Cargando…
Investigating the Incidence of Pulmonary Abnormalities as Identified by Parametric Response Mapping in Patients With Lung Cancer Before Radiation Treatment
PURPOSE: Parametric response mapping (PRM) of high-resolution, paired inspiration and expiration computed tomography (CT) scans is a promising analytical imaging technique that is currently used in diagnostic applications and offers the ability to characterize and quantify certain pulmonary patholog...
Autores principales: | , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9184868/ https://www.ncbi.nlm.nih.gov/pubmed/35693252 http://dx.doi.org/10.1016/j.adro.2022.100980 |
_version_ | 1784724623806431232 |
---|---|
author | Owen, Daniel R. Sun, Yilun Irrer, Jim C. Schipper, Matthew J. Schonewolf, Caitlin A. Galbán, Stefanie Jolly, Shruti Haken, Randall K. Ten Galbán, C.J. Matuszak, M.M. |
author_facet | Owen, Daniel R. Sun, Yilun Irrer, Jim C. Schipper, Matthew J. Schonewolf, Caitlin A. Galbán, Stefanie Jolly, Shruti Haken, Randall K. Ten Galbán, C.J. Matuszak, M.M. |
author_sort | Owen, Daniel R. |
collection | PubMed |
description | PURPOSE: Parametric response mapping (PRM) of high-resolution, paired inspiration and expiration computed tomography (CT) scans is a promising analytical imaging technique that is currently used in diagnostic applications and offers the ability to characterize and quantify certain pulmonary pathologies on a patient-specific basis. As one of the first studies to implement such a technique in the radiation oncology clinic, the goal of this work was to assess the feasibility for PRM analysis to identify pulmonary abnormalities in patients with lung cancer before radiation therapy (RT). METHODS AND MATERIALS: High-resolution, paired inspiration and expiration CT scans were acquired from 23 patients with lung cancer as part of routine treatment planning CT acquisition. When applied to the paired CT scans, PRM analysis classifies lung parenchyma, on a voxel-wise basis, as normal, small airways disease (SAD), emphysema, or parenchymal disease (PD). PRM classifications were quantified as a percent of total lung volume and were evaluated globally and regionally within the lung. RESULTS: PRM analysis of pre-RT CT scans was successfully implemented using a workflow that produced patient-specific maps and quantified specific phenotypes of pulmonary abnormalities. Through this study, a large prevalence of SAD and PD was demonstrated in this lung cancer patient population, with global averages of 10% and 17%, respectively. Moreover, PRM-classified normal and SAD in the region with primary tumor involvement were found to be significantly different from global lung values. When present, elevated levels of PD and SAD abnormalities tended to be pervasive in multiple regions of the lung, indicating a large burden of underlying disease. CONCLUSIONS: Pulmonary abnormalities, as detected by PRM, were characterized in patients with lung cancer scheduled for RT. Although further study is needed, PRM is a highly accessible CT-based imaging technique that has the potential to identify local lung abnormalities associated with chronic obstructive pulmonary disease and interstitial lung disease. Further investigation in the radiation oncology setting may provide strategies for tailoring RT planning and risk assessment based on pre-existing PRM-based pathology. |
format | Online Article Text |
id | pubmed-9184868 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-91848682022-06-11 Investigating the Incidence of Pulmonary Abnormalities as Identified by Parametric Response Mapping in Patients With Lung Cancer Before Radiation Treatment Owen, Daniel R. Sun, Yilun Irrer, Jim C. Schipper, Matthew J. Schonewolf, Caitlin A. Galbán, Stefanie Jolly, Shruti Haken, Randall K. Ten Galbán, C.J. Matuszak, M.M. Adv Radiat Oncol Research Letter PURPOSE: Parametric response mapping (PRM) of high-resolution, paired inspiration and expiration computed tomography (CT) scans is a promising analytical imaging technique that is currently used in diagnostic applications and offers the ability to characterize and quantify certain pulmonary pathologies on a patient-specific basis. As one of the first studies to implement such a technique in the radiation oncology clinic, the goal of this work was to assess the feasibility for PRM analysis to identify pulmonary abnormalities in patients with lung cancer before radiation therapy (RT). METHODS AND MATERIALS: High-resolution, paired inspiration and expiration CT scans were acquired from 23 patients with lung cancer as part of routine treatment planning CT acquisition. When applied to the paired CT scans, PRM analysis classifies lung