Improving The Diagnosis of Thyroid Cancer by Machine Learning and Clinical Data

TL;DR

This paper presents a machine learning framework that improves preoperative diagnosis of thyroid cancer by analyzing clinical data, outperforming human judgment in predicting malignancy with accuracy and interpretability.

Contribution

The study introduces a novel machine learning approach using a new clinical dataset to enhance thyroid cancer diagnosis accuracy before surgery.

Findings

Machine learning model outperforms human assessment in predicting malignancy.

The framework is accurate and interpretable, aiding clinical decision-making.

Validation methods confirm robustness and reliability of the model.

Abstract

Thyroid cancer is a common endocrine carcinoma that occurs in the thyroid gland. Much effort has been invested in improving its diagnosis, and thyroidectomy remains the primary treatment method. A successful operation without unnecessary side injuries relies on an accurate preoperative diagnosis. Current human assessment of thyroid nodule malignancy is prone to errors and may not guarantee an accurate preoperative diagnosis. This study proposed a machine framework to predict thyroid nodule malignancy based on a novel clinical dataset we collected. The 10-fold cross-validation, bootstrap analysis, and permutation predictor importance were applied to estimate and interpret the model performance under uncertainty. The comparison between model prediction and expert assessment shows the advantage of our framework over human judgment in predicting thyroid nodule malignancy. Our method is accurate, interpretable, and thus useable as additional evidence in the preoperative diagnosis for thyroid cancer.