Cargando…
Peritumoral radiomics features on preoperative thin-slice CT images can predict the spread through air spaces of lung adenocarcinoma
The spread through air spaces (STAS) is recognized as a negative prognostic factor in patients with early-stage lung adenocarcinoma. The present study aimed to develop a machine learning model for the prediction of STAS using peritumoral radiomics features extracted from preoperative CT imaging. A t...
Autores principales: | , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9209514/ https://www.ncbi.nlm.nih.gov/pubmed/35725754 http://dx.doi.org/10.1038/s41598-022-14400-w |
_version_ | 1784729972952268800 |
---|---|
author | Takehana, Keiichi Sakamoto, Ryo Fujimoto, Koji Matsuo, Yukinori Nakajima, Naoki Yoshizawa, Akihiko Menju, Toshi Nakamura, Mitsuhiro Yamada, Ryo Mizowaki, Takashi Nakamoto, Yuji |
author_facet | Takehana, Keiichi Sakamoto, Ryo Fujimoto, Koji Matsuo, Yukinori Nakajima, Naoki Yoshizawa, Akihiko Menju, Toshi Nakamura, Mitsuhiro Yamada, Ryo Mizowaki, Takashi Nakamoto, Yuji |
author_sort | Takehana, Keiichi |
collection | PubMed |
description | The spread through air spaces (STAS) is recognized as a negative prognostic factor in patients with early-stage lung adenocarcinoma. The present study aimed to develop a machine learning model for the prediction of STAS using peritumoral radiomics features extracted from preoperative CT imaging. A total of 339 patients who underwent lobectomy or limited resection for lung adenocarcinoma were included. The patients were randomly divided (3:2) into training and test cohorts. Two prediction models were created using the training cohort: a conventional model based on the tumor consolidation/tumor (C/T) ratio and a machine learning model based on peritumoral radiomics features. The areas under the curve for the two models in the testing cohort were 0.70 and 0.76, respectively (P = 0.045). The cumulative incidence of recurrence (CIR) was significantly higher in the STAS high-risk group when using the radiomics model than that in the low-risk group (44% vs. 4% at 5 years; P = 0.002) in patients who underwent limited resection in the testing cohort. In contrast, the 5-year CIR was not significantly different among patients who underwent lobectomy (17% vs. 11%; P = 0.469). In conclusion, the machine learning model for STAS prediction based on peritumoral radiomics features performed better than the C/T ratio model. |
format | Online Article Text |
id | pubmed-9209514 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-92095142022-06-22 Peritumoral radiomics features on preoperative thin-slice CT images can predict the spread through air spaces of lung adenocarcinoma Takehana, Keiichi Sakamoto, Ryo Fujimoto, Koji Matsuo, Yukinori Nakajima, Naoki Yoshizawa, Akihiko Menju, Toshi Nakamura, Mitsuhiro Yamada, Ryo Mizowaki, Takashi Nakamoto, Yuji Sci Rep Article The spread through air spaces (STAS) is recognized as a negative prognostic factor in patients with early-stage lung adenocarcinoma. The present study aimed to develop a machine learning model for the prediction of STAS using peritumoral radiomics features extracted from preoperative CT imaging. A total of 339 patients who underwent lobectomy or limited resection for lung adenocarcinoma were included. The patients were randomly divided (3:2) into training and test cohorts. Two prediction models were created using the training cohort: a conventional model based on the tumor consolidation/tumor (C/T) ratio and a machine learning model based on peritumoral radiomics features. The areas under the curve for the two models in the testing cohort were 0.70 and 0.76, respectively (P = 0.045). The cumulative incidence of recurrence (CIR) was significantly higher in the STAS high-risk group when using the radiomics model than that in the low-risk group (44% vs. 4% at 5 years; P = 0.002) in patients who underwent limited resection in the testing cohort. In contrast, the 5-year CIR was not significantly different among patients who underwent lobectomy (17% vs. 11%; P = 0.469). In conclusion, the machine learning model for STAS prediction based on peritumoral radiomics features performed better than the C/T ratio model. Nature Publishing Group UK 2022-06-20 /pmc/articles/PMC9209514/ /pubmed/35725754 http://dx.doi.org/10.1038/s41598-022-14400-w Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Takehana, Keiichi Sakamoto, Ryo Fujimoto, Koji Matsuo, Yukinori Nakajima, Naoki Yoshizawa, Akihiko Menju, Toshi Nakamura, Mitsuhiro Yamada, Ryo Mizowaki, Takashi Nakamoto, Yuji Peritumoral radiomics features on preoperative thin-slice CT images can predict the spread through air spaces of lung adenocarcinoma |
title | Peritumoral radiomics features on preoperative thin-slice CT images can predict the spread through air spaces of lung adenocarcinoma |
title_full | Peritumoral radiomics features on preoperative thin-slice CT images can predict the spread through air spaces of lung adenocarcinoma |
title_fullStr | Peritumoral radiomics features on preoperative thin-slice CT images can predict the spread through air spaces of lung adenocarcinoma |
title_full_unstemmed | Peritumoral radiomics features on preoperative thin-slice CT images can predict the spread through air spaces of lung adenocarcinoma |
title_short | Peritumoral radiomics features on preoperative thin-slice CT images can predict the spread through air spaces of lung adenocarcinoma |
title_sort | peritumoral radiomics features on preoperative thin-slice ct images can predict the spread through air spaces of lung adenocarcinoma |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9209514/ https://www.ncbi.nlm.nih.gov/pubmed/35725754 http://dx.doi.org/10.1038/s41598-022-14400-w |
work_keys_str_mv | AT takehanakeiichi peritumoralradiomicsfeaturesonpreoperativethinslicectimagescanpredictthespreadthroughairspacesoflungadenocarcinoma AT sakamotoryo peritumoralradiomicsfeaturesonpreoperativethinslicectimagescanpredictthespreadthroughairspacesoflungadenocarcinoma AT fujimotokoji peritumoralradiomicsfeaturesonpreoperativethinslicectimagescanpredictthespreadthroughairspacesoflungadenocarcinoma AT matsuoyukinori peritumoralradiomicsfeaturesonpreoperativethinslicectimagescanpredictthespreadthroughairspacesoflungadenocarcinoma AT nakajimanaoki peritumoralradiomicsfeaturesonpreoperativethinslicectimagescanpredictthespreadthroughairspacesoflungadenocarcinoma AT yoshizawaakihiko peritumoralradiomicsfeaturesonpreoperativethinslicectimagescanpredictthespreadthroughairspacesoflungadenocarcinoma AT menjutoshi peritumoralradiomicsfeaturesonpreoperativethinslicectimagescanpredictthespreadthroughairspacesoflungadenocarcinoma AT nakamuramitsuhiro peritumoralradiomicsfeaturesonpreoperativethinslicectimagescanpredictthespreadthroughairspacesoflungadenocarcinoma AT yamadaryo peritumoralradiomicsfeaturesonpreoperativethinslicectimagescanpredictthespreadthroughairspacesoflungadenocarcinoma AT mizowakitakashi peritumoralradiomicsfeaturesonpreoperativethinslicectimagescanpredictthespreadthroughairspacesoflungadenocarcinoma AT nakamotoyuji peritumoralradiomicsfeaturesonpreoperativethinslicectimagescanpredictthespreadthroughairspacesoflungadenocarcinoma |