Nouraei S A R, Huys Q J M, Chatrath P, Powles J, Harcourt J P
Department of Otolaryngology, Charing Cross Hospital, London, UK.
Clin Otolaryngol. 2007 Aug;32(4):248-54. doi: 10.1111/j.1365-2273.2007.01460.x.
Selecting patients with asymmetrical sensorineural hearing loss for further investigation continues to pose clinical and medicolegal challenges, given the disparity between the number of symptomatic patients, and the low incidence of vestibular schwannoma as the underlying cause. We developed and validated a diagnostic model using a generalisation of neural networks, for detecting vestibular schwannomas from clinical and audiological data, and compared its performance with six previously published clinical and audiological decision-support screening protocols.
Probabilistic complex data classification using a neural network generalization.
Tertiary referral lateral skull base and a computational neuroscience unit.
Clinical and audiometric details of 129 patients with, and as many age and sex-matched patients without vestibular schwannomas, as determined with magnetic resonance imaging.
The ability to diagnose a patient as having or not having vestibular schwannoma.
A Gaussian Process Ordinal Regression Classifier was trained and cross-validated to classify cases as 'with' or 'without' vestibular schwannoma, and its diagnostic performance was assessed using receiver operator characteristic plots. It proved possible to pre-select sensitivity and specificity, with an area under the curve of 0.8025. At 95% sensitivity, the trained system had a specificity of 56%, 30% better than audiological protocols with closest sensitivities. The sensitivities of previously-published audiological protocols ranged between 82-97%, and their specificities ranged between 15-61%.
The Gaussian Process ORdinal Regression Classifier increased the flexibility and specificity of the screening process for vestibular schwannoma when applied to a sample of matched patients with and without this condition. If applied prospectively, it could reduce the number of 'normal' magnetic resonance (MR) scans by as much as 30% without reducing detection sensitivity. Performance can be further improed through incorporating additional data domains. Current findings need to be reproduced using a larger dataset.
鉴于有症状患者数量与作为潜在病因的前庭神经鞘瘤低发病率之间的差异，选择不对称性感音神经性听力损失患者进行进一步检查仍然面临临床和法医学挑战。我们开发并验证了一种使用神经网络泛化的诊断模型，用于从临床和听力学数据中检测前庭神经鞘瘤，并将其性能与之前发表的六种临床和听力学决策支持筛查方案进行比较。
使用神经网络泛化进行概率复杂数据分类。
三级转诊侧颅底和一个计算神经科学单元。
129例经磁共振成像确定患有前庭神经鞘瘤的患者以及年龄和性别匹配的同等数量未患前庭神经鞘瘤患者的临床和听力测定细节。
诊断患者患有或未患有前庭神经鞘瘤的能力。
训练并交叉验证了高斯过程有序回归分类器，以将病例分类为“有”或“无前庭神经鞘瘤”，并使用受试者工作特征图评估其诊断性能。事实证明，可以预先选择敏感性和特异性，曲线下面积为0.8025。在95%的敏感性下，训练后的系统特异性为56%，比具有最接近敏感性的听力学方案高30%。之前发表的听力学方案的敏感性在82%至97%之间，特异性在15%至61%之间。
当应用于有和无前庭神经鞘瘤的匹配患者样本时，高斯过程有序回归分类器提高了前庭神经鞘瘤筛查过程的灵活性和特异性。如果前瞻性应用，它可以在不降低检测敏感性的情况下将“正常”磁共振（MR）扫描的数量减少多达30%。通过纳入额外的数据域，性能可以进一步提高。目前的研究结果需要使用更大的数据组进行重现。
