Bayesian Assessment of Diagnostic Strategy for a Thyroid Nodule Involving a Combination of Clinical Synthetic Features and Molecular Data

Abstract

The use of machine learning has increased over the years, especially in the world of molecular data. Generally, the inference of relationships between features is determined by statistical models. The phenotype (observable clinical characteristics) can result from the expression of the genotype (genetic code) or environmental factors. Molecular datasets have limited information, while supporting clinical data is ambiguous. There are no well-established approaches for combining clinical information with genomic repositories. The genomic tests that are available only use molecular data and give physicians a result which can be integrated clinically. In this article, we present the strategy where clinical data, regardless of its limitations, is combined in one predictive model with molecular features. We predict the risk of malignancy in the thyroid nodules based on the results of fine-needle aspiration biopsy and expression of selected genes. We utilize a Bayesian network (BN) framework to discover relationships between molecular features and assess the impact of added clinical data quality on the performance of the chosen gene set. Bayesian network offering both prognostic and diagnostic perspectives is a perfect non-parametric technique for feature selection, feature extraction, and prediction purposes. We show that certain clinical factors could work as a synthetic feature and provide predictive abilities beyond what genes alone can offer. The experimental results demonstrate a higher performance of predictive models based on molecular and clinical data than when using only molecular data. We also explain why, one should consider the source of clinical data, but be aware of the quality of variables.

Keywords

• Bayesian networks
• feature integration
• synthetic features
• Markov blankets
• Quality of features
• thyroid cancer