Classification of Thyroid Nodules with Stacked Denoising Sparse Autoencoder.

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

SUBMITTER: Li Z 

PROVIDER: S-EPMC7787836 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature