Development of an Individualized CT-based Radiomics Nomogram for Preoperative Differentiation Between Gastric Stromal Tumors and Gastric Leiomyomas

Summary

A CT-based radiomics nomogram was developed to accurately differentiate gastric stromal tumors from gastric leiomyomas, enabling noninvasive and individualized preoperative diagnosis with high predictive performance.

Abstract

This protocol describes the development and validation of a computed tomography (CT)-based radiomics nomogram for the noninvasive preoperative differentiation of gastric stromal tumors (GISTs) and gastric leiomyomas (GLMs), two gastric submucosal lesions with distinct therapeutic strategies and prognostic implications. A retrospective cohort of 172 patients with pathologically confirmed GISTs or GLMs who underwent contrast-enhanced CT within 30 days before surgery was analyzed. Patients were randomly assigned to a training cohort (n = 120) and a validation cohort (n = 52). Demographic variables, CT morphological characteristics, and quantitative radiomic features extracted from manually delineated regions of interest were systematically evaluated. Feature selection was performed using the least absolute shrinkage and selection operator (LASSO) regression with cross-validation. The final predictive model incorporated age, tumor location, enhancement pattern, and the radiomic feature NGLDM_Busyness, which reflects intratumoral texture heterogeneity. These variables were integrated to construct an individualized nomogram for clinical use. Model performance was assessed using receiver operating characteristic analysis, calibration curves, and decision curve analysis. The nomogram demonstrated strong discriminative ability, good calibration, and favorable clinical utility in both the training and validation cohorts, supporting its potential value as a noninvasive tool for individualized preoperative diagnosis.

Introduction

Gastric stromal tumors (GISTs), which are the most common gastric submucosal tumors (SMTs), tend to be considered potentially malignant regardless of their size, with those exhibiting a malignant clinical course accounting for approximately 10% to 30% of cases^1,2. According to the NIH consensus classification³, the clinical malignancy risk of GISTs increases progressively across the strata: very low, low, intermediate, and high risk, which are defined by tumor size and mitotic count. Early diagnosis and early surgical resection are recommended for patients with GISTs^4<....

Protocol

This research was approved by the ethics committee of the hospital (protocol number: 1612167-18). Given the retrospective nature of the study, the requirement for informed consent was waived. Patients with gastric stromal tumors (GISTs) or gastric leiomyomas (GLMs) were identified from institutional records between January 2017 and July 2022, all with postoperative pathological confirmation. The reagents and the equipment used are listed in the Table of Materials.

1. Patient details

Eligible patients are enrolled if they meet the following criteria: (1) gastric lesions with a maximum diameter Ͱ....

Results

Demographic and clinical features
A total of 172 patients with gastric submucosal tumors (110 GISTs, 62 GLMs) were included. Demographic and clinical features are summarized in Table 1, Table 2 and Table 3. This analysis aimed to identify baseline differences between GISTs and GLMs to inform subsequent model development. GISTs were more likely to occur in the gastric body in elderly patients with a moderate enhancement pattern, whereas GLMs were more.......

Discussion

Discriminating between gastrointestinal stromal tumors (GISTs) and gastric leiomyomas (GLMs) is clinically important because it directly affects treatment decisions and patient outcomes. A nomogram was developed and validated as a predictive nomogram that incorporates one demographic feature (age), two CT signs (location and enhancement pattern), and one radiomic parameter (NGLDM_Busyness). This model can be readily applied preoperatively in clinical practice. This study addresses a current gap in noninvasive preoperativ.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Contrast-enhanced CT scanner	Siemens Healthineers	SOMATOM Definition AS+	64-slice CT scanner; typical in Chinese tertiary hospitals during 2017–2022. Scanning parameters: 120 kV, 250–300 mA, 1.5 mm slice thickness, B30f kernel.
glmnet package (R)	CRAN	Version 4.0 (compatible with R 3.6.0)	LASSO regression with 10-fold cross-validation.
Iodinated contrast agent	Bayer AG	Ultravist 370 (Iopromide 370 mg I/mL)	Administered IV at 80–120 mL, 3.0 mL/s. Widely used non-ionic contrast agent in China for abdominal CT.
LIFEx software	www.lifexsoft.org	Version 4.90	Used for radiomic feature extraction from CT ROIs.
R software	The R Foundation	Version 3.6.0	For statistical analysis and modeling (https://www.r-project.org).
rms package (R)	CRAN	Version 5.1-4 (compatible with R 3.6.0)	Nomogram construction and calibration.