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Value of radiomics in differentiating synchronous double primary lung adenocarcinomas from intrapulmonary metastasis

BACKGROUND: Distinguishing synchronous double primary lung adenocarcinoma (SDPLA) from intrapulmonary metastasis (IPM) of lung cancer has significant therapeutic and prognostic values. This study aimed to develop and validate a CT-based radiomics model to differentiate SDPLA from IPM. METHODS: A tot...

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Autores principales: Han, Xiaoyu, Fan, Jun, Zheng, Yuting, Wu, Ying, Alwalid, Osamah, Ding, Chengyu, Jia, Xi, Li, Hanting, Zhang, Xiaohui, Zhang, Kailu, Li, Yumin, Liu, Jia, Guo, Tingting, Ren, Hongwei, Shi, Heshui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AME Publishing Company 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10407476/
https://www.ncbi.nlm.nih.gov/pubmed/37559630
http://dx.doi.org/10.21037/jtd-23-133
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author Han, Xiaoyu
Fan, Jun
Zheng, Yuting
Wu, Ying
Alwalid, Osamah
Ding, Chengyu
Jia, Xi
Li, Hanting
Zhang, Xiaohui
Zhang, Kailu
Li, Yumin
Liu, Jia
Guo, Tingting
Ren, Hongwei
Shi, Heshui
author_facet Han, Xiaoyu
Fan, Jun
Zheng, Yuting
Wu, Ying
Alwalid, Osamah
Ding, Chengyu
Jia, Xi
Li, Hanting
Zhang, Xiaohui
Zhang, Kailu
Li, Yumin
Liu, Jia
Guo, Tingting
Ren, Hongwei
Shi, Heshui
author_sort Han, Xiaoyu
collection PubMed
description BACKGROUND: Distinguishing synchronous double primary lung adenocarcinoma (SDPLA) from intrapulmonary metastasis (IPM) of lung cancer has significant therapeutic and prognostic values. This study aimed to develop and validate a CT-based radiomics model to differentiate SDPLA from IPM. METHODS: A total of 153 patients (93 SDPLA and 60 IPM) with 306 pathologically confirmed lesions were retrospectively studied. CT morphological features were also recorded. Region of interest (ROI) segmentation was performed semiautomatically, and 1,037 radiomics features were extracted from every segmented lesion The differences of radiomics features were defined as the relative net difference in radiomics features between the two lesions on CT. Those low reliable (ICC <0.75) and redundant (r>0.9) features were excluded by intraclass correlation coefficients (ICC) and Pearson’s correlation. Multivariate logistic regression (LR) algorithm was used to establish the classification model according to the selected features. The radiomics model was based on the four most contributing differences of radiomics features. Clinical-CT model and MixModel were based on selected clinical and CT features only and the combination of clinical-CT and Rad-score, respectively. RESULTS: In both the training and testing cohorts, the area under the curves (AUCs) of the radiomics model were larger than those of the clinical-CT model (0.944 vs. 0.793 and 0.886 vs. 0.735 on training and testing cohorts, respectively), and statistically significant differences between the two models in the testing set were found (P<0.001). Meanwhile, three radiologists had sensitivities of 84.2%, 63.9%, and 68.4%, and specificities of 76.9%, 69.2%, and 76.9% in differentiating 19 SDPLA cases from 13 cases of IPM in the testing set. Compared with the performance of the three radiologists, the radiomics model showed better accuracy to the patients in both the training and testing cohorts. Among the three models, the radiomics model showed the best net benefits. CONCLUSIONS: The differences of radiomics features showed excellent diagnostic performance for preoperative differentiation between synchronous double primary lung adenocarcinoma from interpulmonary metastasis, superior to the clinical model and decisions made by radiologists.
