Curr Med Imaging. 2026 Jan 27. doi: 10.2174/0115734056419448251211063018. Online ahead of print.
ABSTRACT
INTRODUCTION: Small gastrointestinal stromal tumors (SGISTs, maximum diameter≤2 cm) still carry a risk of malignancy, and their preoperative evaluation remains a significant challenge. Radiomics, an emerging technique for analyzing image data, has yet to be employed to assess the risk stratification of SGISTs. To develop and validate a CT radiomics model for the preoperative prediction of risk stratification in patients with SGISTs.
METHOD: This study enrolled 133 patients with SGISTs, including 97 in the low-grade group and 36 in the high-grade group. Patients were randomly assigned to a training set (n = 93) and a testing set (n = 40) at a ratio of 7:3. Radiomics features were extracted from preoperative CT images, and dimensionality reduction was performed using the LR-LASSO to identify the most predictive features for constructing the radiomics model. Clinical features were evaluated using univariate and multivariate logistic regression analyses to develop a clinical model. Subsequently, the optimal radiomics and clinical features were integrated to establish a combined model. Model performance was evaluated using ROC curve analysis, and a corresponding nomogram was generated to facilitate clinical application. The Delong test was used to compare the ROC curves, with a p-value < 0.05 considered statistically significant.
RESULTS: Univariable clinical analysis identified maximal tumour diameter as the only significant predictor, with the clinical model achieving an AUC of 0.641 (95% CI: 0.533-0.748). Among the radiomics signatures derived from multiphase CT (non-contrast to delayed phases), the model based on portal venous phase images demonstrated the highest discriminative ability, yielding the best AUC values in both the training set (AUC = 0.848, 95% CI: 0.764-0.931) and the testing set (AUC = 0.824, 95% CI: 0.696-0.953). The combined model, which integrated radiomics features with maximum tumour diameter, demonstrated improved performance, attaining an AUC of 0.862 (95% CI: 0.743-0.975) in the training set and 0.859 (95% CI: 0.743-0.975) in the testing set. Notably, the predictive performance of both the radiomics and combined models was significantly greater than that of the clinical model (DeLong test, P < 0.05). However, no statistically significant differences were observed between the AUC values of the radiomics and combined models. Calibration curves indicated a good fit, and the DCA demonstrated that both the radiomics model and the combined model provided greater clinical benefits.
DISCUSSION: The radiomics model demonstrated superior performance to the clinical model for the preoperative prediction of risk stratification in SGISTs. As a visualization tool, the nomogram of the combined model plays a critical role in optimizing early surgical resection decisions.
CONCLUSION: The radiomics model could serve as an effective tool for non-invasive risk stratification of SGISTs, offering clear advantages over risk stratification models based solely on conventional clinical parameters. This approach could support improved preoperative clinical decisionmaking.
PMID:41603218 | DOI:10.2174/0115734056419448251211063018
