In a study involving assessment of over 1,000 thyroid nodules, researchers found the machine learning model led to substantial increases in sensitivity and specificity for estimating the risk of thyroid malignancy over traditional TI-RADS and guidelines from the American Thyroid Association(ATA).
The researchers found that the XGBoost machine learning algorithm provided an 88.3 percent area under the receiver operating characteristic curve (AUC) in contrast to 54.2 percent for TI-RADS and 44.3 percent for the ATA guidelines. The XGBoost machine learning algorithm was associated with a significant reduction of unnecessary fine-needle aspiration (FNA) rate (7 percent).
Family history, the presence of pathological lymph nodes and a history of head and neck irradiation were noted as key factors in predicting malignancy risk, according to the study authors. However, the researchers noted that none of these factors are utilized in the TI-RADS system or ATA guidelines.
The combination of clinical and demographic features, with ultrasound and FNA cytology significantly enhances the model’s performance and emphasizes their combined value in ML-based risk assessment tools.
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