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Radiomics based targeted radiotherapy planning (Rad-TRaP): a computational framework for prostate cancer treatment planning with MRI

BACKGROUND: Radiomics or computer – extracted texture features have been shown to achieve superior performance than multiparametric MRI (mpMRI) signal intensities alone in targeting prostate cancer (PCa) lesions. Radiomics along with deformable co-registration tools can be used to develop a framewor...

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Autores principales: Shiradkar, Rakesh, Podder, Tarun K, Algohary, Ahmad, Viswanath, Satish, Ellis, Rodney J., Madabhushi, Anant
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5103611/
https://www.ncbi.nlm.nih.gov/pubmed/27829431
http://dx.doi.org/10.1186/s13014-016-0718-3
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author Shiradkar, Rakesh
Podder, Tarun K
Algohary, Ahmad
Viswanath, Satish
Ellis, Rodney J.
Madabhushi, Anant
author_facet Shiradkar, Rakesh
Podder, Tarun K
Algohary, Ahmad
Viswanath, Satish
Ellis, Rodney J.
Madabhushi, Anant
author_sort Shiradkar, Rakesh
collection PubMed
description BACKGROUND: Radiomics or computer – extracted texture features have been shown to achieve superior performance than multiparametric MRI (mpMRI) signal intensities alone in targeting prostate cancer (PCa) lesions. Radiomics along with deformable co-registration tools can be used to develop a framework to generate targeted focal radiotherapy treatment plans. METHODS: The Rad-TRaP framework comprises three distinct modules. Firstly, a module for radiomics based detection of PCa lesions on mpMRI via a feature enabled machine learning classifier. The second module comprises a multi-modal deformable co-registration scheme to map tissue, organ, and delineated target volumes from MRI onto CT. Finally, the third module involves generation of a radiomics based dose plan on MRI for brachytherapy and on CT for EBRT using the target delineations transferred from the MRI to the CT. RESULTS: Rad-TRaP framework was evaluated using a retrospective cohort of 23 patient studies from two different institutions. 11 patients from the first institution were used to train a radiomics classifier, which was used to detect tumor regions in 12 patients from the second institution. The ground truth cancer delineations for training the machine learning classifier were made by an experienced radiation oncologist using mpMRI, knowledge of biopsy location and radiology reports. The detected tumor regions were used to generate treatment plans for brachytherapy using mpMRI, and tumor regions mapped from MRI to CT to generate corresponding treatment plans for EBRT. For each of EBRT and brachytherapy, 3 dose plans were generated - whole gland homogeneous ([Formula: see text] ) which is the current clinical standard, radiomics based focal ([Formula: see text] ), and whole gland with a radiomics based focal boost ([Formula: see text] ). Comparison of [Formula: see text] against conventional [Formula: see text] revealed that targeted focal brachytherapy would result in a marked reduction in dosage to the OARs while ensuring that the prescribed dose is delivered to the lesions. [Formula: see text] resulted in only a marginal increase in dosage to the OARs compared to [Formula: see text] . A similar trend was observed in case of EBRT with [Formula: see text] and [Formula: see text] compared to [Formula: see text] . CONCLUSIONS: A radiotherapy planning framework to generate targeted focal treatment plans has been presented. The focal treatment plans generated using the framework showed reduction in dosage to the organs at risk and a boosted dose delivered to the cancerous lesions.
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spelling pubmed-51036112016-11-14 Radiomics based targeted radiotherapy planning (Rad-TRaP): a computational framework for prostate cancer treatment planning with MRI Shiradkar, Rakesh Podder, Tarun K Algohary, Ahmad Viswanath, Satish Ellis, Rodney J. Madabhushi, Anant Radiat Oncol Research BACKGROUND: Radiomics or computer – extracted texture features have been shown to achieve superior performance than multiparametric MRI (mpMRI) signal intensities alone in targeting prostate cancer (PCa) lesions. Radiomics along with deformable co-registration tools can be used to develop a framework to generate targeted focal radiotherapy treatment plans. METHODS: The Rad-TRaP framework comprises three distinct modules. Firstly, a module for radiomics based detection of PCa lesions on mpMRI via a feature enabled machine learning classifier. The second module comprises a multi-modal deformable co-registration scheme to map tissue, organ, and delineated target volumes from MRI onto CT. Finally, the third module involves generation of a radiomics based dose plan on MRI for brachytherapy and on CT for EBRT using the target delineations transferred from the MRI to the CT. RESULTS: Rad-TRaP framework was evaluated using a retrospective cohort of 23 patient studies from two different institutions. 11 patients from the first institution were used to train a radiomics classifier, which was used to detect tumor regions in 12 patients from the second institution. The ground truth cancer delineations for training the machine learning classifier were made by an experienced radiation oncologist using mpMRI, knowledge of biopsy location and radiology reports. The detected tumor regions were used to generate treatment plans for brachytherapy using mpMRI, and tumor regions mapped from MRI to CT to generate corresponding treatment plans for EBRT. For each of EBRT and brachytherapy, 3 dose plans were generated - whole gland homogeneous ([Formula: see text] ) which is the current clinical standard, radiomics based focal ([Formula: see text] ), and whole gland with a radiomics based focal boost ([Formula: see text] ). Comparison of [Formula: see text] against conventional [Formula: see text] revealed that targeted focal brachytherapy would result in a marked reduction in dosage to the OARs while ensuring that the prescribed dose is delivered to the lesions. [Formula: see text] resulted in only a marginal increase in dosage to the OARs compared to [Formula: see text] . A similar trend was observed in case of EBRT with [Formula: see text] and [Formula: see text] compared to [Formula: see text] . CONCLUSIONS: A radiotherapy planning framework to generate targeted focal treatment plans has been presented. The focal treatment plans generated using the framework showed reduction in dosage to the organs at risk and a boosted dose delivered to the cancerous lesions. BioMed Central 2016-11-10 /pmc/articles/PMC5103611/ /pubmed/27829431 http://dx.doi.org/10.1186/s13014-016-0718-3 Text en © The Author(s) 2016 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Shiradkar, Rakesh
Podder, Tarun K
Algohary, Ahmad
Viswanath, Satish
Ellis, Rodney J.
Madabhushi, Anant
Radiomics based targeted radiotherapy planning (Rad-TRaP): a computational framework for prostate cancer treatment planning with MRI
title Radiomics based targeted radiotherapy planning (Rad-TRaP): a computational framework for prostate cancer treatment planning with MRI
title_full Radiomics based targeted radiotherapy planning (Rad-TRaP): a computational framework for prostate cancer treatment planning with MRI
title_fullStr Radiomics based targeted radiotherapy planning (Rad-TRaP): a computational framework for prostate cancer treatment planning with MRI
title_full_unstemmed Radiomics based targeted radiotherapy planning (Rad-TRaP): a computational framework for prostate cancer treatment planning with MRI
title_short Radiomics based targeted radiotherapy planning (Rad-TRaP): a computational framework for prostate cancer treatment planning with MRI
title_sort radiomics based targeted radiotherapy planning (rad-trap): a computational framework for prostate cancer treatment planning with mri
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5103611/
https://www.ncbi.nlm.nih.gov/pubmed/27829431
http://dx.doi.org/10.1186/s13014-016-0718-3
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