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Open Access 01-12-2023 | Gastric Cancer | Original Article

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

Authors: Min Li, Hongtao Qin, Xianbo Yu, Junyi Sun, Xiaosheng Xu, Yang You, Chongfei Ma, Li Yang

Published in: Insights into Imaging | Issue 1/2023

Abstract

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

Available only for authorised users

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Title: Preoperative prediction of Lauren classification in gastric cancer: a radiomics model based on dual-energy CT iodine map
Authors: Min Li
Hongtao Qin
Xianbo Yu
Junyi Sun
Xiaosheng Xu
Yang You
Chongfei Ma
Li Yang
Publication date: 01-12-2023
Publisher: Springer Vienna
Keywords: Gastric Cancer
Gastric Cancer
Computed Tomography
Computed Tomography
Published in: Insights into Imaging / Issue 1/2023
Electronic ISSN: 1869-4101
DOI: https://doi.org/10.1186/s13244-023-01477-8

Keynote webinar | Spotlight on medication adherence

Springer Medicine

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

Abstract

Objective

Methods

Results

Conclusion

Critical relevance statement

Graphical abstract

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Objective

Methods

Results

Conclusion

Critical relevance statement

Graphical abstract

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