Magnetic resonance imaging (MRI) radiomics of papillary thyroid cancer (PTC): a comparison of predictive performance of multiple classifiers modeling to identify cervical lymph node metastases before surgery
Magnetic resonance imaging (MRI) radiomics of papillary thyroid cancer (PTC): a comparison of predictive performance of multiple classifiers modeling to identify cervical lymph node metastases before surgery
复制标题
甲状腺乳头状癌 (PTC) 的磁共振成像 (MRI) 放射组学：手术前识别颈部淋巴结转移的多个分类器模型的预测性能比较
DOI：
10.1007/s11547-021-01393-1
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发表时间：
2021-07-08
影响因子：
8.9
通讯作者：
Yang, Hong
中科院分区：
文献类型：
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作者：
Qin, Hui;Que, Qiao;Yang, Hong
目的比较多分类器模型预测甲状腺乳头状癌（PTC）淋巴结转移的效果，建立PTC术前MRI放射组学联合模型，对109例PTC患者（其中有淋巴结转移者77例，无淋巴结转移者32例）术前MRI扫描资料进行回顾性分析。将入组病例分为训练组和验证组。从脂肪抑制的T2加权MRI图像中选择放射组学特征，通过斯皮尔曼相关性检验、假设检验和随机森林方法确定最佳特征，然后由8个分类器构建8个预测模型。结果基于MRI纹理的肉眼诊断LN的ROC曲线下面积（AUC）为0.739（敏感性= 0.571，特异性= 0.906）。基于5个最优特征，Logistic回归分类器的MRI放射组学模型的最佳AUC在训练组和验证组中分别为0.805和0.760，并且最佳放射组学模型与MRI纹理视觉诊断的组合具有较高的AUC，为0.969结论放射组学与视觉诊断相结合的模型可作为临床干预前预测PTC患者淋巴结转移的有效方法。
PurposeTo compare predictive efficiency of multiple classifiers modeling and establish a combined magnetic resonance imaging (MRI) radiomics model for identifying lymph node (LN) metastases of papillary thyroid cancer (PTC) preoperatively.Materials and methodsA retrospective analysis based on the preoperative MRI scans of 109 PTC patients including 77 patients with LN metastases and 32 patients without metastases was conducted, and we divided enroll cases into trained group and validation group. Radiomics signatures were selected from fat-suppressed T2-weighted MRI images, and the optimal characteristics were confirmed by spearman correlation test, hypothesis testing and random forest methods, and then, eight predictive models were constructed by eight classifiers. The receiver operating characteristic (ROC) curves analysis were performed to demonstrate the effectiveness of the models.ResultsThe area under the curve (AUC) of ROC based on MRI texture diagnosed LN status by naked eye was 0.739 (sensitivity = 0.571, specificity = 0.906). Based on the 5 optimal signatures, the best AUC of MRI radiomics model by logistics regression classifier had a considerable prediction performance with AUCs 0.805 in trained group and 0.760 in validation group, respectively, and a combination of best radiomics model with visual diagnosis of MRI texture had a high AUC as 0.969 (sensitivity = 0.938, specificity = 1.000), suggesting combined model had a preferable diagnostic efficiency in evaluating LN metastases of PTC.ConclusionOur combined radiomics model with visual diagnosis could be a potentially effective strategy to preoperatively predict LN metastases in PTC patients before clinical intervention.