Ji Xianqun, Shang Yu, Tan Lin, Hu Yan, Liu Junjie, Song Lina, Zhang Junyan, Wang Jingxian, Ye Yingjian, Zhang Haidong, Peng Tianfang, An Peng
Department of Radiology and Surgery, Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang, China.
Department of Emergency Internal Medicine and Orthopedics, Hubei Province Clinical Research Center of Parkinson's Disease, Xiangyang Key Laboratory of Movement Disorders, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, China.
Clin Med Insights Oncol. 2024 Apr 15;18:11795549241245698. doi: 10.1177/11795549241245698. eCollection 2024.
Medium- to high-risk classification-gastrointestinal stromal tumors (MH-GIST) have a high recurrence rate and are difficult to treat. This study aims to predict the recurrence of MH-GIST within 3 years after surgery based on clinical data and preoperative Delta-CT Radiomics modeling.
A retrospective analysis was conducted on clinical imaging data of 242 cases confirmed to have MH-GIST after surgery, including 92 cases of recurrence and 150 cases of normal. The training set and test set were established using a 7:3 ratio and time cutoff point. In the training set, multiple prediction models were established based on clinical data of MH-GIST and the changes in radiomics texture of enhanced computed tomography (CT) at different time periods (Delta-CT radiomics). The area under curve (AUC) values of each model were compared using the Delong test, and the clinical net benefit of the model was tested using decision curve analysis (DCA). Then, the model was externally validated in the test set, and a novel nomogram predicting the recurrence of MH-GIST was finally created.
Univariate analysis confirmed that tumor volume, tumor location, neutrophil-lymphocyte ratio (NLR), platelet lymphocyte ratio (PLR), diabetes, spicy hot pot, CT enhancement mode, and Radscore 1/2 were predictive factors for MH-GIST recurrence ( < .05). The combined model based on these above factors had significantly higher predictive performance (AUC = 0.895, 95% confidence interval [CI] = [0.839-0.937]) than the clinical data model (AUC = 0.735, 95% CI = [0.6 62-0.800]) and radiomics model (AUC = 0.842, 95% CI = [0.779-0.894]). Decision curve analysis also confirmed the higher clinical net benefit of the combined model, and the same results were validated in the test set. The novel nomogram developed based on the combined model helps predict the recurrence of MH-GIST.
The nomogram of clinical and Delta-CT radiomics has important clinical value in predicting the recurrence of MH-GIST, providing reliable data reference for its diagnosis, treatment, and clinical decision-making.
中高危分类的胃肠道间质瘤（MH-GIST）复发率高且难以治疗。本研究旨在基于临床数据和术前Delta-CT影像组学建模预测MH-GIST术后3年内的复发情况。
对242例术后确诊为MH-GIST的临床影像数据进行回顾性分析，其中复发92例，未复发150例。按照7:3的比例和时间截断点建立训练集和测试集。在训练集中，基于MH-GIST的临床数据以及不同时期增强计算机断层扫描（CT）的影像组学纹理变化（Delta-CT影像组学）建立多个预测模型。使用德龙检验比较各模型的曲线下面积（AUC）值，并采用决策曲线分析（DCA）检验模型的临床净效益。然后，在测试集中对模型进行外部验证，最终创建一个预测MH-GIST复发的新型列线图。
单因素分析证实肿瘤体积、肿瘤位置、中性粒细胞与淋巴细胞比值（NLR）、血小板与淋巴细胞比值（PLR）、糖尿病、食用麻辣火锅、CT增强模式以及Radscore 1/2是MH-GIST复发的预测因素（P<0.05）。基于上述因素的联合模型预测性能显著高于临床数据模型（AUC = 0.895，95%置信区间[CI] = [0.839 - 0.937]）和影像组学模型（AUC = 0.842，95% CI = [0.779 - 0.894]）。决策曲线分析也证实联合模型具有更高的临床净效益，且在测试集中得到了相同结果。基于联合模型开发的新型列线图有助于预测MH-GIST的复发。
临床和Delta-CT影像组学列线图在预测MH-GIST复发方面具有重要临床价值，为其诊断、治疗及临床决策提供了可靠的数据参考。
