# A predictive nomogram of thyroid nodules based on deep learning ultrasound image analysis

## TL;DR

This study creates a new nomogram model combining deep learning and ultrasound features to better predict if thyroid nodules are benign or malignant.

## Contribution

A novel nomogram model integrating deep learning predictions and clinical ultrasound features for thyroid nodule classification.

## Key findings

- The deep learning model achieved 0.886 accuracy in predicting thyroid nodule malignancy.
- The nomogram model outperformed C-TIRADS and deep learning models alone in accuracy and clinical utility.
- Age, echogenic foci, and deep learning predicted values were identified as key independent predictors.

## Abstract

The ultrasound characteristics of benign and malignant thyroid nodules were compared to develop a deep learning model, aiming to establish a nomogram model based on deep learning ultrasound image analysis to improve the predictive performance of thyroid nodules.

This retrospective study analyzed the clinical and ultrasound characteristics of 2247 thyroid nodules from March 2016 to October 2023. Among them, 1573 nodules were used for training and testing the deep learning models, and 674 nodules were used for validation, and the deep learning predicted values were obtained. These 674 nodules were randomly divided into a training set and a validation set in a 7:3 ratio to construct a nomogram model.

The accuracy of the deep learning model in 674 thyroid nodules was 0.886, with a precision of 0.900, a recall rate of 0.889, and an F1-score of 0.895. The binary logistic analysis of the training set revealed that age, echogenic foci, and deep learning predicted values were statistically significant (P<0.05). These three indicators were used to construct the nomogram model, showing higher accuracy compared to the China thyroid imaging reports and data systems (C-TIRADS) classification and deep learning models. Moreover, the nomogram model exhibited high calibration and clinical benefits.

Age, deep learning predicted values, and echogenic foci can be used as independent predictive factors to distinguish between benign and malignant thyroid nodules. The nomogram integrates deep learning and patient clinical ultrasound characteristics, yielding higher accuracy than the application of C-TIRADS or deep learning models alone.