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Insights into Imaging
Published in: Insights into Imaging 1/2023

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

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

Authors: Yiyang Liu, Shuai Zhao, Zixin Wu, Hejun Liang, Xingzhi Chen, Chencui Huang, Hao Lu, Mengchen Yuan, Xiaonan Xue, Chenglong Luo, Chenchen Liu, Jianbo Gao

Published in: Insights into Imaging | Issue 1/2023

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Abstract

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

Appendix
Available only for authorised users
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Metadata
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
Authors
Yiyang Liu
Shuai Zhao
Zixin Wu
Hejun Liang
Xingzhi Chen
Chencui Huang
Hao Lu
Mengchen Yuan
Xiaonan Xue
Chenglong Luo
Chenchen Liu
Jianbo Gao
Publication date
01-12-2023
Publisher
Springer Vienna
Published in
Insights into Imaging / Issue 1/2023
Electronic ISSN: 1869-4101
DOI
https://doi.org/10.1186/s13244-023-01459-w

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