Optical biopsy of head and neck cancer using hyperspectral imaging and convolutional neural networks
Optical biopsy of head and neck cancer using hyperspectral imaging and convolutional neural networks
复制标题
DOI：
10.1117/1.jbo.24.3.036007
复制
发表时间：
2019-03-01
影响因子：
3.5
通讯作者：
Fei, Baowei
中科院分区：
文献类型：
--
作者：
Hahoek, Martin;Little, James, V;Fei, Baowei
对于接受鳞状细胞癌(SCCA)手术切除的患者来说，无癌的手术切缘对于良好的预后至关重要。我们开发了一种方法，使用高光谱成像(HSI)，一种非接触式光学成像方式，和卷积神经网络(CNN)来执行光学活检的体外，手术大体组织标本，收集从21例手术癌症切除。使用来自不同患者的数据的交叉验证范例，CNN可以区分SCCA和正常的呼吸道组织，接收器操作曲线(AUC)下的面积(AUC)为0.82。此外，来自上呼吸道的正常组织可分为鳞状上皮、肌肉和腺体，平均AUC为0.94。通过对甲状腺组织的单独训练，CNN可以区分甲状腺癌和正常甲状腺，AUC为0.95，准确率为92%，灵敏度为92%，特异度为92%。此外，CNN还可以区分甲状腺髓样癌和良性多结节甲状腺肿(MNG)，AUC为0.93。经典型乳头状甲状腺癌与MNG的AUC分别为0.91。我们的初步结果表明，使用CNN的基于HSI的光学活检方法可以为正常和癌变的头颈部组织提供多类别的诊断信息，并且需要更多的患者数据来充分研究所提出的技术的潜力和可靠性。(C)作者。由SPIE在知识共享署名4.0未移植许可下发布。
For patients undergoing surgical cancer resection of squamous cell carcinoma (SCCa), cancer-free surgical margins are essential for good prognosis. We developed a method to use hyperspectral imaging (HSI), a noncontact optical imaging modality, and convolutional neural networks (CNNs) to perform an optical biopsy of ex-vivo, surgical gross-tissue specimens, collected from 21 patients undergoing surgical cancer resection. Using a cross-validation paradigm with data from different patients, the CNN can distinguish SCCa from normal aerodigestive tract tissues with an area under the receiver operator curve (AUC) of 0.82. Additionally, normal tissue from the upper aerodigestive tract can be subclassified into squamous epithelium, muscle, and gland with an average AUC of 0.94. After separately training on thyroid tissue, the CNN can differentiate between thyroid carcinoma and normal thyroid with an AUC of 0.95, 92% accuracy, 92% sensitivity, and 92% specificity. Moreover, the CNN can discriminate medullary thyroid carcinoma from benign multinodular goiter (MNG) with an AUC of 0.93. Classical-type papillary thyroid carcinoma is differentiated from MNG with an AUC of 0.91. Our preliminary results demonstrate that an HSI-based optical biopsy method using CNNs can provide multicategory diagnostic information for normal and cancerous head-and-neck tissue, and more patient data are needed to fully investigate the potential and reliability of the proposed technique. (C) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License.