Cargando…
Dosiomics: Extracting 3D Spatial Features From Dose Distribution to Predict Incidence of Radiation Pneumonitis
Radiation pneumonitis (RP) is one of the major toxicities of thoracic radiation therapy. RP incidence has been proven to be closely associated with the dosimetric factors and normal tissue control possibility (NTCP) factors. However, because these factors only utilize limited information of the dose...
Autores principales: | , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6473398/ https://www.ncbi.nlm.nih.gov/pubmed/31032229 http://dx.doi.org/10.3389/fonc.2019.00269 |
_version_ | 1783412421256282112 |
---|---|
author | Liang, Bin Yan, Hui Tian, Yuan Chen, Xinyuan Yan, Lingling Zhang, Tao Zhou, Zongmei Wang, Lvhua Dai, Jianrong |
author_facet | Liang, Bin Yan, Hui Tian, Yuan Chen, Xinyuan Yan, Lingling Zhang, Tao Zhou, Zongmei Wang, Lvhua Dai, Jianrong |
author_sort | Liang, Bin |
collection | PubMed |
description | Radiation pneumonitis (RP) is one of the major toxicities of thoracic radiation therapy. RP incidence has been proven to be closely associated with the dosimetric factors and normal tissue control possibility (NTCP) factors. However, because these factors only utilize limited information of the dose distribution, the prediction abilities of these factors are modest. We adopted the dosiomics method for RP prediction. The dosiomics method first extracts spatial features of the dose distribution within ipsilateral, contralateral, and total lungs, and then uses these extracted features to construct prediction model via univariate and multivariate logistic regression (LR). The dosiomics method is validated using 70 non-small cell lung cancer (NSCLC) patients treated with volumetric modulated arc therapy (VMAT) radiotherapy. Dosimetric and NTCP factors based prediction models are also constructed to compare with the dosiomics features based prediction model. For the dosimetric, NTCP and dosiomics factors/features, the most significant single factors/features are the mean dose, parallel/serial (PS) NTCP and gray level co-occurrence matrix (GLCM) contrast of ipsilateral lung, respectively. And the area under curve (AUC) of univariate LR is 0.665, 0.710 and 0.709, respectively. The second significant factors are V(5) of contralateral lung, equivalent uniform dose (EUD) derived from PS NTCP of contralateral lung and the low gray level run emphasis of gray level run length matrix (GLRLM) of total lungs. The AUC of multivariate LR is improved to 0.676, 0.744, and 0.782, respectively. The results demonstrate that the univariate LR of dosiomics features has approximate predictive ability with NTCP factors, and the multivariate LR outperforms both the dosimetric and NTCP factors. In conclusion, the spatial features of dose distribution extracted by the dosiomics method effectively improves the prediction ability. |
format | Online Article Text |
id | pubmed-6473398 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-64733982019-04-26 Dosiomics: Extracting 3D Spatial Features From Dose Distribution to Predict Incidence of Radiation Pneumonitis Liang, Bin Yan, Hui Tian, Yuan Chen, Xinyuan Yan, Lingling Zhang, Tao Zhou, Zongmei Wang, Lvhua Dai, Jianrong Front Oncol Oncology Radiation pneumonitis (RP) is one of the major toxicities of thoracic radiation therapy. RP incidence has been proven to be closely associated with the dosimetric factors and normal tissue control possibility (NTCP) factors. However, because these factors only utilize limited information of the dose distribution, the prediction abilities of these factors are modest. We adopted the dosiomics method for RP prediction. The dosiomics method first extracts spatial features of the dose distribution within ipsilateral, contralateral, and total lungs, and then uses these extracted features to construct prediction model via univariate and multivariate logistic regression (LR). The dosiomics method is validated using 70 non-small cell lung cancer (NSCLC) patients treated with volumetric modulated arc therapy (VMAT) radiotherapy. Dosimetric and NTCP factors based prediction models are also constructed to compare with the dosiomics features based prediction model. For the dosimetric, NTCP and dosiomics factors/features, the most significant single factors/features are the mean dose, parallel/serial (PS) NTCP and gray level co-occurrence matrix (GLCM) contrast of ipsilateral lung, respectively. And the area under curve (AUC) of univariate LR is 0.665, 0.710 and 0.709, respectively. The second significant factors are V(5) of contralateral lung, equivalent uniform dose (EUD) derived from PS NTCP of contralateral lung and the low gray level run emphasis of gray level run length matrix (GLRLM) of total lungs. The AUC of multivariate LR is improved to 0.676, 0.744, and 0.782, respectively. The results demonstrate that the univariate LR of dosiomics features has approximate predictive ability with NTCP factors, and the multivariate LR outperforms both the dosimetric and NTCP factors. In conclusion, the spatial features of dose distribution extracted by the dosiomics method effectively improves the prediction ability. Frontiers Media S.A. 2019-04-12 /pmc/articles/PMC6473398/ /pubmed/31032229 http://dx.doi.org/10.3389/fonc.2019.00269 Text en Copyright © 2019 Liang, Yan, Tian, Chen, Yan, Zhang, Zhou, Wang and Dai. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Oncology Liang, Bin Yan, Hui Tian, Yuan Chen, Xinyuan Yan, Lingling Zhang, Tao Zhou, Zongmei Wang, Lvhua Dai, Jianrong Dosiomics: Extracting 3D Spatial Features From Dose Distribution to Predict Incidence of Radiation Pneumonitis |
title | Dosiomics: Extracting 3D Spatial Features From Dose Distribution to Predict Incidence of Radiation Pneumonitis |
title_full | Dosiomics: Extracting 3D Spatial Features From Dose Distribution to Predict Incidence of Radiation Pneumonitis |
title_fullStr | Dosiomics: Extracting 3D Spatial Features From Dose Distribution to Predict Incidence of Radiation Pneumonitis |
title_full_unstemmed | Dosiomics: Extracting 3D Spatial Features From Dose Distribution to Predict Incidence of Radiation Pneumonitis |
title_short | Dosiomics: Extracting 3D Spatial Features From Dose Distribution to Predict Incidence of Radiation Pneumonitis |
title_sort | dosiomics: extracting 3d spatial features from dose distribution to predict incidence of radiation pneumonitis |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6473398/ https://www.ncbi.nlm.nih.gov/pubmed/31032229 http://dx.doi.org/10.3389/fonc.2019.00269 |
work_keys_str_mv | AT liangbin dosiomicsextracting3dspatialfeaturesfromdosedistributiontopredictincidenceofradiationpneumonitis AT yanhui dosiomicsextracting3dspatialfeaturesfromdosedistributiontopredictincidenceofradiationpneumonitis AT tianyuan dosiomicsextracting3dspatialfeaturesfromdosedistributiontopredictincidenceofradiationpneumonitis AT chenxinyuan dosiomicsextracting3dspatialfeaturesfromdosedistributiontopredictincidenceofradiationpneumonitis AT yanlingling dosiomicsextracting3dspatialfeaturesfromdosedistributiontopredictincidenceofradiationpneumonitis AT zhangtao dosiomicsextracting3dspatialfeaturesfromdosedistributiontopredictincidenceofradiationpneumonitis AT zhouzongmei dosiomicsextracting3dspatialfeaturesfromdosedistributiontopredictincidenceofradiationpneumonitis AT wanglvhua dosiomicsextracting3dspatialfeaturesfromdosedistributiontopredictincidenceofradiationpneumonitis AT daijianrong dosiomicsextracting3dspatialfeaturesfromdosedistributiontopredictincidenceofradiationpneumonitis |