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CT-based radiomics nomograms for preoperative prediction of diffuse-type and signet ring cell gastric cancer: a multicenter development and validation cohort

BACKGROUND: The prevalence of diffuse-type gastric cancer (GC), especially signet ring cell carcinoma (SRCC), has shown an upward trend in the past decades. This study aimed to develop computed tomography (CT) based radiomics nomograms to distinguish diffuse-type and SRCC GC preoperatively. METHODS:...

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Autores principales: Chen, Tao, Wu, Jing, Cui, Chunhui, He, Qinglie, Li, Xunjun, Liang, Weiqi, Liu, Xiaoyue, Liu, Tianbao, Zhou, Xuanhui, Zhang, Xifan, Lei, Xiaotian, Xiong, Wei, Yu, Jiang, Li, Guoxin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8785479/
https://www.ncbi.nlm.nih.gov/pubmed/35073917
http://dx.doi.org/10.1186/s12967-022-03232-x
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author Chen, Tao
Wu, Jing
Cui, Chunhui
He, Qinglie
Li, Xunjun
Liang, Weiqi
Liu, Xiaoyue
Liu, Tianbao
Zhou, Xuanhui
Zhang, Xifan
Lei, Xiaotian
Xiong, Wei
Yu, Jiang
Li, Guoxin
author_facet Chen, Tao
Wu, Jing
Cui, Chunhui
He, Qinglie
Li, Xunjun
Liang, Weiqi
Liu, Xiaoyue
Liu, Tianbao
Zhou, Xuanhui
Zhang, Xifan
Lei, Xiaotian
Xiong, Wei
Yu, Jiang
Li, Guoxin
author_sort Chen, Tao
collection PubMed
description BACKGROUND: The prevalence of diffuse-type gastric cancer (GC), especially signet ring cell carcinoma (SRCC), has shown an upward trend in the past decades. This study aimed to develop computed tomography (CT) based radiomics nomograms to distinguish diffuse-type and SRCC GC preoperatively. METHODS: A total of 693 GC patients from two centers were retrospectively analyzed and divided into training, internal validation and external validation cohorts. Radiomics features were extracted from CT images, and the Lauren radiomics model was established with a support vector machine (SVM) classifier to identify diffuse-type GC. The Lauren radiomics nomogram integrating radiomics features score (Rad-score) and clinicopathological characteristics were developed and evaluated regarding prediction ability. Further, the SRCC radiomics nomogram designed to identify SRCC from diffuse-type GC was developed and evaluated following the same procedures. RESULTS: Multivariate analysis revealed that Rad-scores was significantly associated with diffuse-type GC and SRCC (p < 0.001). The Lauren radiomics nomogram showed promising prediction performance with an area under the curve (AUC) of 0.895 (95%CI, 0.957–0.932), 0.841 (95%CI, 0.781–0.901) and 0.893 (95%CI, 0.831–0.955) in each cohort. The SRCC radiomics nomogram also showed good discrimination, with AUC of 0.905 (95%CI,0.866–0.944), 0.845 (95%CI, 0.775–0.915) and 0.918 (95%CI, 0.842–0.994) in each cohort. The radiomics nomograms showed great model fitness and clinical usefulness by calibration curve and decision curve analysis. CONCLUSION: Our CT-based radiomics nomograms had the ability to identify the diffuse-type and SRCC GC, providing a non-invasive, efficient and preoperative diagnosis method. They may help guide preoperative clinical decision-making and benefit GC patients in the future. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12967-022-03232-x.
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spelling pubmed-87854792022-01-24 CT-based radiomics nomograms for preoperative prediction of diffuse-type and signet ring cell gastric cancer: a multicenter development and validation cohort Chen, Tao Wu, Jing Cui, Chunhui He, Qinglie Li, Xunjun Liang, Weiqi Liu, Xiaoyue Liu, Tianbao Zhou, Xuanhui Zhang, Xifan Lei, Xiaotian Xiong, Wei Yu, Jiang Li, Guoxin J Transl Med Research BACKGROUND: The prevalence of diffuse-type gastric cancer (GC), especially signet ring cell carcinoma (SRCC), has shown an upward trend in the past decades. This study aimed to develop computed tomography (CT) based radiomics nomograms to distinguish diffuse-type and SRCC GC preoperatively. METHODS: A total of 693 GC patients from two centers were retrospectively analyzed and divided into training, internal validation and external validation cohorts. Radiomics features were extracted from CT images, and the Lauren radiomics model was established with a support vector machine (SVM) classifier to identify diffuse-type GC. The Lauren radiomics nomogram integrating radiomics features score (Rad-score) and clinicopathological characteristics were developed and evaluated regarding prediction ability. Further, the SRCC radiomics nomogram designed to identify SRCC from diffuse-type GC was developed and evaluated following the same procedures. RESULTS: Multivariate analysis revealed that Rad-scores was significantly associated with diffuse-type GC and SRCC (p < 0.001). The Lauren radiomics nomogram showed promising prediction performance with an area under the curve (AUC) of 0.895 (95%CI, 0.957–0.932), 0.841 (95%CI, 0.781–0.901) and 0.893 (95%CI, 0.831–0.955) in each cohort. The SRCC radiomics nomogram also showed good discrimination, with AUC of 0.905 (95%CI,0.866–0.944), 0.845 (95%CI, 0.775–0.915) and 0.918 (95%CI, 0.842–0.994) in each cohort. The radiomics nomograms showed great model fitness and clinical usefulness by calibration curve and decision curve analysis. CONCLUSION: Our CT-based radiomics nomograms had the ability to identify the diffuse-type and SRCC GC, providing a non-invasive, efficient and preoperative diagnosis method. They may help guide preoperative clinical decision-making and benefit GC patients in the future. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12967-022-03232-x. BioMed Central 2022-01-24 /pmc/articles/PMC8785479/ /pubmed/35073917 http://dx.doi.org/10.1186/s12967-022-03232-x 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
Chen, Tao
Wu, Jing
Cui, Chunhui
He, Qinglie
Li, Xunjun
Liang, Weiqi
Liu, Xiaoyue
Liu, Tianbao
Zhou, Xuanhui
Zhang, Xifan
Lei, Xiaotian
Xiong, Wei
Yu, Jiang
Li, Guoxin
CT-based radiomics nomograms for preoperative prediction of diffuse-type and signet ring cell gastric cancer: a multicenter development and validation cohort
title CT-based radiomics nomograms for preoperative prediction of diffuse-type and signet ring cell gastric cancer: a multicenter development and validation cohort
title_full CT-based radiomics nomograms for preoperative prediction of diffuse-type and signet ring cell gastric cancer: a multicenter development and validation cohort
title_fullStr CT-based radiomics nomograms for preoperative prediction of diffuse-type and signet ring cell gastric cancer: a multicenter development and validation cohort
title_full_unstemmed CT-based radiomics nomograms for preoperative prediction of diffuse-type and signet ring cell gastric cancer: a multicenter development and validation cohort
title_short CT-based radiomics nomograms for preoperative prediction of diffuse-type and signet ring cell gastric cancer: a multicenter development and validation cohort
title_sort ct-based radiomics nomograms for preoperative prediction of diffuse-type and signet ring cell gastric cancer: a multicenter development and validation cohort
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8785479/
https://www.ncbi.nlm.nih.gov/pubmed/35073917
http://dx.doi.org/10.1186/s12967-022-03232-x
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