Prediction of radiation pneumonitis after definitive radiotherapy for locally advanced non-small cell lung cancer using multi-region radiomics analysis

Abstract To predict grade ≥ 2 radiation pneumonitis (RP) in patients with locally advanced non-small cell lung cancer (NSCLC) using multi-region radiomics analysis. Data from 77 patients with NSCLC who underwent definitive radiotherapy between 2008 and 2018 were analyzed. Radiomic feature extraction...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Daisuke Kawahara, Nobuki Imano, Riku Nishioka, Kouta Ogawa, Tomoki Kimura, Taku Nakashima, Hiroshi Iwamoto, Kazunori Fujitaka, Noboru Hattori, Yasushi Nagata
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/162a5a1857c44f82800534b435321ca5
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:162a5a1857c44f82800534b435321ca5
record_format	dspace
spelling	oai:doaj.org-article:162a5a1857c44f82800534b435321ca52021-12-02T15:08:39ZPrediction of radiation pneumonitis after definitive radiotherapy for locally advanced non-small cell lung cancer using multi-region radiomics analysis10.1038/s41598-021-95643-x2045-2322https://doaj.org/article/162a5a1857c44f82800534b435321ca52021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-95643-xhttps://doaj.org/toc/2045-2322Abstract To predict grade ≥ 2 radiation pneumonitis (RP) in patients with locally advanced non-small cell lung cancer (NSCLC) using multi-region radiomics analysis. Data from 77 patients with NSCLC who underwent definitive radiotherapy between 2008 and 2018 were analyzed. Radiomic feature extraction from the whole lung (whole-lung radiomics analysis) and imaging- and dosimetric-based segmentation (multi-region radiomics analysis) were performed. Patients with RP grade ≥ 2 or < 2 were classified. Predictors were selected with least absolute shrinkage and selection operator logistic regression and the model was built with neural network classifiers. A total of 49,383 radiomics features per patient image were extracted from the radiotherapy planning computed tomography. We identified 4 features and 13 radiomics features in the whole-lung and multi-region radiomics analysis for classification, respectively. The accuracy and area under the curve (AUC) without the synthetic minority over-sampling technique (SMOTE) were 60.8%, and 0.62 for whole-lung and 80.1%, and 0.84 for multi-region radiomics analysis. These were improved 1.7% for whole-lung and 2.1% for multi-region radiomics analysis with the SMOTE. The developed multi-region radiomics analysis can help predict grade ≥ 2 RP. The radiomics features in the median- and high-dose regions, and the local intensity roughness and variation were important factors in predicting grade ≥ 2 RP.Daisuke KawaharaNobuki ImanoRiku NishiokaKouta OgawaTomoki KimuraTaku NakashimaHiroshi IwamotoKazunori FujitakaNoboru HattoriYasushi NagataNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Daisuke Kawahara Nobuki Imano Riku Nishioka Kouta Ogawa Tomoki Kimura Taku Nakashima Hiroshi Iwamoto Kazunori Fujitaka Noboru Hattori Yasushi Nagata Prediction of radiation pneumonitis after definitive radiotherapy for locally advanced non-small cell lung cancer using multi-region radiomics analysis
description	Abstract To predict grade ≥ 2 radiation pneumonitis (RP) in patients with locally advanced non-small cell lung cancer (NSCLC) using multi-region radiomics analysis. Data from 77 patients with NSCLC who underwent definitive radiotherapy between 2008 and 2018 were analyzed. Radiomic feature extraction from the whole lung (whole-lung radiomics analysis) and imaging- and dosimetric-based segmentation (multi-region radiomics analysis) were performed. Patients with RP grade ≥ 2 or < 2 were classified. Predictors were selected with least absolute shrinkage and selection operator logistic regression and the model was built with neural network classifiers. A total of 49,383 radiomics