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使用细针抽吸活检对甲状腺结节进行细胞分子分类。

Cytomolecular Classification of Thyroid Nodules Using Fine-Needle Washes Aspiration Biopsies.

机构信息

Bicocca Bioinformatics Biostatistics and Bioimaging B4 Center, Department of Medicine and Surgery, University of Milano-Bicocca, 20900 Monza, Italy.

Proteomics and Metabolomics Unit, Department of Medicine and Surgery, University of Milano-Bicocca, 20900 Monza, Italy.

出版信息

Int J Mol Sci. 2022 Apr 9;23(8):4156. doi: 10.3390/ijms23084156.

DOI:10.3390/ijms23084156
PMID:35456973
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9028391/
Abstract

Fine-needle aspiration biopsies (FNA) represent the gold standard to exclude the malignant nature of thyroid nodules. After cytomorphology, 20-30% of cases are deemed "indeterminate for malignancy" and undergo surgery. However, after thyroidectomy, 70-80% of these nodules are benign. The identification of tools for improving FNA's diagnostic performances is explored by matrix-assisted laser-desorption ionization mass spectrometry imaging (MALDI-MSI). A clinical study was conducted in order to build a classification model for the characterization of thyroid nodules on a large cohort of 240 samples, showing that MALDI-MSI can be effective in separating areas with benign/malignant cells. The model had optimal performances in the internal validation set ( = 70), with 100.0% (95% CI = 83.2-100.0%) sensitivity and 96.0% (95% CI = 86.3-99.5%) specificity. The external validation ( = 170) showed a specificity of 82.9% (95% CI = 74.3-89.5%) and a sensitivity of 43.1% (95% CI = 30.9-56.0%). The performance of the model was hampered in the presence of poor and/or noisy spectra. Consequently, restricting the evaluation to the subset of FNAs with adequate cellularity, sensitivity improved up to 76.5% (95% CI = 58.8-89.3). Results also suggest the putative role of MALDI-MSI in routine clinical triage, with a three levels diagnostic classification that accounts for an indeterminate gray zone of nodules requiring a strict follow-up.

摘要

细针穿刺活检(FNA)是排除甲状腺结节恶性性质的金标准。在细胞形态学检查后,20-30%的病例被认为是“恶性程度不确定”,需要进行手术。然而,在甲状腺切除术后,这些结节中有 70-80%是良性的。为了提高 FNA 的诊断性能,人们探索了基质辅助激光解吸电离质谱成像(MALDI-MSI)等工具。进行了一项临床研究,以构建一个基于 240 个样本的大样本队列的甲状腺结节特征分类模型,结果表明 MALDI-MSI 可以有效地分离出良性/恶性细胞区域。该模型在内部验证集(=70)中的表现最佳,灵敏度为 100.0%(95%CI=83.2-100.0%),特异性为 96.0%(95%CI=86.3-99.5%)。外部验证(=170)的特异性为 82.9%(95%CI=74.3-89.5%),灵敏度为 43.1%(95%CI=30.9-56.0%)。在存在较差和/或嘈杂谱的情况下,模型的性能受到阻碍。因此,将评估限制在具有足够细胞结构的 FNAs 子集中,灵敏度提高至 76.5%(95%CI=58.8-89.3)。结果还表明 MALDI-MSI 在常规临床分诊中的潜在作用,具有三级诊断分类,为需要严格随访的不确定的结节灰色区域提供了诊断。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0001/9028391/505a73797b9a/ijms-23-04156-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0001/9028391/9b2ec74129b9/ijms-23-04156-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0001/9028391/505a73797b9a/ijms-23-04156-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0001/9028391/9b2ec74129b9/ijms-23-04156-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0001/9028391/505a73797b9a/ijms-23-04156-g002.jpg

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