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Development and validation of a preoperative CT-based radiomic nomogram to predict pathology invasiveness in patients with a solitary pulmonary nodule: a machine learning approach, multicenter, diagnostic study

OBJECTIVES: To develop and validate a preoperative CT-based nomogram combined with radiomic and clinical–radiological signatures to distinguish preinvasive lesions from pulmonary invasive lesions. METHODS: This was a retrospective, diagnostic study conducted from August 1, 2018, to May 1, 2020, at t...

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Autores principales: Huang, Luyu, Lin, Weihuan, Xie, Daipeng, Yu, Yunfang, Cao, Hanbo, Liao, Guoqing, Wu, Shaowei, Yao, Lintong, Wang, Zhaoyu, Wang, Mei, Wang, Siyun, Wang, Guangyi, Zhang, Dongkun, Yao, Su, He, Zifan, Cho, William Chi-Shing, Chen, Duo, Zhang, Zhengjie, Li, Wanshan, Qiao, Guibin, Chan, Lawrence Wing-Chi, Zhou, Haiyu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8831242/
https://www.ncbi.nlm.nih.gov/pubmed/34654966
http://dx.doi.org/10.1007/s00330-021-08268-z
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author Huang, Luyu
Lin, Weihuan
Xie, Daipeng
Yu, Yunfang
Cao, Hanbo
Liao, Guoqing
Wu, Shaowei
Yao, Lintong
Wang, Zhaoyu
Wang, Mei
Wang, Siyun
Wang, Guangyi
Zhang, Dongkun
Yao, Su
He, Zifan
Cho, William Chi-Shing
Chen, Duo
Zhang, Zhengjie
Li, Wanshan
Qiao, Guibin
Chan, Lawrence Wing-Chi
Zhou, Haiyu
author_facet Huang, Luyu
Lin, Weihuan
Xie, Daipeng
Yu, Yunfang
Cao, Hanbo
Liao, Guoqing
Wu, Shaowei
Yao, Lintong
Wang, Zhaoyu
Wang, Mei
Wang, Siyun
Wang, Guangyi
Zhang, Dongkun
Yao, Su
He, Zifan
Cho, William Chi-Shing
Chen, Duo
Zhang, Zhengjie
Li, Wanshan
Qiao, Guibin
Chan, Lawrence Wing-Chi
Zhou, Haiyu
author_sort Huang, Luyu
collection PubMed
description OBJECTIVES: To develop and validate a preoperative CT-based nomogram combined with radiomic and clinical–radiological signatures to distinguish preinvasive lesions from pulmonary invasive lesions. METHODS: This was a retrospective, diagnostic study conducted from August 1, 2018, to May 1, 2020, at three centers. Patients with a solitary pulmonary nodule were enrolled in the GDPH center and were divided into two groups (7:3) randomly: development (n = 149) and internal validation (n = 54). The SYSMH center and the ZSLC Center formed an external validation cohort of 170 patients. The least absolute shrinkage and selection operator (LASSO) algorithm and logistic regression analysis were used to feature signatures and transform them into models. RESULTS: The study comprised 373 individuals from three independent centers (female: 225/373, 60.3%; median [IQR] age, 57.0 [48.0–65.0] years). The AUCs for the combined radiomic signature selected from the nodular area and the perinodular area were 0.93, 0.91, and 0.90 in the three cohorts. The nomogram combining the clinical and combined radiomic signatures could accurately predict interstitial invasion in patients with a solitary pulmonary nodule (AUC, 0.94, 0.90, 0.92) in the three cohorts, respectively. The radiomic nomogram outperformed any clinical or radiomic signature in terms of clinical predictive abilities, according to a decision curve analysis and the Akaike information criteria. CONCLUSIONS: This study demonstrated that a nomogram constructed by identified clinical–radiological signatures and combined radiomic signatures has the potential to precisely predict pathology invasiveness. KEY POINTS: • The radiomic signature from the perinodular area has the potential to predict pathology invasiveness of the solitary pulmonary nodule. • The new radiomic nomogram was useful in clinical decision-making associated with personalized surgical intervention and therapeutic regimen selection in patients with early-stage non-small-cell lung cancer. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00330-021-08268-z.
