# Preliminary exploratory study on differential diagnosis between benign and malignant peripheral lung tumors: based on deep learning networks

## TL;DR

This study shows that a deep learning model using ultrasound images can accurately tell the difference between benign and malignant lung tumors, with the ResNet18 model performing best.

## Contribution

The study introduces a deep learning model using ultrasound imaging for differentiating benign and malignant lung tumors, showing superior performance with ResNet18.

## Key findings

- The ResNet18 model outperformed other deep learning models in distinguishing benign and malignant lung tumors.
- ResNet18 showed statistically significant improvements in predictive accuracy and reclassification ability compared to other models.
- The model provides a non-invasive and effective tool for early detection of lung cancer.

## Abstract

Deep learning has shown considerable promise in the differential diagnosis of lung lesions. However, the majority of previous studies have focused primarily on X-ray, computed tomography (CT), and magnetic resonance imaging (MRI), with relatively few investigations exploring the predictive value of ultrasound imaging.

This study aims to develop a deep learning model based on ultrasound imaging to differentiate between benign and malignant peripheral lung tumors.

A retrospective analysis was conducted on a cohort of 371 patients who underwent ultrasound-guided percutaneous lung tumor procedures across two centers. The dataset was divided into a training set (n = 296) and a test set (n = 75) in an 8:2 ratio for further analysis and model evaluation. Five distinct deep learning models were developed using ResNet152, ResNet101, ResNet50, ResNet34, and ResNet18 algorithms. Receiver Operating Characteristic (ROC) curves were generated, and the Area Under the Curve (AUC) was calculated to assess the diagnostic performance of each model. DeLong’s test was employed to compare the differences between the groups.

Among the five models, the one based on the ResNet18 algorithm demonstrated the highest performance. It exhibited statistically significant advantages in predictive accuracy (p < 0.05) compared to the models based on ResNet152, ResNet101, ResNet50, and ResNet34 algorithms. Specifically, the ResNet18 model showed superior discriminatory power. Quantitative evaluation through Net Reclassification Improvement (NRI) analysis revealed that the NRI values for the ResNet18 model, when compared with ResNet152, ResNet101, ResNet50, and ResNet34, were 0.180, 0.240, 0.186, and 0.221, respectively. All corresponding p-values were less than 0.05 (p < 0.05 for each comparison), further confirming that the ResNet18 model significantly outperformed the other four models in reclassification ability. Moreover, its predictive outcomes led to marked improvements in risk stratification and classification accuracy.

The ResNet18-based deep learning model demonstrated superior accuracy in distinguishing between benign and malignant peripheral lung tumors, providing an effective and non-invasive tool for the early detection of lung cancer.