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Virtual biopsy using CT radiomics for evaluation of disagreement in pathology between endoscopic biopsy and postoperative specimens in patients with gastric cancer: a dual-energy CT generalizability study

PURPOSE: To develop a noninvasive radiomics-based nomogram for identification of disagreement in pathology between endoscopic biopsy and postoperative specimens in gastric cancer (GC). MATERIALS AND METHODS: This observational study recruited 181 GC patients who underwent pre-treatment computed tomo...

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Autores principales: Liu, Yiyang, Zhao, Shuai, Wu, Zixin, Liang, Hejun, Chen, Xingzhi, Huang, Chencui, Lu, Hao, Yuan, Mengchen, Xue, Xiaonan, Luo, Chenglong, Liu, Chenchen, Gao, Jianbo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Vienna 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10323070/
https://www.ncbi.nlm.nih.gov/pubmed/37405591
http://dx.doi.org/10.1186/s13244-023-01459-w
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author Liu, Yiyang
Zhao, Shuai
Wu, Zixin
Liang, Hejun
Chen, Xingzhi
Huang, Chencui
Lu, Hao
Yuan, Mengchen
Xue, Xiaonan
Luo, Chenglong
Liu, Chenchen
Gao, Jianbo
author_facet Liu, Yiyang
Zhao, Shuai
Wu, Zixin
Liang, Hejun
Chen, Xingzhi
Huang, Chencui
Lu, Hao
Yuan, Mengchen
Xue, Xiaonan
Luo, Chenglong
Liu, Chenchen
Gao, Jianbo
author_sort Liu, Yiyang
collection PubMed
description PURPOSE: To develop a noninvasive radiomics-based nomogram for identification of disagreement in pathology between endoscopic biopsy and postoperative specimens in gastric cancer (GC). MATERIALS AND METHODS: This observational study recruited 181 GC patients who underwent pre-treatment computed tomography (CT) and divided them into a training set (n = 112, single-energy CT, SECT), a test set (n = 29, single-energy CT, SECT) and a validation cohort (n = 40, dual-energy CT, DECT). Radiomics signatures (RS) based on five machine learning algorithms were constructed from the venous-phase CT images. AUC and DeLong test were used to evaluate and compare the performance of the RS. We assessed the dual-energy generalization ability of the best RS. An individualized nomogram combined the best RS and clinical variables was developed, and its discrimination, calibration, and clinical usefulness were determined. RESULTS: RS obtained with support vector machine (SVM) showed promising predictive capability with AUC of 0.91 and 0.83 in the training and test sets, respectively. The AUC of the best RS in the DECT validation cohort (AUC, 0.71) was significantly lower than that of the training set (Delong test, p = 0.035). The clinical-radiomic nomogram accurately predicted pathologic disagreement in the training and test sets, fitting well in the calibration curves. Decision curve analysis confirmed the clinical usefulness of the nomogram. CONCLUSION: CT-based radiomics nomogram showed potential as a clinical aid for predicting pathologic disagreement status between biopsy samples and resected specimens in GC. When practicability and stability are considered, the SECT-based radiomics model is not recommended for DECT generalization. CRITICAL RELEVANCE STATEMENT: Radiomics can identify disagreement in pathology between endoscopic biopsy and postoperative specimen. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13244-023-01459-w.
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spelling pubmed-103230702023-07-07 Virtual biopsy using CT radiomics for evaluation of disagreement in pathology between endoscopic biopsy and postoperative specimens in patients with gastric cancer: a dual-energy CT generalizability study Liu, Yiyang Zhao, Shuai Wu, Zixin Liang, Hejun Chen, Xingzhi Huang, Chencui Lu, Hao Yuan, Mengchen Xue, Xiaonan Luo, Chenglong Liu, Chenchen Gao, Jianbo Insights Imaging Original Article PURPOSE: To develop a noninvasive radiomics-based nomogram for identification of disagreement in pathology between endoscopic biopsy and postoperative specimens in gastric cancer (GC). MATERIALS AND METHODS: This observational study recruited 181 GC patients who underwent pre-treatment computed tomography (CT) and divided them into a training set (n = 112, single-energy CT, SECT), a test set (n = 29, single-energy CT, SECT) and a validation cohort (n = 40, dual-energy CT, DECT). Radiomics signatures (RS) based on five machine learning algorithms were constructed from the venous-phase CT images. AUC and DeLong test were used to evaluate and compare the performance of the RS. We assessed the dual-energy generalization ability of the best RS. An individualized nomogram combined the best RS and clinical variables was developed, and its discrimination, calibration, and clinical usefulness were determined. RESULTS: RS obtained with support vector machine (SVM) showed promising predictive capability with AUC of 0.91 and 0.83 in the training and test sets, respectively. The AUC of the best RS in the DECT validation cohort (AUC, 0.71) was significantly lower than that of the training set (Delong test, p = 0.035). The clinical-radiomic nomogram accurately predicted pathologic disagreement in the training and test sets, fitting well in the calibration curves. Decision curve analysis confirmed the clinical usefulness of the nomogram. CONCLUSION: CT-based radiomics nomogram showed potential as a clinical aid for predicting pathologic disagreement status between biopsy samples and resected specimens in GC. When practicability and stability are considered, the SECT-based radiomics model is not recommended for DECT generalization. CRITICAL RELEVANCE STATEMENT: Radiomics can identify disagreement in pathology between endoscopic biopsy and postoperative specimen. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13244-023-01459-w. Springer Vienna 2023-07-05 /pmc/articles/PMC10323070/ /pubmed/37405591 http://dx.doi.org/10.1186/s13244-023-01459-w Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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 Original Article
Liu, Yiyang
Zhao, Shuai
Wu, Zixin
Liang, Hejun
Chen, Xingzhi
Huang, Chencui
Lu, Hao
Yuan, Mengchen
Xue, Xiaonan
Luo, Chenglong
Liu, Chenchen
Gao, Jianbo
Virtual biopsy using CT radiomics for evaluation of disagreement in pathology between endoscopic biopsy and postoperative specimens in patients with gastric cancer: a dual-energy CT generalizability study
title Virtual biopsy using CT radiomics for evaluation of disagreement in pathology between endoscopic biopsy and postoperative specimens in patients with gastric cancer: a dual-energy CT generalizability study
title_full Virtual biopsy using CT radiomics for evaluation of disagreement in pathology between endoscopic biopsy and postoperative specimens in patients with gastric cancer: a dual-energy CT generalizability study
title_fullStr Virtual biopsy using CT radiomics for evaluation of disagreement in pathology between endoscopic biopsy and postoperative specimens in patients with gastric cancer: a dual-energy CT generalizability study
title_full_unstemmed Virtual biopsy using CT radiomics for evaluation of disagreement in pathology between endoscopic biopsy and postoperative specimens in patients with gastric cancer: a dual-energy CT generalizability study
title_short Virtual biopsy using CT radiomics for evaluation of disagreement in pathology between endoscopic biopsy and postoperative specimens in patients with gastric cancer: a dual-energy CT generalizability study
title_sort virtual biopsy using ct radiomics for evaluation of disagreement in pathology between endoscopic biopsy and postoperative specimens in patients with gastric cancer: a dual-energy ct generalizability study
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10323070/
https://www.ncbi.nlm.nih.gov/pubmed/37405591
http://dx.doi.org/10.1186/s13244-023-01459-w
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