Zexin Li1, Kaiji Yang2, Lili Zhang1, Chiju Wei3, Peixuan Yang1, Wencan Xu4. 1. Health Care Center, The First Affiliated Hospital of Shantou University Medical College, No. 57, Changping Road, Shantou 515041, China. 2. Department of Radiology, The First Affiliated Hospital of Shantou University Medical College, No. 57, Changping Road, Shantou 515041, China. 3. Multidisciplinary Research Center, Shantou University, No. 243, Daxue Road, Shantou 515063, China. 4. Department of Endocrinology, The First Affiliated Hospital of Shantou University Medical College, No. 57, Changping Road, Shantou 515041, China.
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.
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.
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