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Preoperative prediction of Lauren classification in gastric cancer: a radiomics model based on dual-energy CT iodine map

OBJECTIVE: To investigate the value of a radiomics model based on dual-energy computed tomography (DECT) venous-phase iodine map (IM) and 120 kVp equivalent mixed images (MIX) in predicting the Lauren classification of gastric cancer. METHODS: A retrospective analysis of 240 patients undergoing preo...

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Autores principales: Li, Min, Qin, Hongtao, Yu, Xianbo, Sun, Junyi, Xu, Xiaosheng, You, Yang, Ma, Chongfei, Yang, Li
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Vienna 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10350444/
https://www.ncbi.nlm.nih.gov/pubmed/37454355
http://dx.doi.org/10.1186/s13244-023-01477-8
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author Li, Min
Qin, Hongtao
Yu, Xianbo
Sun, Junyi
Xu, Xiaosheng
You, Yang
Ma, Chongfei
Yang, Li
author_facet Li, Min
Qin, Hongtao
Yu, Xianbo
Sun, Junyi
Xu, Xiaosheng
You, Yang
Ma, Chongfei
Yang, Li
author_sort Li, Min
collection PubMed
description OBJECTIVE: To investigate the value of a radiomics model based on dual-energy computed tomography (DECT) venous-phase iodine map (IM) and 120 kVp equivalent mixed images (MIX) in predicting the Lauren classification of gastric cancer. METHODS: A retrospective analysis of 240 patients undergoing preoperative DECT and postoperative pathologically confirmed gastric cancer was done. Training sets (n = 168) and testing sets (n = 72) were randomly assigned with a ratio of 7:3. Patients are divided into intestinal and non-intestinal groups. Traditional features were analyzed by two radiologists, using logistic regression to determine independent predictors for building clinical models. Using the Radiomics software, radiomics features were extracted from the IM and MIX images. ICC and Boruta algorithm were used for dimensionality reduction, and a random forest algorithm was applied to construct the radiomics model. ROC and DCA were used to evaluate the model performance. RESULTS: Gender and maximum tumor thickness were independent predictors of Lauren classification and were used to build a clinical model. Separately establish IM-radiomics (R-IM), mixed radiomics (R-MIX), and combined IM + MIX image radiomics (R-COMB) models. In the training set, each radiomics model performed better than the clinical model, and the R-COMB model showed the best prediction performance (AUC: 0.855). In the testing set also, the R-COMB model had better prediction performance than the clinical model (AUC: 0.802). CONCLUSION: The R-COMB radiomics model based on DECT-IM and 120 kVp equivalent MIX images can effectively be used for preoperative noninvasive prediction of the Lauren classification of gastric cancer. CRITICAL RELEVANCE STATEMENT: The radiomics model based on dual-energy CT can be used for Lauren classification prediction of preoperative gastric cancer and help clinicians formulate individualized treatment plans and assess prognosis. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13244-023-01477-8.
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spelling pubmed-103504442023-07-18 Preoperative prediction of Lauren classification in gastric cancer: a radiomics model based on dual-energy CT iodine map Li, Min Qin, Hongtao Yu, Xianbo Sun, Junyi Xu, Xiaosheng You, Yang Ma, Chongfei Yang, Li Insights Imaging Original Article OBJECTIVE: To investigate the value of a radiomics model based on dual-energy computed tomography (DECT) venous-phase iodine map (IM) and 120 kVp equivalent mixed images (MIX) in predicting the Lauren classification of gastric cancer. METHODS: A retrospective analysis of 240 patients undergoing preoperative DECT and postoperative pathologically confirmed gastric cancer was done. Training sets (n = 168) and testing sets (n = 72) were randomly assigned with a ratio of 7:3. Patients are divided into intestinal and non-intestinal groups. Traditional features were analyzed by two radiologists, using logistic regression to determine independent predictors for building clinical models. Using the Radiomics software, radiomics features were extracted from the IM and MIX images. ICC and Boruta algorithm were used for dimensionality reduction, and a random forest algorithm was applied to construct the radiomics model. ROC and DCA were used to evaluate the model performance. RESULTS: Gender and maximum tumor thickness were independent predictors of Lauren classification and were used to build a clinical model. Separately establish IM-radiomics (R-IM), mixed radiomics (R-MIX), and combined IM + MIX image radiomics (R-COMB) models. In the training set, each radiomics model performed better than the clinical model, and the R-COMB model showed the best prediction performance (AUC: 0.855). In the testing set also, the R-COMB model had better prediction performance than the clinical model (AUC: 0.802). CONCLUSION: The R-COMB radiomics model based on DECT-IM and 120 kVp equivalent MIX images can effectively be used for preoperative noninvasive prediction of the Lauren classification of gastric cancer. CRITICAL RELEVANCE STATEMENT: The radiomics model based on dual-energy CT can be used for Lauren classification prediction of preoperative gastric cancer and help clinicians formulate individualized treatment plans and assess prognosis. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13244-023-01477-8. Springer Vienna 2023-07-16 /pmc/articles/PMC10350444/ /pubmed/37454355 http://dx.doi.org/10.1186/s13244-023-01477-8 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
Li, Min
Qin, Hongtao
Yu, Xianbo
Sun, Junyi
Xu, Xiaosheng
You, Yang
Ma, Chongfei
Yang, Li
Preoperative prediction of Lauren classification in gastric cancer: a radiomics model based on dual-energy CT iodine map
title Preoperative prediction of Lauren classification in gastric cancer: a radiomics model based on dual-energy CT iodine map
title_full Preoperative prediction of Lauren classification in gastric cancer: a radiomics model based on dual-energy CT iodine map
title_fullStr Preoperative prediction of Lauren classification in gastric cancer: a radiomics model based on dual-energy CT iodine map
title_full_unstemmed Preoperative prediction of Lauren classification in gastric cancer: a radiomics model based on dual-energy CT iodine map
title_short Preoperative prediction of Lauren classification in gastric cancer: a radiomics model based on dual-energy CT iodine map
title_sort preoperative prediction of lauren classification in gastric cancer: a radiomics model based on dual-energy ct iodine map
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10350444/
https://www.ncbi.nlm.nih.gov/pubmed/37454355
http://dx.doi.org/10.1186/s13244-023-01477-8
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