能谱CT组学列线图术在胃神经分泌肿瘤检测及误诊分析

Energy Spectrum CT Omics Nomogram in the Detection and Misdiagnosis Analysis of Gastric Neurosecretory Tumors

本研究探讨了能谱CT组学列线图术在胃神经分泌肿瘤检测及误诊分析中的应用。选取胃神经分泌肿瘤患者34例,依据完全随机法划分为训练集(25例)与测试集(9例)。能谱CT影像组学分析病灶特征;LASSO回归建立影像组学风险评分;Bootstrap法分别分析ROC曲线AUC、特异性、敏感性及误诊率。训练集与测试集中,肿瘤大小、淋巴结肿大、静脉瘤栓差异显著(P<0.05)。与低级别患者相比,高级别患者风险评分更高(P<0.05)。经Bootstrap法验证,训练集中误诊率8.00%,AUC为0.940,特异性92.0%,敏感性95.4%;测试集中误诊率11.11%,AUC为0.859,特异度88.89%,敏感度77.6%。总之,能谱CT组学列线图术在胃神经分泌肿瘤检测及误诊分析中预测效能良好。

The purpose of this study was to explore the application of energy spectrum CT omics nomogram in the detection and misdiagnosis analysis of gastric neurosecretory tumors.A total of 34 patients with gastric neurosecretory tumors were selected and divided into training set(25 cases)and test set(9 cases)according to the completely random method.Energy spectrum CT imaging omics analysis of lesion characteristics;LASSO regression to establish imaging omics risk score;Bootstrap method to analyze the AUC,specificity,sensitivity and misdiagnosis rate under the ROC curve.The training set and the test set showed significant differences in tumor size,lymph node enlargement,and venous tumor thrombus(P<0.05).Compared with low-grade patients,high-grade patients had higher risk scores(P<0.05).Validated by Bootstrap method,the misdiagnosis rate in the training set is 8.00%,AUC 0.940,specificity 92.0%,and sensitivity 95.4%;the misdiagnosis rate in the test set is 11.11%,AUC 0.859,specificity 88.89%,and sensitivity 77.6%.In conclusion,the power spectrum CT omics nomogram has good predictive performance in the detection and misdiagnosis analysis of gastric neurosecretory tumors.