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spelling pubmed-104074762023-08-09 Value of radiomics in differentiating synchronous double primary lung adenocarcinomas from intrapulmonary metastasis Han, Xiaoyu Fan, Jun Zheng, Yuting Wu, Ying Alwalid, Osamah Ding, Chengyu Jia, Xi Li, Hanting Zhang, Xiaohui Zhang, Kailu Li, Yumin Liu, Jia Guo, Tingting Ren, Hongwei Shi, Heshui J Thorac Dis Original Article BACKGROUND: Distinguishing synchronous double primary lung adenocarcinoma (SDPLA) from intrapulmonary metastasis (IPM) of lung cancer has significant therapeutic and prognostic values. This study aimed to develop and validate a CT-based radiomics model to differentiate SDPLA from IPM. METHODS: A total of 153 patients (93 SDPLA and 60 IPM) with 306 pathologically confirmed lesions were retrospectively studied. CT morphological features were also recorded. Region of interest (ROI) segmentation was performed semiautomatically, and 1,037 radiomics features were extracted from every segmented lesion The differences of radiomics features were defined as the relative net difference in radiomics features between the two lesions on CT. Those low reliable (ICC <0.75) and redundant (r>0.9) features were excluded by intraclass correlation coefficients (ICC) and Pearson’s correlation. Multivariate logistic regression (LR) algorithm was used to establish the classification model according to the selected features. The radiomics model was based on the four most contributing differences of radiomics features. Clinical-CT model and MixModel were based on selected clinical and CT features only and the combination of clinical-CT and Rad-score, respectively. RESULTS: In both the training and testing cohorts, the area under the curves (AUCs) of the radiomics model were larger than those of the clinical-CT model (0.944 vs. 0.793 and 0.886 vs. 0.735 on training and testing cohorts, respectively), and statistically significant differences between the two models in the testing set were found (P<0.001). Meanwhile, three radiologists had sensitivities of 84.2%, 63.9%, and 68.4%, and specificities of 76.9%, 69.2%, and 76.9% in differentiating 19 SDPLA cases from 13 cases of IPM in the testing set. Compared with the performance of the three radiologists, the radiomics model showed better accuracy to the patients in both the training and testing cohorts. Among the three models, the radiomics model showed the best net benefits. CONCLUSIONS: The differences of radiomics features showed excellent diagnostic performance for preoperative differentiation between synchronous double primary lung adenocarcinoma from interpulmonary metastasis, superior to the clinical model and decisions made by radiologists. AME Publishing Company 2023-07-06 2023-07-31 /pmc/articles/PMC10407476/ /pubmed/37559630 http://dx.doi.org/10.21037/jtd-23-133 Text en 2023 Journal of Thoracic Disease. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) .
spellingShingle Original Article
Han, Xiaoyu
Fan, Jun
Zheng, Yuting
Wu, Ying
Alwalid, Osamah
Ding, Chengyu
Jia, Xi
Li, Hanting
Zhang, Xiaohui
Zhang, Kailu
Li, Yumin
Liu, Jia
Guo, Tingting
Ren, Hongwei
Shi, Heshui
Value of radiomics in differentiating synchronous double primary lung adenocarcinomas from intrapulmonary metastasis
title Value of radiomics in differentiating synchronous double primary lung adenocarcinomas from intrapulmonary metastasis
title_full Value of radiomics in differentiating synchronous double primary lung adenocarcinomas from intrapulmonary metastasis
title_fullStr Value of radiomics in differentiating synchronous double primary lung adenocarcinomas from intrapulmonary metastasis
title_full_unstemmed Value of radiomics in differentiating synchronous double primary lung adenocarcinomas from intrapulmonary metastasis
title_short Value of radiomics in differentiating synchronous double primary lung adenocarcinomas from intrapulmonary metastasis
title_sort value of radiomics in differentiating synchronous double primary lung adenocarcinomas from intrapulmonary metastasis
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10407476/
https://www.ncbi.nlm.nih.gov/pubmed/37559630
http://dx.doi.org/10.21037/jtd-23-133
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