What question did this study set out to answer?

The aim is to identify risk factors linked to thyroid nodule occurrence and growth in euthyroid individuals and develop a predictive model using machine learning.

March 18, 2026Open Access

Nine-year retrospective study on risk factors and a machine learning-assisted model for predicting occurrence and growth of thyroid nodules in Euthyroid individuals

Key Points

The aim is to identify risk factors linked to thyroid nodule occurrence and growth in euthyroid individuals and develop a predictive model using machine learning.
Analyzed a nine-year retrospective cohort of 1140 euthyroid participants.
Applied LASSO regression to select key predictors from 17 variables.
Developed a LASSO-Cox predictive model incorporating chosen predictors.
Assessed model performance using AUC, concordance index, and calibration plots.
Identified 10 variables linked to thyroid nodule occurrence and enlargement.
Sex, systolic blood pressure, and BMI were significant predictors for occurrence.
Sex, HbA1c, and metabolic syndrome were linked to nodule enlargement.
Achieved AUCs of 0.65 for occurrence and 0.64 for enlargement.

Abstract

Objective This study aimed to identify risk factors for the occurrence and growth of thyroid nodules (TNs) in euthyroid individuals, and to develop a machine learning-assisted predictive model (least absolute shrinkage and selection operator (LASSO) Cox regression) for preliminary individualized risk stratification. Methods A nine-year retrospective cohort of 1140 participants with normal thyroid function (6444 data points) was analyzed. First, LASSO regression—a classic regularization algorithm in machine learning—was applied to screen key predictors from 17 candidate variables. The selected variables were then incorporated into multivariable Cox proportional hazards models to construct a LASSO-Cox predictive model. A nomogram was developed, and model performance was assessed using the area under the curve (AUC), bootstrap-corrected concordance index, calibration plots, and decision curve analysis (DCA). Results Machine learning-based LASSO regression screened 10 variables for TN occurrence (age, sex, systolic blood pressure (SBP), diastolic blood pressure (DBP), waist circumference (WC), body mass index (BMI), fasting plasma glucose (FPG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), metabolic syndrome (MetS)) and 10 variables for TN enlargement (age, sex, SBP, BMI, hemoglobin A1c (HbA1c), TC, triglycerides, HDL-C, uric acid, MetS). Cox analysis further confirmed that sex, SBP, DBP, WC, BMI, FPG, HDL-C, and MetS were predictors of TN occurrence, while sex, HbA1c, HDL-C, and MetS were associated with TN enlargement. The LASSO-Cox nomogram achieved AUCs of 0.65 for occurrence and 0.64 for enlargement, with bootstrap-corrected C-indices of 0.62 and 0.64. Calibration showed good agreement between predicted and observed risks, and DCA demonstrated consistent net benefit across threshold probabilities of 0.05–0.6. Conclusion Sex, HDL-C, and MetS are key predictors of TN occurrence and enlargement. The machine learning-assisted LASSO-Cox nomogram, based on routine clinical indicators, shows moderate discrimination and good calibration. This preliminary tool may assist in population-level risk stratification; however, its modest discrimination limits its immediate clinical implementation.

Nine-year retrospective study on risk factors and a machine learning-assisted model for predicting occurrence and growth of thyroid nodules in Euthyroid individuals

Key Points

Abstract

Cite This Study