超声影像组学联合模型预测甲状腺乳头状癌同侧颈部中央区淋巴结转移的价值

Value of ultrasonography combined with model in predicting ipsilateral central cervical lymph node metastasis in papillary thyroid carcinoma

目的 探讨超声检查联合模型预测甲状腺乳头状癌(PTC)同侧中央区颈部淋巴结转移(CLNM)的价值。方法 回顾性分析南方医科大学南方医院2021年1~7月137例经病理证实的PTC患者的临床资料及术前甲状腺超声二维影像,根据术后病理结果将患者分为转移组65例,非转移组72例。所有患者均淋巴结行预防性中央区淋巴结清扫,并根据术后病理结果分为转移组和非转移组。在超声影像中手动勾画病变,从处理后的超声图像中导出了纹理特征。然后使用ICC、统计筛选、相关系数筛选以及LASSO方法,最终将LASSO筛选的非0特征作为输入,进行影像特征模型建模。将137例患者的临床有效信息构建与影像特征模型相同的临床特征模型。将影像特征与临床特征相结合,构建联合模型。结果 在影像特征模型中,ExtraTrees模型表现最佳,训练集和测试集的曲线下面积分别为0.895和0.836。临床特征的最优模型也是ExtraTrees模型,训练集和测试集的曲线下面积分别为0.843和0.701。而联合模型的预测能力最好,训练集和测试集的曲线下面积分别为0.900和0.854。结论 结合影像特征和临床特征的联合模型对PTC同侧CLNM的预测能力较好,可为临床决策提供一种无创、有效的方法。

Objective To investigate the value of ultrasound combined model in predicting ipsilateral central cervical lymph node metastasis(CLNM) in papillary thyroid carcinoma(PTC).Methods The clinical data and preoperative two-dimensional ultrasound images of 137 patients with pathologically confirmed PTC in Nanfang Hospital of Southern Medical University from January 2021 to July 2021 were retrospectively analyzed,and they were were divided into metastatic group(n=65) and non-metastatic group(n=72) by postoperative pathological results.All patients underwent prophylactic central lymph node dissection.The lesions were delineated manually in the ultrasound images,and the texture features were derived from the processed ultrasound images.Then ICC,statistical screening,correlation coefficient screening and LASSO method were used,and the non-0 features filtered by LASSO were used as input to build the image feature model.137 patients' clinically effective information was used to construct the same clinical feature model as the image feature model.A combined model was constructed by combining imaging features with clinical features.Results Among the image feature models,the ExtraTrees model has the best performance,and the AUC of the training set and the test set are 0.895 and 0.836 respectively.The optimal model for clinical features is also the ExtraTrees model,with AUC of 0.843 and 0.701 in the training and test sets,respectively.The combined model has the best predictive ability,with AUC of 0.900 and 0.854 for the training set and test set,respectively.Conclusion The combined model combining imaging features and clinical features has a good ability to predict CLNM in the ipsilateral central region of PTC,and it can provide a non-invasive and effective method for clinical decision-making.