Brown Anna M, Nagala Sidhartha, McLean Mary A, Lu Yonggang, Scoffings Daniel, Apte Aditya, Gonen Mithat, Stambuk Hilda E, Shaha Ashok R, Tuttle R Michael, Deasy Joseph O, Priest Andrew N, Jani Piyush, Shukla-Dave Amita, Griffiths John
Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge, United Kingdom.
Duke University School of Medicine, Durham, North Carolina, USA.
Magn Reson Med. 2016 Apr;75(4):1708-16. doi: 10.1002/mrm.25743. Epub 2015 May 20.
Ultrasound-guided fine needle aspirate cytology fails to diagnose many malignant thyroid nodules; consequently, patients may undergo diagnostic lobectomy. This study assessed whether textural analysis (TA) could noninvasively stratify thyroid nodules accurately using diffusion-weighted MRI (DW-MRI).
This multi-institutional study examined 3T DW-MRI images obtained with spin echo echo planar imaging sequences. The training data set included 26 patients from Cambridge, United Kingdom, and the test data set included 18 thyroid cancer patients from Memorial Sloan Kettering Cancer Center (New York, New York, USA). Apparent diffusion coefficients (ADCs) were compared over regions of interest (ROIs) defined on thyroid nodules. TA, linear discriminant analysis (LDA), and feature reduction were performed using the 21 MaZda-generated texture parameters that best distinguished benign and malignant ROIs.
Training data set mean ADC values were significantly different for benign and malignant nodules (P = 0.02) with a sensitivity and specificity of 70% and 63%, respectively, and a receiver operator characteristic (ROC) area under the curve (AUC) of 0.73. The LDA model of the top 21 textural features correctly classified 89/94 DW-MRI ROIs with 92% sensitivity, 96% specificity, and an AUC of 0.97. This algorithm correctly classified 16/18 (89%) patients in the independently obtained test set of thyroid DW-MRI scans.
TA classifies thyroid nodules with high sensitivity and specificity on multi-institutional DW-MRI data sets. This method requires further validation in a larger prospective study. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance.
超声引导下细针穿刺抽吸细胞学检查无法诊断许多恶性甲状腺结节；因此，患者可能需要接受诊断性肺叶切除术。本研究评估了纹理分析（TA）能否使用扩散加权磁共振成像（DW-MRI）对甲状腺结节进行准确的无创分层。
这项多机构研究检查了通过自旋回波平面回波成像序列获得的3T DW-MRI图像。训练数据集包括来自英国剑桥的26名患者，测试数据集包括来自美国纽约纪念斯隆凯特琳癌症中心的18名甲状腺癌患者。比较了在甲状腺结节上定义的感兴趣区域（ROI）的表观扩散系数（ADC）。使用21个由MaZda生成的能最佳区分良性和恶性ROI的纹理参数进行TA、线性判别分析（LDA）和特征约简。
训练数据集中良性和恶性结节的平均ADC值有显著差异（P = 0.02），敏感性和特异性分别为70%和63%，曲线下面积（AUC）为0.73。前21个纹理特征的LDA模型正确分类了94个DW-MRI ROI中的89个，敏感性为92%，特异性为96%，AUC为0.97。该算法在独立获得的甲状腺DW-MRI扫描测试集中正确分类了16/18（89%）的患者。
TA在多机构DW-MRI数据集上对甲状腺结节进行分类具有高敏感性和特异性。该方法需要在更大规模的前瞻性研究中进一步验证。《医学磁共振》由威利期刊公司代表国际磁共振学会出版。
