Yang Fan, Liu Chun-Wei, Zhang Dai, Wang Hai-Ling, Wei Xi, Yang Mo
Department of Ultrasound Diagnosis and Treatment, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China.
Department of Ultrasound, Tianjin's Clinical Research Center for Cancer, Tianjin, China.
Front Oncol. 2025 Jul 3;15:1590432. doi: 10.3389/fonc.2025.1590432. eCollection 2025.
To evaluate the value of oral contrast-enhanced ultrasonography and radiomics analysis in predicting the National Institutes of Health (NIH) staging of gastrointestinal stromal tumors (GISTs).
A retrospective cohort study was conducted on 204 patients presenting with GISTs in Tianjin Medical University Cancer Institute and Hospital from January 2020 to January 2023. The clinical profiles, oral contrast-enhanced ultrasonography (CEUS), and endoscopic ultrasound (EUS) imaging data were collected. 105 patients with high-risk and moderate-risk GISTs were classified into the high-risk group, while 99 patients with low-risk and very-low-risk GISTs were classified into the low-risk group. The ITK-SNAP software and Pyradiomics (version 3.0.1) package were used to extract a comprehensive set of ultrasonographic radiomics features from the segmented regions of interest (ROIs). The patient dataset was randomly divided into a training set and a validation set at a ratio of 7:3. Leveraging the XGBoost (XGB) algorithm within the Scikit-learn (Sklearn) machine-learning library, three distinct predictive models were developed: a clinical ultrasound imaging model (US model), an ultrasonographic radiomics model (US radiomics model), and a combined model integrating both clinical, ultrasound, and radiomics features. Additionally, 51 GIST patients from Tianjin Medical University General Hospital were included in the external validation analysis.
636 ultrasonic radiomics features from ROIs were successfully extracted. 6 key ultrasonic radiomics features were finally selected for subsequent model construction. In the internal validation set, the area under the curve (AUC), sensitivity, specificity, and accuracy for the US model, US radiomics model, combined model, and endoscopic ultrasound were 0.69, 0.62, 0.66, 0.64; 0.83, 0.85, 0.74, 0.79; 0.91, 0.86, 0.85, 0.85; and 0.94, 0.95, 0.85, 0.89, respectively. In the external validation set, the AUC, sensitivity, specificity, and accuracy for the US model, US radiomics model, combined model, and endoscopic ultrasound were 0.71, 0.65, 0.67, 0.66; 0.81, 0.77, 0.72, 0.74; 0.89, 0.85, 0.80, 0.83; and 0.90, 0.93, 0.86, 0.90, respectively. The Delong test showed a larger AUC in the US radiomics model compared with the US model (Z = 2.776, P < 0.01). The performance of the combined model was significantly better than that of the US model (Z = 4.822, P < 0.01) and the US radiomics model (Z = 2.200, P = 0.029). However, there was no significant difference in AUC between the combined model and the endoscopic ultrasound (Z = 1.150, P = 0.141). The superiority of the combined model was further demonstrated by the calibration curve (CC) and decision curve analysis (DCA) in both the internal and external validation sets.
This study demonstrates that the US radiomics model, based on oral contrast-enhanced ultrasonography images, is feasible for predicting the NIH risk stratification of gastrointestinal stromal tumors (GISTs). The combined model showed a better diagnostic performance.
评估口服对比增强超声检查及影像组学分析在预测胃肠道间质瘤（GIST）美国国立卫生研究院（NIH）分期中的价值。
对2020年1月至2023年1月在天津医科大学肿瘤医院就诊的204例GIST患者进行回顾性队列研究。收集临床资料、口服对比增强超声（CEUS）及内镜超声（EUS）影像数据。105例高危和中危GIST患者分为高危组，99例低危和极低危GIST患者分为低危组。使用ITK-SNAP软件和Pyradiomics（3.0.1版）软件包从分割的感兴趣区域（ROI）中提取一套完整的超声影像组学特征。患者数据集按7:3的比例随机分为训练集和验证集。利用Scikit-learn（Sklearn）机器学习库中的XGBoost（XGB）算法，开发了三种不同的预测模型：临床超声影像模型（US模型）、超声影像组学模型（US影像组学模型）以及整合临床、超声和影像组学特征的联合模型。此外，将天津医科大学总医院的51例GIST患者纳入外部验证分析。
成功从ROI中提取636个超声影像组学特征。最终选择6个关键超声影像组学特征用于后续模型构建。在内部验证集，US模型、US影像组学模型、联合模型及内镜超声的曲线下面积（AUC）、灵敏度、特异度和准确度分别为0.69、0.62、0.66、0.64；0.83、0.85、0.74、0.79；0.91、0.86、0.85、0.85；0.94、0.95、0.85、0.89。在外部验证集，US模型、US影像组学模型、联合模型及内镜超声的AUC、灵敏度、特异度和准确度分别为0.71、0.65、0.67、0.66；0.81、0.77、0.72、0.74；0.89、0.85、0.80、0.83；0.90、0.93、0.86、0.90。Delong检验显示，US影像组学模型的AUC大于US模型（Z = 2.776，P < 0.01）。联合模型的性能显著优于US模型（Z = 4.822，P < 0.01）和US影像组学模型（Z = 2.200，P = 0.029）。然而，联合模型与内镜超声的AUC无显著差异（Z = 1.150，P = 0.141）。校准曲线（CC）和决策曲线分析（DCA）在内部和外部验证集均进一步证明了联合模型的优越性。
本研究表明，基于口服对比增强超声图像的US影像组学模型在预测胃肠道间质瘤（GIST）的NIH风险分层方面是可行且有效的。联合模型显示出更好的诊断性能。
