 96.3% and 100% regarding the 4 response-to-treatment classes. Artificial neural networks (dpeaa)DE-He213 Images descriptors (dpeaa)DE-He213 PET/CT images (dpeaa)DE-He213 Treatment response assessment (dpeaa)DE-He213 Abreu, Pedro H. aut Martins, Pedro aut Machado, Penousal aut Duarte, Hugo aut Santos, João aut Enthalten in BMC medical imaging London : BioMed Central, 2001 17(2017), 1 vom: 13. Feb. (DE-627)33679911X (DE-600)2061975-3 1471-2342 nnns volume:17 year:2017 number:1 day:13 month:02 https://dx.doi.org/10.1186/s12880-017-0181-0 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 17 2017 1 13 02
allfields_unstemmed	10.1186/s12880-017-0181-0 doi (DE-627)SPR027476901 (SPR)s12880-017-0181-0-e DE-627 ger DE-627 rakwb eng Nogueira, Mariana A. verfasserin aut An artificial neural networks approach for assessment treatment response in oncological patients using PET/CT images 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2017 Background Positron Emission Tomography – Computed Tomography (PET/CT) imaging is the basis for the evaluation of response-to-treatment of several oncological diseases. In practice, such evaluation is manually performed by specialists, which is rather complex and time-consuming. Evaluation measures have been proposed, but with questionable reliability. The usage of before and after-treatment image descriptors of the lesions for treatment response evaluation is still a territory to be explored. Methods In this project, Artificial Neural Network approaches were implemented to automatically assess treatment response of patients suffering from neuroendocrine tumors and Hodgkyn lymphoma, based on image features extracted from PET/CT. Results The results show that the considered set of features allows for the achievement of very high classification performances, especially when data is properly balanced. Conclusions After synthetic data generation and PCA-based dimensionality reduction to only two components, LVQNN assured classification accuracies of 100%, 100%, 96.3% and 100% regarding the 4 response-to-treatment classes. Artificial neural networks (dpeaa)DE-He213 Images descriptors (dpeaa)DE-He213 PET/CT images (dpeaa)DE-He213 Treatment response assessment (dpeaa)DE-He213 Abreu, Pedro H. aut Martins, Pedro aut Machado, Penousal aut Duarte, Hugo aut Santos, João aut Enthalten in BMC medical imaging London : BioMed Central, 2001 17(2017), 1 vom: 13. Feb. (DE-627)33679911X (DE-600)2061975-3 1471-2342 nnns volume:17 year:2017 number:1 day:13 month:02 https://dx.doi.org/10.1186/s12880-017-0181-0 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 17 2017 1 13 02
allfieldsGer	10.1186/s12880-017-0181-0 doi (DE-627)SPR027476901 (SPR)s12880-017-0181-0-e DE-627 ger DE-627 rakwb eng Nogueira, Mariana A. verfasserin aut An artificial neural networks approach for assessment treatment response in oncological patients using PET/CT images 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2017 Background Positron Emission Tomography – Computed Tomography (PET/CT) imaging is the basis for the evaluation of response-to-treatment of several oncological diseases. In practice, such evaluation is manually performed by specialists, which is rather complex and time-consuming. Evaluation measures have been proposed, but with questionable reliability. The usage of before and after-treatment image descriptors of the lesions for treatment response evaluation is still a territory to be explored. Methods In this project, Artificial Neural Network approaches were implemented to automatically assess treatment response of patients suffering from neuroendocrine tumors and Hodgkyn lymphoma, based on image features extracted from PET/CT. Results The results show that the considered set of features allows for the achievement of very high classification performances, especially when data is properly balanced. Conclusions After synthetic data generation and PCA-based dimensionality reduction to only two components, LVQNN assured classification accuracies of 100%, 100%, 96.3% and 100% regarding the 4 response-to-treatment classes. Artificial neural networks (dpeaa)DE-He213 Images descriptors (dpeaa)DE-He213 PET/CT images (dpeaa)DE-He213 Treatment response assessment (dpeaa)DE-He213 Abreu, Pedro H. aut Martins, Pedro aut Machado, Penousal aut Duarte, Hugo aut Santos, João aut Enthalten in BMC medical imaging London : BioMed Central, 2001 17(2017), 1 vom: 13. Feb. (DE-627)33679911X (DE-600)2061975-3 1471-2342 nnns volume:17 year:2017 number:1 day:13 month:02 https://dx.doi.org/10.1186/s12880-017-0181-0 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 17 2017 1 13 02
allfieldsSound	10.1186/s12880-017-0181-0 doi (DE-627)SPR027476901 (SPR)s12880-017-0181-0-e DE-627 ger DE-627 rakwb eng Nogueira, Mariana A. verfasserin aut An artificial neural networks approach for assessment treatment response in oncological patients using PET/CT images 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2017 Background Positron Emission Tomography – Computed Tomography (PET/CT) imaging is the basis for the evaluation of response-to-treatment of several oncological diseases. In practice, such evaluation is manually performed by specialists, which is rather complex and time-consuming. Evaluation measures have been proposed, but with questionable reliability. The usage of before and after-treatment image descriptors of the lesions for treatment response evaluation is still a territory to be explored. Methods In this project, Artificial Neural Network approaches were implemented to automatically assess treatment response of patients suffering from neuroendocrine tumors and Hodgkyn lymphoma, based on image features extracted from PET/CT. Results The results show that the considered set of features allows for the achievement of very high classification performances, especially when data is properly balanced. Conclusions After synthetic data generation and PCA-based dimensionality reduction to only two components, LVQNN assured classification accuracies of 100%, 100%, 96.3% and 100% regarding the 4 response-to-treatment classes. Artificial neural networks (dpeaa)DE-He213 Images descriptors (dpeaa)DE-He213 PET/CT images (dpeaa)DE-He213 Treatment response assessment (dpeaa)DE-He213 Abreu, Pedro H. aut Martins, Pedro aut Machado, Penousal aut Duarte, Hugo aut Santos, João aut Enthalten in BMC medical imaging London : BioMed Central, 2001 17(2017), 1 vom: 13. Feb. (DE-627)33679911X (DE-600)2061975-3 1471-2342 nnns volume:17 year:2017 number:1 day:13 month:02 https://dx.doi.org/10.1186/s12880-017-0181-0 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 17 2017 1 13 02
language	English
source	Enthalten in BMC medical imaging 17(2017), 1 vom: 13. Feb. volume:17 year:2017 number:1 day:13 month:02
sourceStr	Enthalten in BMC medical imaging 17(2017), 1 vom: 13. Feb. volume:17 year:2017 number:1 day:13 month:02
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institution	findex.gbv.de
topic_facet	Artificial neural networks Images descriptors PET/CT images Treatment response assessment
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authorswithroles_txt_mv	Nogueira, Mariana A. @@aut@@ Abreu, Pedro H. @@aut@@ Martins, Pedro @@aut@@ Machado, Penousal @@aut@@ Duarte, Hugo @@aut@@ Santos, João @@aut@@
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author	Nogueira, Mariana A.
