Retrospective Study
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Feb 14, 2024; 30(6): 542-555
Published online Feb 14, 2024. doi: 10.3748/wjg.v30.i6.542
Preoperative prediction of lymphovascular and perineural invasion in gastric cancer using spectral computed tomography imaging and machine learning
Hui-Ting Ge, Jian-Wu Chen, Li-Li Wang, Tian-Xiu Zou, Bin Zheng, Yuan-Fen Liu, Yun-Jing Xue, Wei-Wen Lin
Hui-Ting Ge, Tian-Xiu Zou, Yuan-Fen Liu, Yun-Jing Xue, Wei-Wen Lin, Department of Radiology, Fujian Medical University Union Hospital, Fuzhou 350001, Fujian Province, China
Hui-Ting Ge, Jian-Wu Chen, Li-Li Wang, Fujian Key Laboratory of Intelligent Imaging and Precision Radiotherapy for Tumors, Fujian Medical University, Fuzhou 350001, Fujian Province, China
Hui-Ting Ge, Jian-Wu Chen, Li-Li Wang, Digestive, Hematological and Breast Malignancies, Clinical Research Center for Radiology and Radiotherapy of Fujian Province, Fuzhou 350001, Fujian Province, China
Jian-Wu Chen, Department of Radiation Oncology, Fujian Medical University Union Hospital, Fuzhou 350001, Fujian Province, China
Li-Li Wang, Department of Diagnostic Radiology, Fujian Medical University Union Hospital, Fuzhou 350001, Fujian Province, China
Bin Zheng, School of Electrical and Computer Engineering, University of Oklahoma, Oklahoma, OK 73019, United States
Co-first authors: Hui-Ting Ge and Jian-Wu Chen.
Co-corresponding authors: Yun-Jing Xue and Wei-Wen Lin.
Author contributions: Ge HT, Chen JW, and Wang LL contributed equally to this work; Ge HT, Chen JW, and Wang LL designed the research study and performed the research; Ge HT wrote the manuscript and collected data; Zou TX contributed new software and analytic tools; Wang LL and Zheng B analyzed the data; Liu YF provided methods; Xue YJ and Lin WW searched literature and managed the project; Xue YJ and Lin WW contributed equally to this work; and all authors have read and approve the final manuscript.
Supported by Science and Technology Project of Fujian Province, No. 2022Y0025.
Institutional review board statement: The study was reviewed and approved by the Ethical Review Board of Fujian Medical University Union Hospital (Fuzhou, China), approval No. 2022KJT016.
Informed consent statement: The requirement for informed consent was waived.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Data sharing statement: Technical appendix, statistical code, and dataset available from the corresponding author at Participants gave informed consent for data sharing but the presented data are anonymized and risk of identification is low.
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:
Corresponding author: Wei-Wen Lin, PhD, Doctor, Professor, Researcher, Department of Radiology, Fujian Medical University Union Hospital, Xinquan Road, Gulou District, Fuzhou 350001, Fujian Province, China.
Received: October 20, 2023
Peer-review started: October 20, 2023
First decision: December 5, 2023
Revised: December 18, 2023
Accepted: January 15, 2024
Article in press: January 15, 2024
Published online: February 14, 2024
Research background

The research background involves the critical role of lymphovascular invasion (LVI) and perineural invasion (PNI) as prognostic factors in gastric cancer (GC), indicating an increased risk of metastasis and poor patient outcomes. The ability to accurately predict LVI/PNI status preoperatively is significant for identifying high-risk patients and guiding treatment decisions. Conventional models using standard computed tomography (CT) images to predict these invasions have had limited success; thus, this study proposes a new approach using spectral CT imaging and machine learning to improve prediction accuracy.

Research motivation

The research is motivated by the necessity to improve preoperative predictions of LVI and PNI in GC patients, addressing the limitations of conventional CT imaging techniques. The primary objective is to develop a more precise predictive model by integrating spectral CT imaging parameters with clinical markers through machine learning algorithms. Successfully achieving this could refine preoperative assessments, aid in risk stratification, inform treatment planning, and potentially elevate future diagnostic strategies in the field of GC.

Research objectives

The primary objective of the research is to test the hypothesis that an optimal fusion of spectral CT parameters with clinical markers using a machine learning method can more accurately predict LVI or PNI status in GC patients before surgery. Specifically, the study analyzed a set of clinical indicators, such as preoperative CT evaluation of gastric wall invasion depth, lymph node metastasis, extramural vein invasion, and serum tumor markers, along with quantitative spectral CT parameters. The research aimed to develop a logistic regression (LR)-based nomogram model that integrates these clinical indicators with spectral CT parameters to predict histological LVI and PNI statuses in GC. Realizing these objectives has significant implications for improving preoperative staging and tailoring appropriate treatment plans for GC patients, thus advancing future research and diagnostic strategies in this field.

Research methods

The research adopted a retrospective dataset and a LR-based nomogram model that incorporated clinical indicators with quantitative spectral CT parameters for the preoperative prediction of lymphovascular and PNI in GC patients. Methods included using statistical software for univariate analysis and correlation-based feature selection, along with 10-fold cross-validation and information gain ranking within a training-validation cohort framework to select significant features. The model’s performance was evaluated through receiver operating characteristic (ROC) analysis, calibration using the Hosmer-Lemeshow test and bootstrapping, and decision curve analysis to quantify potential net benefits. These methods highlighted novel approaches in integrating machine learning with available clinical and imaging data to potentially improve preoperative assessment and treatment planning.

Research results

The research results demonstrated that CT values and parameters such as iodine concentration and normalized iodine concentration were significantly higher in the LVI/PNI-positive group across all phases (arterial, venous, and equilibrium) when compared to the LVI/PNI-negative group, with statistical significance (P < 0.05). Good inter- and intra-observer agreement was observed for these spectral CT parameters, as indicated by the inter-observer intraclass correlation coefficients (ICC) values ranging from 0.766 to 0.955 and intra-observer ICC values from 0.759 to 0.945. This reproducibility led to their retention for feature selection in developing the predictive model. These findings contribute to the overall research in the field by introducing reproducible and quantifiable spectral CT parameters as reliable predictors for LVI/PNI status in GC patients. The study opens avenues for further investigation into refining and validating these spectral CT-based assessment methods, possibly addressing existing challenges in preoperative staging and treatment planning.

Research conclusions

The study proposes a novel application of spectral CT imaging integrated with machine learning to preoperatively predict lymphovascular and PNI in patients with GC. Through the use of a logistic regression-based nomogram model, the research introduces a new method for combining clinical indicators with quantitative imaging parameters to improve the accuracy of preoperative assessments. This contributes to the field by proposing an alternative to the current postoperative pathology methods and could improve treatment planning by enabling non-invasive, individualized risk stratification prior to surgery.

Research perspectives

The direction of future research following this study is anticipated to focus on validating the noninvasive spectral CT-based machine learning model in prospective multicenter studies to confirm its clinical utility in preoperative risk stratification. Additionally, further research may explore the integration of this model in routine clinical practice to assess its impact on patient management, particularly in the identification of those who may benefit from more aggressive treatment strategies preoperatively. By refining and expanding the predictive capabilities of spectral CT imaging, future research could pave the way for improved individualized treatment planning and outcomes in GC care.