Molecular Classification of Thyroid Nodules Using High-Dimensionality Genomic Data
Molecular Classification of Thyroid Nodules Using High-Dimensionality Genomic Data
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DOI：
10.1210/jc.2010-1087
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发表时间：
2010-12-01
影响因子：
5.8
通讯作者：
Kennedy, Giulia C.
中科院分区：
文献类型：
--
作者：
Chudova, Darya;Wilde, Jonathan I.;Kennedy, Giulia C.
目的：我们着手开发一种分子检测方法，利用细针抽吸物（FNA）区分良性和恶性甲状腺结节。设计：我们使用mRNA表达分析来测量315个甲状腺结节中超过247，186个转录本，包括多种亚型。数据集包括178例回顾性手术组织和137例前瞻性采集的FNA样本。两个分类器分别在手术组织和FNA上进行训练。使用一个独立的48前瞻性FNA样本，其中包括50%不确定的cytopathology.Results：组织训练的分类器的性能进行了评估，显着低于在FNAs比组织。探索性分析指出，组织和FNA之间的细胞异质性差异是可能的原因。在FNA样本上训练的分类器导致性能提高，使用30倍交叉验证和独立测试集进行估计。在测试集上，阴性预测值和特异性估计分别为96%和84%，表明在考虑手术的患者的管理中具有临床实用性。使用在硅片和体外混合实验，我们证明，即使在80%的稀释度与良性背景的存在下，分类器可以正确地识别恶性肿瘤在大多数FNA samples.Conclusions：的FNA训练的分类器是能够分类一个独立的一组FNAs中发生了大量的RNA降解，并在血液的存在下。对稀释的高耐受性使得分类器在常规临床设置中有用，其中采样误差可能是一个问题。一项正在进行的多中心临床试验将使我们能够在一个更大的前瞻性收集的甲状腺FNA独立测试集上验证分子测试性能。（临床内分泌代谢杂志95：5296 - 5304，2010）
Objective: We set out to develop a molecular test that distinguishes benign and malignant thyroid nodules using fine-needle aspirates (FNA).Design: We used mRNA expression analysis to measure more than 247,186 transcripts in 315 thyroid nodules, comprising multiple subtypes. The data set consisted of 178 retrospective surgical tissues and 137 prospectively collected FNA samples. Two classifiers were trained separately on surgical tissues and FNAs. The performance was evaluated using an independent set of 48 prospective FNA samples, which included 50% with indeterminate cytopathology.Results: Performance of the tissue-trained classifier was markedly lower in FNAs than in tissue. Exploratory analysis pointed to differences in cellular heterogeneity between tissues and FNAs as the likely cause. The classifier trained on FNA samples resulted in increased performance, estimated using both 30-fold cross-validation and an independent test set. On the test set, negative predictive value and specificity were estimated to be 96 and 84%, respectively, suggesting clinical utility in the management of patients considering surgery. Using in silico and in vitro mixing experiments, we demonstrated that even in the presence of 80% dilution with benign background, the classifier can correctly recognize malignancy in the majority of FNA samples.Conclusions: The FNA-trained classifier was able to classify an independent set of FNAs in which substantial RNA degradation had occurred and in the presence of blood. High tolerance to dilution makes the classifier useful in routine clinical settings where sampling error may be a concern. An ongoing multicenter clinical trial will allow us to validate molecular test performance on a larger independent test set of prospectively collected thyroid FNAs. (J Clin Endocrinol Metab 95: 5296-5304, 2010)