Radiomics is feasible for prediction of spread through air spaces in patients with nonsmall cell lung cancer

Abstract Tumor spread through air spaces (STAS) in non-small-cell lung cancer (NSCLC) is known to influence a poor patient outcome, even in patients presenting with early-stage disease. However, the pre-operative diagnosis of STAS remains challenging. With the progress of radiomics-based analyses se...

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Autores principales:	Yuki Onozato, Takahiro Nakajima, Hajime Yokota, Jyunichi Morimoto, Akira Nishiyama, Takahide Toyoda, Terunaga Inage, Kazuhisa Tanaka, Yuichi Sakairi, Hidemi Suzuki, Takashi Uno, Ichiro Yoshino
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a3a4c781ec0f4f2e88b0e892e423e60d
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spelling	oai:doaj.org-article:a3a4c781ec0f4f2e88b0e892e423e60d2021-12-02T18:18:59ZRadiomics is feasible for prediction of spread through air spaces in patients with nonsmall cell lung cancer10.1038/s41598-021-93002-42045-2322https://doaj.org/article/a3a4c781ec0f4f2e88b0e892e423e60d2021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-93002-4https://doaj.org/toc/2045-2322Abstract Tumor spread through air spaces (STAS) in non-small-cell lung cancer (NSCLC) is known to influence a poor patient outcome, even in patients presenting with early-stage disease. However, the pre-operative diagnosis of STAS remains challenging. With the progress of radiomics-based analyses several attempts have been made to predict STAS based on radiological findings. In the present study, patients with NSCLC which is located peripherally and tumors ≤ 2 cm in size on computed tomography (CT) that were potential candidates for sublobar resection were enrolled in this study. The radiologic features of the targeted tumors on thin-section CT were extracted using the PyRadiomics v3.0 software package, and a predictive model for STAS was built using the t-test and XGBoost. Thirty-five out of 226 patients had a STAS histology. The predictive model of STAS indicated an area under the receiver-operator characteristic curve (AUC) of 0.77. There was no significant difference in the overall survival (OS) for lobectomy between the predicted-STAS (+) and (−) groups (p = 0.19), but an unfavorable OS for sublobar resection was indicated in the predicted-STAS (+) group (p < 0.01). These results suggest that radiomics with machine-learning helped to develop a favorable model of STAS (+) NSCLC, which might be useful for the proper selection of candidates who should undergo sublobar resection.Yuki OnozatoTakahiro NakajimaHajime YokotaJyunichi MorimotoAkira NishiyamaTakahide ToyodaTerunaga InageKazuhisa TanakaYuichi SakairiHidemi SuzukiTakashi UnoIchiro YoshinoNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yuki Onozato Takahiro Nakajima Hajime Yokota Jyunichi Morimoto Akira Nishiyama Takahide Toyoda Terunaga Inage Kazuhisa Tanaka Yuichi Sakairi Hidemi Suzuki Takashi Uno Ichiro Yoshino Radiomics is feasible for prediction of spread through air spaces in patients with nonsmall cell lung cancer
description	Abstract Tumor spread through air spaces (STAS) in non-small-cell lung cancer (NSCLC) is known to influence a poor patient outcome, even in patients presenting with early-stage disease. However, the pre-operative diagnosis of STAS remains challenging. With the progress of radiomics-based analyses several attempts have been made to predict STAS based on radiological findings. In the present study, patients with NSCLC which is located peripherally and tumors ≤ 2 cm in size on computed tomography (CT) that were potential candidates for sublobar resection were enrolled in this study. The radiologic features of the targeted tumors on thin-section CT were extracted using the PyRadiomics v3.0 software package, and a predictive model for STAS was built using the t-test and XGBoost. Thirty-five out of 226 patients had a STAS histology. The predictive model of STAS indicated an area under the receiver-operator characteristic curve (AUC) of 0.77. There was no significant difference in the overall survival (OS) for lobectomy between the predicted-STAS (+) and (−) groups (p = 0.19), but an unfavorable OS for sublobar resection was indicated in the predicted-STAS (+) group (p < 0.01). These results suggest that radiomics with machine-learning helped to develop a favorable model of STAS (+) NSCLC, which might be useful for the proper selection of candidates who should undergo sublobar resection.
format	article
author	Yuki Onozato Takahiro Nakajima Hajime Yokota Jyunichi Morimoto Akira Nishiyama Takahide Toyoda Terunaga Inage Kazuhisa Tanaka Yuichi Sakairi Hidemi Suzuki Takashi Uno Ichiro Yoshino
author_facet	Yuki Onozato Takahiro Nakajima Hajime Yokota Jyunichi Morimoto Akira Nishiyama Takahide Toyoda Terunaga Inage Kazuhisa Tanaka Yuichi Sakairi Hidemi Suzuki Takashi Uno Ichiro Yoshino
author_sort	Yuki Onozato
title	Radiomics is feasible for prediction of spread through air spaces in patients with nonsmall cell lung cancer
title_short	Radiomics is feasible for prediction of spread through air spaces in patients with nonsmall cell lung cancer
title_full	Radiomics is feasible for prediction of spread through air spaces in patients with nonsmall cell lung cancer
title_fullStr	Radiomics is feasible for prediction of spread through air spaces in patients with nonsmall cell lung cancer
title_full_unstemmed	Radiomics is feasible for prediction of spread through air spaces in patients with nonsmall cell lung cancer
title_sort	radiomics is feasible for prediction of spread through air spaces in patients with nonsmall cell lung cancer
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/a3a4c781ec0f4f2e88b0e892e423e60d
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Radiomics is feasible for prediction of spread through air spaces in patients with nonsmall cell lung cancer

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