# Enhanced Malignancy Prediction of Small Lung Nodules in Different Populations Using Transfer Learning on Low-Dose Computed Tomography

## TL;DR

This study shows that transfer learning improves the accuracy of predicting cancer in small lung nodules across different populations using CT scans.

## Contribution

The study demonstrates that transfer learning significantly enhances malignancy prediction in diverse international datasets.

## Key findings

- Initial models showed a 15.2% to 97.9% performance drop when applied to different datasets.
- Transfer learning improved model performance by 21.1% to 159.5% across datasets.
- TL achieved high accuracy (0.86–0.91) and AUC (0.90–0.97) for malignancy prediction.

## Abstract

Background: Predicting malignancy in small lung nodules (SLNs) across diverse populations is challenging due to significant demographic and clinical variations. This study investigates whether transfer learning (TL) can improve malignancy prediction for SLNs using low-dose computed tomography across datasets from different countries. Methods: We collected two datasets: an Asian dataset (669 SLNs from Cathay General Hospital, CGH, Taiwan) and an American dataset (600 SLNs from the National Lung Screening Trial, NLST, America). Initial U-Net models for malignancy prediction were trained on each dataset, followed by the application of TL to transfer model parameters across datasets. Model performance was evaluated using accuracy, specificity, sensitivity, and the area under the receiver operating characteristic curve (AUC). Results: Significant demographic differences (p < 0.001) were observed between the CGH and NLST datasets. Initial models trained on one dataset showed a substantial performance decline of 15.2% to 97.9% when applied to the other dataset. TL enhanced model performance across datasets by 21.1% to 159.5% (p < 0.001), achieving an accuracy of 0.86–0.91, sensitivity of 0.81–0.96, specificity of 0.89–0.92, and an AUC of 0.90–0.97. Conclusions: TL enhances SLN malignancy prediction models by addressing population variations and enabling their application across diverse international datasets.