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Intratumoral and peritumoral radiomics analysis for preoperative Lauren classification in gastric cancer

BACKGROUND: Preoperative prediction of the Lauren classification in gastric cancer (GC) is very important to the choice of therapy, the evaluation of prognosis, and the improvement of quality of life. However, there is not yet radiomics analysis concerning the prediction of Lauren classification str...

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Autores principales: Wang, Xiao-Xiao, Ding, Yi, Wang, Si-Wen, Dong, Di, Li, Hai-Lin, Chen, Jian, Hu, Hui, Lu, Chao, Tian, Jie, Shan, Xiu-Hong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7684959/
https://www.ncbi.nlm.nih.gov/pubmed/33228815
http://dx.doi.org/10.1186/s40644-020-00358-3
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author Wang, Xiao-Xiao
Ding, Yi
Wang, Si-Wen
Dong, Di
Li, Hai-Lin
Chen, Jian
Hu, Hui
Lu, Chao
Tian, Jie
Shan, Xiu-Hong
author_facet Wang, Xiao-Xiao
Ding, Yi
Wang, Si-Wen
Dong, Di
Li, Hai-Lin
Chen, Jian
Hu, Hui
Lu, Chao
Tian, Jie
Shan, Xiu-Hong
author_sort Wang, Xiao-Xiao
collection PubMed
description BACKGROUND: Preoperative prediction of the Lauren classification in gastric cancer (GC) is very important to the choice of therapy, the evaluation of prognosis, and the improvement of quality of life. However, there is not yet radiomics analysis concerning the prediction of Lauren classification straightly. In this study, a radiomic nomogram was developed to preoperatively differentiate Lauren diffuse type from intestinal type in GC. METHODS: A total of 539 GC patients were enrolled in this study and later randomly allocated to two cohorts at a 7:3 ratio for training and validation. Two sets of radiomic features were derived from tumor regions and peritumor regions on venous phase computed tomography (CT) images, respectively. With the least absolute shrinkage and selection operator logistic regression, a combined radiomic signature was constructed. Also, a tumor-based model and a peripheral ring-based model were built for comparison. Afterwards, a radiomic nomogram integrating the combined radiomic signature and clinical characteristics was developed. All the models were evaluated regarding classification ability and clinical usefulness. RESULTS: The combined radiomic signature achieved an area under receiver operating characteristic curve (AUC) of 0.715 (95% confidence interval [CI], 0.663–0.767) in the training cohort and 0.714 (95% CI, 0.636–0.792) in the validation cohort. The radiomic nomogram incorporating the combined radiomic signature, age, CT T stage, and CT N stage outperformed the other models with a training AUC of 0.745 (95% CI, 0.696–0.795) and a validation AUC of 0.758 (95% CI, 0.685–0.831). The significantly improved sensitivity of radiomic nomogram (0.765 and 0.793) indicated better identification of diffuse type GC patients. Further, calibration curves and decision curves demonstrated its great model fitness and clinical usefulness. CONCLUSIONS: The radiomic nomogram involving the combined radiomic signature and clinical characteristics holds potential in differentiating Lauren diffuse type from intestinal type for reasonable clinical treatment strategy. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40644-020-00358-3.
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spelling pubmed-76849592020-11-25 Intratumoral and peritumoral radiomics analysis for preoperative Lauren classification in gastric cancer Wang, Xiao-Xiao Ding, Yi Wang, Si-Wen Dong, Di Li, Hai-Lin Chen, Jian Hu, Hui Lu, Chao Tian, Jie Shan, Xiu-Hong Cancer Imaging Research Article BACKGROUND: Preoperative prediction of the Lauren classification in gastric cancer (GC) is very important to the choice of therapy, the evaluation of prognosis, and the improvement of quality of life. However, there is not yet radiomics analysis concerning the prediction of Lauren classification straightly. In this study, a radiomic nomogram was developed to preoperatively differentiate Lauren diffuse type from intestinal type in GC. METHODS: A total of 539 GC patients were enrolled in this study and later randomly allocated to two cohorts at a 7:3 ratio for training and validation. Two sets of radiomic features were derived from tumor regions and peritumor regions on venous phase computed tomography (CT) images, respectively. With the least absolute shrinkage and selection operator logistic regression, a combined radiomic signature was constructed. Also, a tumor-based model and a peripheral ring-based model were built for comparison. Afterwards, a radiomic nomogram integrating the combined radiomic signature and clinical characteristics was developed. All the models were evaluated regarding classification ability and clinical usefulness. RESULTS: The combined radiomic signature achieved an area under receiver operating characteristic curve (AUC) of 0.715 (95% confidence interval [CI], 0.663–0.767) in the training cohort and 0.714 (95% CI, 0.636–0.792) in the validation cohort. The radiomic nomogram incorporating the combined radiomic signature, age, CT T stage, and CT N stage outperformed the other models with a training AUC of 0.745 (95% CI, 0.696–0.795) and a validation AUC of 0.758 (95% CI, 0.685–0.831). The significantly improved sensitivity of radiomic nomogram (0.765 and 0.793) indicated better identification of diffuse type GC patients. Further, calibration curves and decision curves demonstrated its great model fitness and clinical usefulness. CONCLUSIONS: The radiomic nomogram involving the combined radiomic signature and clinical characteristics holds potential in differentiating Lauren diffuse type from intestinal type for reasonable clinical treatment strategy. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40644-020-00358-3. BioMed Central 2020-11-23 /pmc/articles/PMC7684959/ /pubmed/33228815 http://dx.doi.org/10.1186/s40644-020-00358-3 Text en © The Author(s) 2020 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/. The Creative Commons Public Domain Dedication waiver (http://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 Article
Wang, Xiao-Xiao
Ding, Yi
Wang, Si-Wen
Dong, Di
Li, Hai-Lin
Chen, Jian
Hu, Hui
Lu, Chao
Tian, Jie
Shan, Xiu-Hong
Intratumoral and peritumoral radiomics analysis for preoperative Lauren classification in gastric cancer
title Intratumoral and peritumoral radiomics analysis for preoperative Lauren classification in gastric cancer
title_full Intratumoral and peritumoral radiomics analysis for preoperative Lauren classification in gastric cancer
title_fullStr Intratumoral and peritumoral radiomics analysis for preoperative Lauren classification in gastric cancer
title_full_unstemmed Intratumoral and peritumoral radiomics analysis for preoperative Lauren classification in gastric cancer
title_short Intratumoral and peritumoral radiomics analysis for preoperative Lauren classification in gastric cancer
title_sort intratumoral and peritumoral radiomics analysis for preoperative lauren classification in gastric cancer
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7684959/
https://www.ncbi.nlm.nih.gov/pubmed/33228815
http://dx.doi.org/10.1186/s40644-020-00358-3
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