| 摘要: |
| 目的 探讨基于超声的机器学习及其联合临床特征预测隐匿性甲状腺乳头状癌患者颈部淋巴结转移的价值。方法 回顾性分析81例颈部淋巴结转移的隐匿性甲状腺乳头状癌患者和103例无颈部淋巴结患者的临床资料和二维声像图,按7:3比例随机分为训练组和验证组,提取超声图像特征,构建支持向量机(support vector machine, SVM)模型,并结合临床特征建立联合模型,观察预测模型的诊断效能。结果 共筛选出11个超声图像特征和2个临床特征建立机器学习预测模型和联合预测模型。机器学习模型在训练组预测淋巴结转移的AUC、准确性、敏感度、特异度、阳性预测值、阴性预测值分别为0.78[95%CI(0.74,0.82)]、79%、76%、80%、78%、81%；在验证组分别为0.72[95%CI(0.68,0.75)]、71%、69%、69%、74%、74%。联合模型在训练组的AUC、准确性、敏感度、特异度、阳性预测值、阴性预测值分别为0.87[95%CI(0.83,0.90)]、87%、85%、87%、87%、88%；在验证组分别为0.81[95%CI(0.78,0.83)]、80%、83%、86%、73%、79%。结论 超声机器学习预测隐匿性甲状腺乳头状癌患者颈部淋巴结转移具有较好的预测效能,结合临床特征建立的联合预测模型效能更佳。 |
| 关键词: 超声检查 机器学习 甲状腺乳头状癌 淋巴结转移 |
| DOI： |
| 投稿时间：2023-09-02修订日期：2024-02-19 |
| 基金项目:河南省医学科技攻关计划联合共建项目(编号2018020416) |
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| Ultrasound-based machine learning for early prediction of lymph node metastasis in occult papillary thyroid carcinoma |
| YU Yihang,NIU Yaning,ZHANG Mengli,FAN Zhina,MA Bingxin,LI Chuang,LI Guoqing,WU Gang |
| (Henan University People''''s Hospital,Department of Ultrasound,Henan Provincial People''s Hospital,Henan University People''s Hospital,Department of Thyroid Surgery,Henan Provincial People''s Hospital) |
| Abstract: |
| Objectives To evaluate the value of Ultrasound-based machine learning and Ultrasound-based machine learning combined with clinical featuers in predicting lymph node metastasis in occult papillary thyroid carcinoma.Methods Retrospectively analyzed the clinical data and ultrasound images of 81 occult papillary thyroid carcinoma patients with lymph node metastasis and 103 patients without lymph node metastasis,and divided randomly into the training and validation groups at the ratio of 7:3. Extracted the features of ultrasound images and used Support Vector Machine SVM to build models.Furthermore, built combined model based on ultrasound featuers and clinical features.Then observe the efficacy of each model.Results 11 ultrasound features and 2 clinical featuers were ultimately selected to build machine learning models and combined models.The AUC、accuracy、sensitivity、specificity、positive predictive value and negative predictive value in the training group of machine learning model was 0.78[95%CI(0.74,0.82)]、79%、76%、80%、78%、81%,while in the validation group was 0.72[95%CI(0.68,0.75)]、71%、69%、69%、74%、74%.The AUC、accuracy、sensitivity、specificity、positive predictive value and negative predictive value in the training group of combined model was 0.87[95%CI(0.83,0.90)]、87%、85%、87%、87%、88%,while in the validation group was 0.81[95%CI(0.78,0.83)]、80%、83%、86%、73%、79%.Conclusions Ultrasound-based machine learning was effective at predicting the lymph node metastasis of occult papillary thyroid carcinoma patients,and the combined model achieved a much higher diagnostic accuracy. |
| Key words: Ultrasonography machine learning papillary thyroid carcinoma lymph node metastasis |
