Fine-needle aspiration of follicular adenoma versus parathyroid adenoma - The utility of multispectral imaging in differentiating lesions with subtle cytomorphologic differences
Fine-needle aspiration of follicular adenoma versus parathyroid adenoma - The utility of multispectral imaging in differentiating lesions with subtle cytomorphologic differences
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DOI：
10.1002/cncr.23252
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发表时间：
2008-02-25
影响因子：
3.4
通讯作者：
Rimm, David L.
中科院分区：
文献类型：
--
作者：
Mansoor, Ibrahim;Zalles, Carola;Rimm, David L.
背景。多光谱图像分析是一种新兴的工具，它利用空间和光谱图像信息对图像进行分类，可用于区分良性和恶性细胞。当前研究的目的是分析该工具在鉴别人眼无法识别的细微细胞学差异方面的能力。在此，作者使用细针穿刺滤泡腺瘤（FA）和甲状旁腺瘤（PA）作为试验病例。Nuance平台用于收集图像堆栈，随后使用CRI-MLS软件进行分析，CRI-MLS是一种基于神经网络的人工智能系统，可以使用自动“学习”的空间光谱特征对图像进行分类。CRI-MLS在训练集中随机、保存良好的FA细胞和PA细胞上进行训练（每个细胞n = 45个）。开发了一种算法解决方案，然后在由5个FA病例的1904个FA细胞和5个PA病例的690个PA细胞组成的独立系列上进行验证。来自CRI-MLS分类器的溶液显示1876个FA细胞（98.5%）为真FA， 28个FA细胞（1.5%）为假PA，而663个PA细胞（96%）为真PA， 27个PA细胞（4%）为假FA。该溶液的敏感性为98.5%，特异性为96.1%，阳性预测值为98.6%。最佳的空间光谱成像解决方案能够正确分类2594个细胞中的2534个（98%），错误分类2594个细胞中的55个（2%）。这些数据表明，该技术可能是有价值的，在临床设置，以帮助区分和分类形态相似的病变。
BACKGROUND. Multispectral image analysis is an emerging tool that utilizes both spatial and spectral image information to classify images that can be used for the differentiation between benign versus malignant cells. The aim of the current study was to analyze the ability of this tool in differentiating subtle cytologic differences that cannot be appreciated by the human eye. Herein, the authors used fine-needle aspirations (FNAs) of follicular adenoma (FA) and parathyroid adenoma (PA) as a test case.METHODS. The Nuance platform was used to collect image stacks that were subsequently analyzed with CRI-MLS software, a neural network-based artificial intelligence system that can classify images using automatically "learned" spatial-spectral features. CRI-MLS was trained on random, well-preserved FA cells and PA cells from the training set (n = 45 cells each). An algorithmic solution was developed and then validated on an independent series comprised of 1904 FA cells from 5 FA cases and 690 PA cells from 5 PA cases.RESULTS. The solution from the CRI-MLS classifier showed 1876 FA cells (98.5%) as true FA and 28 FA cells (1.5%) as false PA, whereas 663 PA cells (96%) were true PA and 27 PA cells (4%) were false FA. The summary result of this solution was a sensitivity of 98.5%, a specificity of 96.1%, and a positive predictive value of 98.6%.CONCLUSIONS. The best spatial-spectral imaging solution was able to correctly classify 2534 of 2594 cells (98%) and misclassified only 55 of 2594 cells (2%). These data suggest that this technology may be valuable in a clinical setting to help differentiate and classify morphologically similar lesions.