Dynamic contrast-enhanced MRI model selection for predicting tumor aggressiveness in papillary thyroid cancers.
Dynamic contrast-enhanced MRI model selection for predicting tumor aggressiveness in papillary thyroid cancers.
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DOI：
10.1002/nbm.4166
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发表时间：
2020-01
影响因子：
2.9
通讯作者：
Shukla-Dave A
中科院分区：
文献类型：
--
作者：
Paudyal R;Lu Y;Hatzoglou V;Moreira A;Stambuk HE;Oh JH;Cunanan KM;Aramburu Nunez D;Mazaheri Y;Gonen M;Ho A;Fagin JA;Wong RJ;Shaha A;Tuttle RM;Shukla-Dave A
本研究的目的是从动态对比增强T1加权磁共振成像(DCE-MRI)数据中确定最佳示踪剂动力学模型，并评估根据最佳模型估计的参数是否能预测甲状腺乳头状癌(PTC)患者术前组织病理学确定的肿瘤侵袭性。在这项前瞻性研究中，18名PTC患者在甲状腺切除术前接受了3T磁共振扫描仪的DCE-MRI检查。这项研究得到了机构审查委员会的批准，并获得了所有患者的知情同意。在体素基础上比较两室交换模型(2CXM)、间隔组织摄取模型(CTUM)、扩展Tofts模型(ETM)和标准Tofts模型(TM)，以确定使用修正的Akaike信息标准(AICC)的PTC的最佳模型。最优模型是AICC最低的模型。统计分析包括配对和非配对t检验以及单因素方差分析。多重比较采用Bonferroni校正。根据最佳模型参数生成受试者工作特征(ROC)曲线，以区分有无侵袭性特征的PTC，并比较AUC。在四个模型中，ETM的表现最好，AICC最低，权重最高(0.44)。在所有3419个体素中，44%的人倾向于ETM。有侵袭性甲状腺外侵犯的PTC患者的−-1显著高于无侵袭性的PTC患者(0.78±0.29vs.0.34±0.18min，P=0.005)。根据ROC分析，分别在0.45min−1、0.28和0.014确定了区分有无ETE的PTC的KTRANS、Ve和Vp的界值。敏感度和特异度分别为86%和82%，71%和82%，86%和55%。他们各自的AUC分别为0.90、0.71和0.71。我们得出结论，ETM KTRANS对PTC患者中有侵袭性ETE和无侵袭性ETE的肿瘤进行分类具有潜力。
The purpose of this study was to identify the optimal tracer kinetic model from dynamic contrast-enhanced T1‐weighted magnetic resonance imaging (DCE-MRI) data and evaluate whether parameters estimated from the optimal model predict tumor aggressiveness determined from histopathology in patients with papillary thyroid carcinoma (PTC) prior to surgery. In this prospective study, 18 PTC patients underwent pretreatment DCE-MRI on a 3T MR scanner prior to thyroidectomy. This study was approved by the institutional review board and informed consent was obtained from all patients. The two-compartment exchange model (2CXM), compartmental tissue uptake model (CTUM), extended Tofts model (ETM), and standard Tofts model (TM) were compared on a voxel-wise basis to determine the optimal model using the corrected Akaike information criterion (AICc) for PTC. The optimal model is the one with the lowest AICc. Statistical analysis included paired and unpaired t-tests and a one-way Analysis of variance. Bonferroni correction was applied for multiple comparisons. Receiver operating characteristic (ROC) curves were generated from the optimal model parameters to differentiate PTC with and without aggressive features, and AUC’s were compared. ETM performed the best with the lowest AICc and highest weights (0.44) among the four models. ETM was preferred in 44% of all 3419 voxels. The ETM estimates of Ktrans in PTCs with aggressive feature extrathyroidal extension (ETE) were significantly higher than those without ETE (0.78±0.29 vs. 0.34±0.18 min−1, P=0.005). From ROC analysis, cut-off values of Ktrans, ve, and vp which discriminated between PTCs with and without ETE were determined at 0.45 min−1, 0.28, and 0.014, respectively. The sensitivities and specificities were 86% and 82% (Ktrans), 71% and 82% (ve), and 86% and 55% (vp). Their respective AUC’s were 0.90, 0.71, and 0.71. We conclude the ETM Ktrans has shown potential to classify tumors with and without aggressive ETE in patients with PTC.
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