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©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 wwl152559063@163.com. 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:
https://creativecommons.org/Licenses/by-nc/4.0/ 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. wwl152559063@163.com
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
Processing time: 107 Days and 23 Hours
BACKGROUND
Lymphovascular invasion (LVI) and perineural invasion (PNI) are important prognostic factors for gastric cancer (GC) that indicate an increased risk of metastasis and poor outcomes. Accurate preoperative prediction of LVI/PNI status could help clinicians identify high-risk patients and guide treatment decisions. However, prior models using conventional computed tomography (CT) images to predict LVI or PNI separately have had limited accuracy. Spectral CT provides quantitative enhancement parameters that may better capture tumor invasion. We hypothesized that a predictive model combining clinical and spectral CT parameters would accurately preoperatively predict LVI/PNI status in GC patients.
AIM
To develop and test a machine learning model that fuses spectral CT parameters and clinical indicators to predict LVI/PNI status accurately.
METHODS
This study used a retrospective dataset involving 257 GC patients (training cohort, n = 172; validation cohort, n = 85). First, several clinical indicators, including serum tumor markers, CT-TN stages and CT-detected extramural vein invasion (CT-EMVI), were extracted, as were quantitative spectral CT parameters from the delineated tumor regions. Next, a two-step feature selection approach using correlation-based methods and information gain ranking inside a 10-fold cross-validation loop was utilized to select informative clinical and spectral CT parameters. A logistic regression (LR)-based nomogram model was subsequently constructed to predict LVI/PNI status, and its performance was evaluated using the area under the receiver operating characteristic curve (AUC).
RESULTS
In both the training and validation cohorts, CT T3-4 stage, CT-N positive status, and CT-EMVI positive status are more prevalent in the LVI/PNI-positive group and these differences are statistically significant (P < 0.05). LR analysis of the training group showed preoperative CT-T stage, CT-EMVI, single-energy CT values of 70 keV of venous phase (VP-70 keV), and the ratio of standardized iodine concentration of equilibrium phase (EP-NIC) were independent influencing factors. The AUCs of VP-70 keV and EP-NIC were 0.888 and 0.824, respectively, which were slightly greater than those of CT-T and CT-EMVI (AUC = 0.793, 0.762). The nomogram combining CT-T stage, CT-EMVI, VP-70 keV and EP-NIC yielded AUCs of 0.918 (0.866-0.954) and 0.874 (0.784-0.936) in the training and validation cohorts, which are significantly higher than using each of single independent factors (P < 0.05).
CONCLUSION
The study found that using portal venous and EP spectral CT parameters allows effective preoperative detection of LVI/PNI in GC, with accuracy boosted by integrating clinical markers.
Core Tip: This study developed a machine learning model using clinical indicators and spectral computed tomography (CT) imaging parameters to preoperatively predict lymphovascular and perineural invasive risk in gastric cancer patients. The model combining CT staging, extramural vein invasive based on CT, and quantitative spectral CT measures had high accuracy for noninvasive prediction of these important histological features.
