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Prediction of the therapeutic efficacy of epirubicin combined with ifosfamide in patients with lung metastases from soft tissue sarcoma based on contrast-enhanced CT radiomics features

OBJECTIVE: To investigate the value of contrast-enhanced computed tomography (CECT) radiomics features in predicting the efficacy of epirubicin combined with ifosfamide in patients with pulmonary metastases from soft tissue sarcoma. METHODS: A retrospective analysis of 51 patients with pulmonary met...

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Autores principales: Miao, Lei, Ma, Shu-Tao, Jiang, Xu, Zhang, Huan-Huan, Wang, Yan-Mei, Li, Meng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9316811/
https://www.ncbi.nlm.nih.gov/pubmed/35883116
http://dx.doi.org/10.1186/s12880-022-00859-6
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author Miao, Lei
Ma, Shu-Tao
Jiang, Xu
Zhang, Huan-Huan
Wang, Yan-Mei
Li, Meng
author_facet Miao, Lei
Ma, Shu-Tao
Jiang, Xu
Zhang, Huan-Huan
Wang, Yan-Mei
Li, Meng
author_sort Miao, Lei
collection PubMed
description OBJECTIVE: To investigate the value of contrast-enhanced computed tomography (CECT) radiomics features in predicting the efficacy of epirubicin combined with ifosfamide in patients with pulmonary metastases from soft tissue sarcoma. METHODS: A retrospective analysis of 51 patients with pulmonary metastases from soft tissue sarcoma who received the chemotherapy regimen of epirubicin combined with ifosfamide was performed, and efficacy was evaluated by Recist1.1. ROIs (1 or 2) were selected for each patient. Lung metastases were used as target lesions (86 target lesions total), and the patients were divided into a progression group (n = 29) and a non-progressive group (n = 57); the latter included a stable group (n = 34) and a partial response group (n = 23). Information on lung metastases was extracted from CECT images before chemotherapy, and all lesions were delineated by ITK-SNAP software manually or semiautomatically. The decision tree classifier had a better performance in all radiomics models. A receiver operating characteristic curve was plotted to evaluate the predictive performance of the radiomics model. RESULTS: In total, 851 CECT radiomics features were extracted for each target lesion and finally reduced to 2 radiomics features, which were then used to construct a radiomics model. Areas under the curves of the model for predicting lesion progression were 0.917 and 0.856 in training and testing groups, respectively. CONCLUSION: The model established based on the radiomics features of CECT before treatment has certain predictive value for assessing the efficacy of chemotherapy for patients with soft tissue sarcoma lung metastases. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12880-022-00859-6.
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spelling pubmed-93168112022-07-27 Prediction of the therapeutic efficacy of epirubicin combined with ifosfamide in patients with lung metastases from soft tissue sarcoma based on contrast-enhanced CT radiomics features Miao, Lei Ma, Shu-Tao Jiang, Xu Zhang, Huan-Huan Wang, Yan-Mei Li, Meng BMC Med Imaging Research OBJECTIVE: To investigate the value of contrast-enhanced computed tomography (CECT) radiomics features in predicting the efficacy of epirubicin combined with ifosfamide in patients with pulmonary metastases from soft tissue sarcoma. METHODS: A retrospective analysis of 51 patients with pulmonary metastases from soft tissue sarcoma who received the chemotherapy regimen of epirubicin combined with ifosfamide was performed, and efficacy was evaluated by Recist1.1. ROIs (1 or 2) were selected for each patient. Lung metastases were used as target lesions (86 target lesions total), and the patients were divided into a progression group (n = 29) and a non-progressive group (n = 57); the latter included a stable group (n = 34) and a partial response group (n = 23). Information on lung metastases was extracted from CECT images before chemotherapy, and all lesions were delineated by ITK-SNAP software manually or semiautomatically. The decision tree classifier had a better performance in all radiomics models. A receiver operating characteristic curve was plotted to evaluate the predictive performance of the radiomics model. RESULTS: In total, 851 CECT radiomics features were extracted for each target lesion and finally reduced to 2 radiomics features, which were then used to construct a radiomics model. Areas under the curves of the model for predicting lesion progression were 0.917 and 0.856 in training and testing groups, respectively. CONCLUSION: The model established based on the radiomics features of CECT before treatment has certain predictive value for assessing the efficacy of chemotherapy for patients with soft tissue sarcoma lung metastases. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12880-022-00859-6. BioMed Central 2022-07-26 /pmc/articles/PMC9316811/ /pubmed/35883116 http://dx.doi.org/10.1186/s12880-022-00859-6 Text en © The Author(s) 2022 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/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Miao, Lei
Ma, Shu-Tao
Jiang, Xu
Zhang, Huan-Huan
Wang, Yan-Mei
Li, Meng
Prediction of the therapeutic efficacy of epirubicin combined with ifosfamide in patients with lung metastases from soft tissue sarcoma based on contrast-enhanced CT radiomics features
title Prediction of the therapeutic efficacy of epirubicin combined with ifosfamide in patients with lung metastases from soft tissue sarcoma based on contrast-enhanced CT radiomics features
title_full Prediction of the therapeutic efficacy of epirubicin combined with ifosfamide in patients with lung metastases from soft tissue sarcoma based on contrast-enhanced CT radiomics features
title_fullStr Prediction of the therapeutic efficacy of epirubicin combined with ifosfamide in patients with lung metastases from soft tissue sarcoma based on contrast-enhanced CT radiomics features
title_full_unstemmed Prediction of the therapeutic efficacy of epirubicin combined with ifosfamide in patients with lung metastases from soft tissue sarcoma based on contrast-enhanced CT radiomics features
title_short Prediction of the therapeutic efficacy of epirubicin combined with ifosfamide in patients with lung metastases from soft tissue sarcoma based on contrast-enhanced CT radiomics features
title_sort prediction of the therapeutic efficacy of epirubicin combined with ifosfamide in patients with lung metastases from soft tissue sarcoma based on contrast-enhanced ct radiomics features
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9316811/
https://www.ncbi.nlm.nih.gov/pubmed/35883116
http://dx.doi.org/10.1186/s12880-022-00859-6
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