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 GISTs⁴. In contrast, as a benign neoplasm with a rare probability of metastasizing and apparently the most frequent myogenic tumors of gastric SMTs, gastric leiomyomas (GLMs) require conservative observation or minimally invasive treatments instead of surgery, except for cases in which the lesions are larger than 5 cm^5,6. Thus, it is of great importance to distinguish GISTs from GLMs smaller than 5 cm because of their substantially different therapeutic and prognostic implications. This distinction is particularly challenging for small lesions, as they often share overlapping imaging features and lack definitive clinical symptoms^7,8.

Endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA), as the most mature technique relating to pretherapeutic identification in gastric SMTs, has been proven to provide a safe and accurate pathological diagnosis of GISTs⁷. Unfortunately, the ability of EUS-FNA to extract tissue samples is severely limited, which appears to reduce the accuracy of its diagnosis⁹. Moreover, the technique is considered to be invasive. Computed tomography is considered an optimal and economical imaging tool for the preoperative diagnosis of gastrointestinal tract tumors¹⁰. However, differentiating between GISTs and GLMs preoperatively remains challenging, given the complexity of their similar clinical symptoms and CT appearances¹⁰.

Radiomics represents a promising tool with the ability to extract quantitative imaging features from radiographic images automatically, allowing the objective quantification of the heterogeneity of tumors¹¹. In previous studies, radiomics models have been developed primarily for risk stratification of GISTs based on CT or MRI, demonstrating their potential in evaluating malignant potential^12,13,14. However, studies focused specifically on the differential diagnosis between GISTs and GLMs remain relatively rare. To present knowledge, no prior CT-based radiomics nomogram has been developed for the preoperative differentiation of GISTs from GLMs. Therefore, this study constructs a preoperative prediction nomogram using morphological features of CT images and radiomics parameters to address the abovementioned challenges.

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 ≤5 cm; (2) availability of complete pathological diagnosis; (3) undergoing radical surgery with curative intent; and (4) having undergone contrast-enhanced CT within 30 days prior to surgery. Patients with a history of malignant tumors or preoperative therapy were excluded. Therefore, 110 GIST patients and 62 with GLMs were enrolled in the present study. A population flowchart was demonstrated (Figure 1). A total of 172 patients (110 GISTs, 62 GLMs; 82 males, 90 females) were included, with a mean age of 55.05 ± 12.42 years (range: 22-79). Patients were randomly assigned to a training set (n = 120) and a validation set (n = 52) using simple randomization without stratification. Baseline demographic data are summarized in Table 1.

2. CT image analysis

CT scanning within the present research was performed on 64-slice CT scanners. Every patient was required to fast for at least 6 h. Approximately 30 min before the CT scan, each patient was instructed to orally ingest 500-800 mL of tap water, and 1000 mL of water was requested to be taken immediately before the scan to distend the stomach. All patients in the study were positioned in the supine position to prevent artefacts caused by air in the stomach, while ensuring that all lesion areas were covered. Patients were informed of exposure to ionizing radiation as part of the clinical imaging procedure. Prior to contrast administration, contraindications to iodinated contrast medium, including known allergy and renal insufficiency (eGFR <30 mL/min/1.73 m²), were screened. Patients were monitored for at least 15 min post-injection for acute adverse reactions. A standard reconstruction algorithm was apparently utilized, and the scanning process strictly followed these specifications: 120 kV tube voltage; 250-300 mA tube current; 1.5 mm slice thickness and interval. A soft-tissue reconstruction kernel (B30f) was used. Subsequently, during the enhanced scanning process, 80-120 mL iodinated contrast agent was administered, with an injection rate of 3.0 mL/s intravenously. Arterial, portal, and delayed phases should be collected during approximately 30 s, 60 s, and 120 s after the injection time. For radiomic analysis, images were preprocessed by resampling to an isotropic voxel size of 1.5 × 1.5 × 1.5 mm³ and intensity discretization using a fixed bin width of 25 Hounsfield units (HU).

3. Radiomic feature extraction

As for radiomic features, LIFEx software (version 4.90; www.lifexsoft.org) was used to extract features from the region of interest (ROI). All regions of interest were delineated on portal venous phase CT images only, which were selected for radiomic analysis to ensure consistency and feature stability. For each patient, the ROI was delineated on all slices containing the lesion in the transverse plane, with sagittal and coronal views used to ensure accurate volume coverage (Figure 2). ROIs were independently drawn by two radiologists without consensus during initial segmentation to enable reproducibility assessment. The ROI represented a two-dimensional (2D) single-slice contour, not a three-dimensional volume. All tumor areas were included when delineating ROIs, and calcifications were excluded based on density thresholds in the LIFEx software setting. Visual quality control was performed to ensure adequate gastric distension and complete inclusion of the lesion within the ROI. Intra- and interclass correlation coefficients (ICCs) were calculated to assess reproducibility of radiomic feature extraction; features with ICC > 0.75 were retained. Through the automatic algorithm of LIFEx software, 37 radiomic features were obtained: 5 histogram parameters, 7 grey-level co-occurrence matrix (GLCM) parameters, 11 grey-level run length matrix (GLRLM) parameters, 3 neighbourhood grey-level difference matrix (NGLDM) parameters, and 11 grey-level zone length matrix (GLZLM) parameters. Feature extraction was performed via the menu path: Segmentation → Radiomics → Compute. Image preprocessing included resampling to an isotropic voxel size of 1.5 × 1.5 × 1.5 mm³ and intensity discretization using a fixed bin width of 25 Hounsfield units (HU). No feature scaling or normalization was applied prior to modeling.

4. Statistics

Statistical analyses were performed using R software (version 3.6.0; https://www.r-project.org). Continuous variables were compared between groups using independent t-tests; categorical variables were assessed with χ² tests. A two-sided P value < 0.05 was considered statistically significant.

5. Establishment of prediction nomogram

Univariate analysis was conducted using Pearson's correlation to screen demographic, CT morphological, and radiomic characteristics for association with diagnosis (GIST vs. GLM). Multivariate modeling employed least absolute shrinkage and selection operator (LASSO) regression implemented via the glmnet package (cv.glmnet function) with 10-fold cross-validation¹⁵. The optimal regularization parameter λ was selected at lambda.1se to favor a more parsimonious model. The final predictors, age, tumor location, enhancement pattern, and NGLDM_Busyness, were linearly combined using their respective coefficients to generate a prediction score (Prescore). Among all extracted radiomic features, only NGLDM_Busyness retained a non-zero coefficient after LASSO penalization, suggesting that other radiomic features were either redundant or provided limited additional discriminative value. A nomogram was constructed based on the multivariate model using the nomogram function from the rms package.

6. Prediction effectiveness and validation of the nomogram

Predictive performance was evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy in both training and validation cohorts. Calibration was assessed via bootstrapping resampling (1000 iterations) and Hosmer-Lemeshow goodness-of-fit test; calibration curves were plotted accordingly. A P value < 0.05 indicated a significant deviation from ideal calibration. The concordance index (C-index) was used to quantify discriminative reliability. Clinical utility was evaluated by decision curve analysis (DCA).

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.