# AI-based prediction of recurrence after carbon ion radiotherapy for early stage non-small cell lung cancer

## TL;DR

This study uses AI to predict which early stage lung cancer patients are at high risk of recurrence after carbon ion radiotherapy, using clinical and imaging data.

## Contribution

The novel contribution is the development of an AI model specifically for predicting recurrence after carbon ion radiotherapy in non-small cell lung cancer.

## Key findings

- The AI model achieved a median AUC of 0.762 in predicting recurrence within 2 years of CIRT.
- The three-axis plane method for CT image input was more predictive than other imaging methods.
- Gradient-weighted class activation mapping improved the model's interpretability.

## Abstract

Lung cancer is a leading cause of cancer-related deaths. Carbon ion radiotherapy (CIRT) is a treatment modality for patients with inoperable conditions or who decline surgery, but there is room for research to identify patients at high risk of recurrence. The use of artificial intelligence (AI)-based predictive models in healthcare is growing, yet their application in predicting outcomes after CIRT in NSCLC remains unexplored. This study developed an AI prediction model using clinical and imaging data to identify patients at high risk of recurrence after CIRT for early stage NSCLC. Patients with untreated early stage peripheral NSCLC undergoing CIRT between June 2010 and December 2020 were included. Simulated computed tomography (CT) images and clinical data were used to develop a model to predict recurrence within 2 years of CIRT. The model was tested using 5-fold cross-validation and evaluated using receiver operating characteristic (ROC) analysis. The study involved 124 patients. Two-year overall survival, local control, and progression-free survival rates stood at 90.8%, 91.0%, and 69.4%, respectively. The three-axis plane method for CT image input was more predictive than the three-transverse plane or 3D methods. Our AI-based model using CT images and clinical data predicted recurrence within 2 years of CIRT with a median area under the ROC curve of 0.762. Gradient-weighted class activation mapping enhanced model interpretability. The multimodal AI-based model identifies early stage NSCLC patients at high recurrence risk after CIRT although external validation is required for its generalizability and robustness.