spellingShingle	Nogueira, Mariana A. misc Artificial neural networks misc Images descriptors misc PET/CT images misc Treatment response assessment An artificial neural networks approach for assessment treatment response in oncological patients using PET/CT images
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topic_title	An artificial neural networks approach for assessment treatment response in oncological patients using PET/CT images Artificial neural networks (dpeaa)DE-He213 Images descriptors (dpeaa)DE-He213 PET/CT images (dpeaa)DE-He213 Treatment response assessment (dpeaa)DE-He213
topic	misc Artificial neural networks misc Images descriptors misc PET/CT images misc Treatment response assessment
topic_unstemmed	misc Artificial neural networks misc Images descriptors misc PET/CT images misc Treatment response assessment
topic_browse	misc Artificial neural networks misc Images descriptors misc PET/CT images misc Treatment response assessment
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title	An artificial neural networks approach for assessment treatment response in oncological patients using PET/CT images
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title_full	An artificial neural networks approach for assessment treatment response in oncological patients using PET/CT images
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author_browse	Nogueira, Mariana A. Abreu, Pedro H. Martins, Pedro Machado, Penousal Duarte, Hugo Santos, João
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title_sort	artificial neural networks approach for assessment treatment response in oncological patients using pet/ct images
title_auth	An artificial neural networks approach for assessment treatment response in oncological patients using PET/CT images
abstract	Background Positron Emission Tomography – Computed Tomography (PET/CT) imaging is the basis for the evaluation of response-to-treatment of several oncological diseases. In practice, such evaluation is manually performed by specialists, which is rather complex and time-consuming. Evaluation measures have been proposed, but with questionable reliability. The usage of before and after-treatment image descriptors of the lesions for treatment response evaluation is still a territory to be explored. Methods In this project, Artificial Neural Network approaches were implemented to automatically assess treatment response of patients suffering from neuroendocrine tumors and Hodgkyn lymphoma, based on image features extracted from PET/CT. Results The results show that the considered set of features allows for the achievement of very high classification performances, especially when data is properly balanced. Conclusions After synthetic data generation and PCA-based dimensionality reduction to only two components, LVQNN assured classification accuracies of 100%, 100%, 96.3% and 100% regarding the 4 response-to-treatment classes. © The Author(s) 2017
abstractGer	Background Positron Emission Tomography – Computed Tomography (PET/CT) imaging is the basis for the evaluation of response-to-treatment of several oncological diseases. In practice, such evaluation is manually performed by specialists, which is rather complex and time-consuming. Evaluation measures have been proposed, but with questionable reliability. The usage of before and after-treatment image descriptors of the lesions for treatment response evaluation is still a territory to be explored. Methods In this project, Artificial Neural Network approaches were implemented to automatically assess treatment response of patients suffering from neuroendocrine tumors and Hodgkyn lymphoma, based on image features extracted from PET/CT. Results The results show that the considered set of features allows for the achievement of very high classification performances, especially when data is properly balanced. Conclusions After synthetic data generation and PCA-based dimensionality reduction to only two components, LVQNN assured classification accuracies of 100%, 100%, 96.3% and 100% regarding the 4 response-to-treatment classes. © The Author(s) 2017
abstract_unstemmed	Background Positron Emission Tomography – Computed Tomography (PET/CT) imaging is the basis for the evaluation of response-to-treatment of several oncological diseases. In practice, such evaluation is manually performed by specialists, which is rather complex and time-consuming. Evaluation measures have been proposed, but with questionable reliability. The usage of before and after-treatment image descriptors of the lesions for treatment response evaluation is still a territory to be explored. Methods In this project, Artificial Neural Network approaches were implemented to automatically assess treatment response of patients suffering from neuroendocrine tumors and Hodgkyn lymphoma, based on image features extracted from PET/CT. Results The results show that the considered set of features allows for the achievement of very high classification performances, especially when data is properly balanced. Conclusions After synthetic data generation and PCA-based dimensionality reduction to only two components, LVQNN assured classification accuracies of 100%, 100%, 96.3% and 100% regarding the 4 response-to-treatment classes. © The Author(s) 2017
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title_short	An artificial neural networks approach for assessment treatment response in oncological patients using PET/CT images
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author2	Abreu, Pedro H. Martins, Pedro Machado, Penousal Duarte, Hugo Santos, João
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An artificial neural networks approach for assessment treatment response in oncological patients using PET/CT images

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