基于CT影像组学诺模图术前预测甲状腺乳头状癌颈部中央区淋巴结转移的研究

目的 探讨基于CT影像组学与临床危险因素的诺模图在术前预测甲状腺乳头状癌颈部中央区淋巴结转移中的价值.方法 回顾性分析114例PTC患者,收集治疗前的CT及临床资料.以7:3比例通过完全随机方法将入组患者分为训练集(n=85)和测试集(n=29),从CT平扫期和增强动脉期的图像中提取影像组学特征.在训练集中,使用方差阈...

CT-based radiomics nomogram for the preoperative prediction of central lymph node metastases of papillary thyroid carcinoma

Objective To evaluate the value of a nomogram based on CT radiomics and clinical risk factors for the preoperative prediction of lymph node metastases of papillary thyroid carcinomas(PTCs)in the central neck. Methods The cases of 114 patients with PTCs diagnosed and treated surgically at Yantai Yuhuangding hospital were retrospectively analyzed, and the clinical and CT imaging data before treatment were collected. The data of the 114 patients were randomly divided into the training(n=85)and test(n=29)sets using a ratio of 7∶3. Radiological features were extracted from the images during the plain CT scan and the enhanced CT arterial phases. Analysis of variance(ANOVA)and the least absolute shrinkage and selection operator(LASSO)algorithm were used to reduce the dimensionality of the radiomics features in the training set to screen out the features with statistical significance. Combining the clinical risk factors and CT-reported lymph nodes status, the efficacy predictors were screened by multivariate logistic regression, and a radiomics nomogram was established for the preoperative prediction of lymph node metastases of PTCs in the central cervical region. The ROC curve was used to evaluate the diagnostic efficiency of the model, and the model was internally verified, calibrated, and clinically applied. Results A total of 2818 CT radiomics features were extracted from the plain and enhanced CT images of 114 patients. After dimensional reduction, 25 features were highly correlated with lymph node metastases of PTCs in the central neck area. The radiomic nomogram, which incorporated the radiomic signature and CT-reported lymph node status, also showed good calibration and discrimination for the test set(AUC 0.858), which were higher than those of the individual CT image nomogram model for the test set(AUC 0.769)those of the prediction model for the individual lymph node status test set(AUC 0.721). The degree of calibration, internal verification consistency, and clinical value were high for this nomogram. Conclusion The presented radiomics nomogram, a non-invasive preoperative tool that incorporates the radiomic signature and CT-reported lymph node status, showed a favorable predictive accuracy for central lymph node metastases in patients with PTCs.