Dataset Information
Classification of Thyroid Nodules with Stacked Denoising Sparse Autoencoder.
Ontology highlight
ABSTRACT: Purpose
Several commercial tests have been used for the classification of indeterminate thyroid nodules in cytology. However, the geographic inconvenience and high cost confine their widespread use. This study aims to develop a classifier for conveniently clinical utility.Methods
Gene expression data of thyroid nodule tissues were collected from three public databases. Immune-related genes were used to construct the classifier with stacked denoising sparse autoencoder.Results
The classifier performed well in discriminating malignant and benign thyroid nodules, with an area under the curve of 0.785 [0.638-0.931], accuracy of 92.9% [92.7-93.0%], sensitivity of 98.6% [95.9-101.3%], specificity of 58.3% [30.4-86.2%], positive likelihood ratio of 2.367 [1.211-4.625], and negative likelihood ratio of 0.024 [0.003-0.177]. In the cancer prevalence range of 20-40% for indeterminate thyroid nodules in cytology, the range of negative predictive value of this classifier was 37-61%, and the range of positive predictive value was 98-99%.Conclusion
The classifier developed in this study has the superb discriminative ability for thyroid nodules. However, it needs validation in cytologically indeterminate thyroid nodules before clinical use.
SUBMITTER: Li Z
PROVIDER: S-EPMC7787836 | biostudies-literature | 2020
REPOSITORIES: biostudies-literature
Publications
Classification of Thyroid Nodules with Stacked Denoising Sparse Autoencoder.
International journal of endocrinology 20201207
<h4>Purpose</h4>Several commercial tests have been used for the classification of indeterminate thyroid nodules in cytology. However, the geographic inconvenience and high cost confine their widespread use. This study aims to develop a classifier for conveniently clinical utility.<h4>Methods</h4>Gene expression data of thyroid nodule tissues were collected from three public databases. Immune-related genes were used to construct the classifier with stacked denoising sparse autoencoder.<h4>Results ...[more]