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spelling pubmed-88312422022-02-23 Development and validation of a preoperative CT-based radiomic nomogram to predict pathology invasiveness in patients with a solitary pulmonary nodule: a machine learning approach, multicenter, diagnostic study Huang, Luyu Lin, Weihuan Xie, Daipeng Yu, Yunfang Cao, Hanbo Liao, Guoqing Wu, Shaowei Yao, Lintong Wang, Zhaoyu Wang, Mei Wang, Siyun Wang, Guangyi Zhang, Dongkun Yao, Su He, Zifan Cho, William Chi-Shing Chen, Duo Zhang, Zhengjie Li, Wanshan Qiao, Guibin Chan, Lawrence Wing-Chi Zhou, Haiyu Eur Radiol Computed Tomography OBJECTIVES: To develop and validate a preoperative CT-based nomogram combined with radiomic and clinical–radiological signatures to distinguish preinvasive lesions from pulmonary invasive lesions. METHODS: This was a retrospective, diagnostic study conducted from August 1, 2018, to May 1, 2020, at three centers. Patients with a solitary pulmonary nodule were enrolled in the GDPH center and were divided into two groups (7:3) randomly: development (n = 149) and internal validation (n = 54). The SYSMH center and the ZSLC Center formed an external validation cohort of 170 patients. The least absolute shrinkage and selection operator (LASSO) algorithm and logistic regression analysis were used to feature signatures and transform them into models. RESULTS: The study comprised 373 individuals from three independent centers (female: 225/373, 60.3%; median [IQR] age, 57.0 [48.0–65.0] years). The AUCs for the combined radiomic signature selected from the nodular area and the perinodular area were 0.93, 0.91, and 0.90 in the three cohorts. The nomogram combining the clinical and combined radiomic signatures could accurately predict interstitial invasion in patients with a solitary pulmonary nodule (AUC, 0.94, 0.90, 0.92) in the three cohorts, respectively. The radiomic nomogram outperformed any clinical or radiomic signature in terms of clinical predictive abilities, according to a decision curve analysis and the Akaike information criteria. CONCLUSIONS: This study demonstrated that a nomogram constructed by identified clinical–radiological signatures and combined radiomic signatures has the potential to precisely predict pathology invasiveness. KEY POINTS: • The radiomic signature from the perinodular area has the potential to predict pathology invasiveness of the solitary pulmonary nodule. • The new radiomic nomogram was useful in clinical decision-making associated with personalized surgical intervention and therapeutic regimen selection in patients with early-stage non-small-cell lung cancer. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00330-021-08268-z. Springer Berlin Heidelberg 2021-10-16 2022 /pmc/articles/PMC8831242/ /pubmed/34654966 http://dx.doi.org/10.1007/s00330-021-08268-z Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 Computed Tomography
Huang, Luyu
Lin, Weihuan
Xie, Daipeng
Yu, Yunfang
Cao, Hanbo
Liao, Guoqing
Wu, Shaowei
Yao, Lintong
Wang, Zhaoyu
Wang, Mei
Wang, Siyun
Wang, Guangyi
Zhang, Dongkun
Yao, Su
He, Zifan
Cho, William Chi-Shing
Chen, Duo
Zhang, Zhengjie
Li, Wanshan
Qiao, Guibin
Chan, Lawrence Wing-Chi
Zhou, Haiyu
Development and validation of a preoperative CT-based radiomic nomogram to predict pathology invasiveness in patients with a solitary pulmonary nodule: a machine learning approach, multicenter, diagnostic study
title Development and validation of a preoperative CT-based radiomic nomogram to predict pathology invasiveness in patients with a solitary pulmonary nodule: a machine learning approach, multicenter, diagnostic study
title_full Development and validation of a preoperative CT-based radiomic nomogram to predict pathology invasiveness in patients with a solitary pulmonary nodule: a machine learning approach, multicenter, diagnostic study
title_fullStr Development and validation of a preoperative CT-based radiomic nomogram to predict pathology invasiveness in patients with a solitary pulmonary nodule: a machine learning approach, multicenter, diagnostic study
title_full_unstemmed Development and validation of a preoperative CT-based radiomic nomogram to predict pathology invasiveness in patients with a solitary pulmonary nodule: a machine learning approach, multicenter, diagnostic study
title_short Development and validation of a preoperative CT-based radiomic nomogram to predict pathology invasiveness in patients with a solitary pulmonary nodule: a machine learning approach, multicenter, diagnostic study
title_sort development and validation of a preoperative ct-based radiomic nomogram to predict pathology invasiveness in patients with a solitary pulmonary nodule: a machine learning approach, multicenter, diagnostic study
topic Computed Tomography
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8831242/
https://www.ncbi.nlm.nih.gov/pubmed/34654966
http://dx.doi.org/10.1007/s00330-021-08268-z
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