parenchyma, on a voxel-wise basis, as normal, small airways disease (SAD), emphysema, or parenchymal disease (PD). PRM classifications were quantified as a percent of total lung volume and were evaluated globally and regionally within the lung. RESULTS: PRM analysis of pre-RT CT scans was successfully implemented using a workflow that produced patient-specific maps and quantified specific phenotypes of pulmonary abnormalities. Through this study, a large prevalence of SAD and PD was demonstrated in this lung cancer patient population, with global averages of 10% and 17%, respectively. Moreover, PRM-classified normal and SAD in the region with primary tumor involvement were found to be significantly different from global lung values. When present, elevated levels of PD and SAD abnormalities tended to be pervasive in multiple regions of the lung, indicating a large burden of underlying disease. CONCLUSIONS: Pulmonary abnormalities, as detected by PRM, were characterized in patients with lung cancer scheduled for RT. Although further study is needed, PRM is a highly accessible CT-based imaging technique that has the potential to identify local lung abnormalities associated with chronic obstructive pulmonary disease and interstitial lung disease. Further investigation in the radiation oncology setting may provide strategies for tailoring RT planning and risk assessment based on pre-existing PRM-based pathology. Elsevier 2022-04-25 /pmc/articles/PMC9184868/ /pubmed/35693252 http://dx.doi.org/10.1016/j.adro.2022.100980 Text en © 2022 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Research Letter Owen, Daniel R. Sun, Yilun Irrer, Jim C. Schipper, Matthew J. Schonewolf, Caitlin A. Galbán, Stefanie Jolly, Shruti Haken, Randall K. Ten Galbán, C.J. Matuszak, M.M. Investigating the Incidence of Pulmonary Abnormalities as Identified by Parametric Response Mapping in Patients With Lung Cancer Before Radiation Treatment |
title | Investigating the Incidence of Pulmonary Abnormalities as Identified by Parametric Response Mapping in Patients With Lung Cancer Before Radiation Treatment |
title_full | Investigating the Incidence of Pulmonary Abnormalities as Identified by Parametric Response Mapping in Patients With Lung Cancer Before Radiation Treatment |
title_fullStr | Investigating the Incidence of Pulmonary Abnormalities as Identified by Parametric Response Mapping in Patients With Lung Cancer Before Radiation Treatment |
title_full_unstemmed | Investigating the Incidence of Pulmonary Abnormalities as Identified by Parametric Response Mapping in Patients With Lung Cancer Before Radiation Treatment |
title_short | Investigating the Incidence of Pulmonary Abnormalities as Identified by Parametric Response Mapping in Patients With Lung Cancer Before Radiation Treatment |
title_sort | investigating the incidence of pulmonary abnormalities as identified by parametric response mapping in patients with lung cancer before radiation treatment |
topic | Research Letter |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9184868/ https://www.ncbi.nlm.nih.gov/pubmed/35693252 http://dx.doi.org/10.1016/j.adro.2022.100980 |
work_keys_str_mv | AT owendanielr investigatingtheincidenceofpulmonaryabnormalitiesasidentifiedbyparametricresponsemappinginpatientswithlungcancerbeforeradiationtreatment AT sunyilun investigatingtheincidenceofpulmonaryabnormalitiesasidentifiedbyparametricresponsemappinginpatientswithlungcancerbeforeradiationtreatment AT irrerjimc investigatingtheincidenceofpulmonaryabnormalitiesasidentifiedbyparametricresponsemappinginpatientswithlungcancerbeforeradiationtreatment AT schippermatthewj investigatingtheincidenceofpulmonaryabnormalitiesasidentifiedbyparametricresponsemappinginpatientswithlungcancerbeforeradiationtreatment AT schonewolfcaitlina investigatingtheincidenceofpulmonaryabnormalitiesasidentifiedbyparametricresponsemappinginpatientswithlungcancerbeforeradiationtreatment AT galbanstefanie investigatingtheincidenceofpulmonaryabnormalitiesasidentifiedbyparametricresponsemappinginpatientswithlungcancerbeforeradiationtreatment AT jollyshruti investigatingtheincidenceofpulmonaryabnormalitiesasidentifiedbyparametricresponsemappinginpatientswithlungcancerbeforeradiationtreatment AT hakenrandallkten investigatingtheincidenceofpulmonaryabnormalitiesasidentifiedbyparametricresponsemappinginpatientswithlungcancerbeforeradiationtreatment AT galbancj investigatingtheincidenceofpulmonaryabnormalitiesasidentifiedbyparametricresponsemappinginpatientswithlungcancerbeforeradiationtreatment AT matuszakmm investigatingtheincidenceofpulmonaryabnormalitiesasidentifiedbyparametricresponsemappinginpatientswithlungcancerbeforeradiationtreatment |