Retrospective Study
Copyright ©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Aug 21, 2022; 28(31): 4363-4375
Published online Aug 21, 2022. doi: 10.3748/wjg.v28.i31.4363
Application of computed tomography-based radiomics in differential diagnosis of adenocarcinoma and squamous cell carcinoma at the esophagogastric junction
Ke-Pu Du, Wen-Peng Huang, Si-Yun Liu, Yun-Jin Chen, Li-Ming Li, Xiao-Nan Liu, Yi-Jing Han, Yue Zhou, Chen-Chen Liu, Jian-Bo Gao
Ke-Pu Du, Wen-Peng Huang, Yun-Jin Chen, Li-Ming Li, Yi-Jing Han, Yue Zhou, Chen-Chen Liu, Jian-Bo Gao, Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan Province, China
Si-Yun Liu, Department of Pharmaceutical Diagnostics, General Electric Company Healthcare, Beijing 100176, China
Xiao-Nan Liu, Department of Pathology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan Province, China
Author contributions: Du KP and Huang WP designed the research; Liu SY and Li LM performed the research; Chen YJ, Han YJ, Zhou Y, and Liu CC collected the data; Du KP and Huang WP analyzed the data and wrote the paper; Gao JB reviewed the paper; and all authors have read and approved the final manuscript.
Institutional review board statement: The study was approved by the Institutional Review Board at the First Affiliated Hospital of Zhengzhou University (No. 2021-ky-1070-002).
Conflict-of-interest statement: The authors declare that they have no competing interests to disclose.
Data sharing statement: All data generated or analyzed during this study are included in this published article.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Jian-Bo Gao, PhD, Academic Research, Chairman, Chief Doctor, Instructor, Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No. 1 East Jianshe Road, Zhengzhou 450052, Henan Province, China. jianbogaochina@163.com
Received: March 16, 2022
Peer-review started: March 16, 2022
First decision: June 11, 2022
Revised: June 11, 2022
Accepted: July 25, 2022
Article in press: July 25, 2022
Published online: August 21, 2022
Processing time: 153 Days and 2.1 Hours
ARTICLE HIGHLIGHTS
Research background

Differentiating squamous cell carcinoma of the esophagogastric junction (SCCEG) from adenocarcinoma of the esophagogastric junction (AEG) can indicate Siewert stage and whether the surgical route for patients with carcinoma of the esophagogastric junction (CEGJ) is transthoracic or transabdominal, as well as aid in determining the extent of lymph node dissection. With the development of neoadjuvant therapy, preoperative determination of pathological type can help in the selection of neoadjuvant radiotherapy and chemotherapy regimens.

Research motivation

Radiomics technique uses a combined medical-industrial approach to transform traditional images into digital quantitative features, which has potential for digging the potential biological characteristics and heterogeneity of tumor images and has been widely and non-invasively used in the diagnosis, differential diagnosis, and disease evaluation. However, to the best of our knowledge, there is no literature that has evaluated whether a radiomics signature derived from computed tomography (CT) images would be useful in predicting pathological type in patients with CEGJ.

Research objectives

In the current study, we proposed a CT radiomics-based classification method by considering the performance of 3D or 2D segmentation and multiple CT imaging phases to discriminate SCCEG and AEG.

Research methods

We retrospectively analyzed the preoperative contrasted-enhanced CT imaging data of single-center patients with pathologically confirmed SCCEG (n = 130) and AEG (n = 130). One thousand four hundred and nine radiomics features were separately extracted from 2D or 3D regions of interest in arterial and venous phases. Totally, 6 logistic regression models were established based on 2D and 3D multi-phase features.

Research results

The venous model showed a positive improvement compared with the arterial model (NRI > 0, IDI > 0), and the 3D-venous and combined models showed a significant positive improvement compared with the 2D-venous and combined models (P < 0.05). Decision curve analysis showed that the combined 3D-arterial-venous model and 3D-venous model had a higher net clinical benefit within the same threshold probability range in the test group.

Research conclusions

The combined arterial-venous CT radiomics model based on 3D segmentation can improve the performance in differentiating EGJ squamous cell carcinoma from adenocarcinoma.

Research perspectives

These models require further validation as decision support tools to guide clinical practice and develop individualized treatment plans for CEGJ patients.