features per patient image were extracted from the radiotherapy planning computed tomography. We identified 4 features and 13 radiomics features in the whole-lung and multi-region radiomics analysis for classification, respectively. The accuracy and area under the curve (AUC) without the synthetic minority over-sampling technique (SMOTE) were 60.8%, and 0.62 for whole-lung and 80.1%, and 0.84 for multi-region radiomics analysis. These were improved 1.7% for whole-lung and 2.1% for multi-region radiomics analysis with the SMOTE. The developed multi-region radiomics analysis can help predict grade ≥ 2 RP. The radiomics features in the median- and high-dose regions, and the local intensity roughness and variation were important factors in predicting grade ≥ 2 RP.
format	article
author	Daisuke Kawahara Nobuki Imano Riku Nishioka Kouta Ogawa Tomoki Kimura Taku Nakashima Hiroshi Iwamoto Kazunori Fujitaka Noboru Hattori Yasushi Nagata
author_facet	Daisuke Kawahara Nobuki Imano Riku Nishioka Kouta Ogawa Tomoki Kimura Taku Nakashima Hiroshi Iwamoto Kazunori Fujitaka Noboru Hattori Yasushi Nagata
author_sort	Daisuke Kawahara
title	Prediction of radiation pneumonitis after definitive radiotherapy for locally advanced non-small cell lung cancer using multi-region radiomics analysis
title_short	Prediction of radiation pneumonitis after definitive radiotherapy for locally advanced non-small cell lung cancer using multi-region radiomics analysis
title_full	Prediction of radiation pneumonitis after definitive radiotherapy for locally advanced non-small cell lung cancer using multi-region radiomics analysis
title_fullStr	Prediction of radiation pneumonitis after definitive radiotherapy for locally advanced non-small cell lung cancer using multi-region radiomics analysis
title_full_unstemmed	Prediction of radiation pneumonitis after definitive radiotherapy for locally advanced non-small cell lung cancer using multi-region radiomics analysis
title_sort	prediction of radiation pneumonitis after definitive radiotherapy for locally advanced non-small cell lung cancer using multi-region radiomics analysis
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/162a5a1857c44f82800534b435321ca5
work_keys_str_mv	AT daisukekawahara predictionofradiationpneumonitisafterdefinitiveradiotherapyforlocallyadvancednonsmallcelllungcancerusingmultiregionradiomicsanalysis AT nobukiimano predictionofradiationpneumonitisafterdefinitiveradiotherapyforlocallyadvancednonsmallcelllungcancerusingmultiregionradiomicsanalysis AT rikunishioka predictionofradiationpneumonitisafterdefinitiveradiotherapyforlocallyadvancednonsmallcelllungcancerusingmultiregionradiomicsanalysis AT koutaogawa predictionofradiationpneumonitisafterdefinitiveradiotherapyforlocallyadvancednonsmallcelllungcancerusingmultiregionradiomicsanalysis AT tomokikimura predictionofradiationpneumonitisafterdefinitiveradiotherapyforlocallyadvancednonsmallcelllungcancerusingmultiregionradiomicsanalysis AT takunakashima predictionofradiationpneumonitisafterdefinitiveradiotherapyforlocallyadvancednonsmallcelllungcancerusingmultiregionradiomicsanalysis AT hiroshiiwamoto predictionofradiationpneumonitisafterdefinitiveradiotherapyforlocallyadvancednonsmallcelllungcancerusingmultiregionradiomicsanalysis AT kazunorifujitaka predictionofradiationpneumonitisafterdefinitiveradiotherapyforlocallyadvancednonsmallcelllungcancerusingmultiregionradiomicsanalysis AT noboruhattori predictionofradiationpneumonitisafterdefinitiveradiotherapyforlocallyadvancednonsmallcelllungcancerusingmultiregionradiomicsanalysis AT yasushinagata predictionofradiationpneumonitisafterdefinitiveradiotherapyforlocallyadvancednonsmallcelllungcancerusingmultiregionradiomicsanalysis
_version_	1718388044586811392

Prediction of radiation pneumonitis after definitive radiotherapy for locally advanced non-small cell lung cancer using multi-region radiomics analysis

Ejemplares similares