1214P - Imaging AI prognosis of early stage lung cancer using CT radiomics

Background

Accurate prediction of lung cancer recurrence risk is crucial for treatment decisions and follow-up, particularly for Stage I patients who are not eligible for (neo-)adjuvant therapy but approximately one-third still recur after surgical resection. We present a machine learning model that uses patient computed tomography (CT) images and clinical features to predict lung cancer recurrence.

Methods

A dataset of 968 clinical stage I-III lung cancer patients who underwent surgical resection was gathered from the US National Lung Screening Trial, the North Estonia Medical Centre, the Stanford University School of Medicine and Palo Alto Veterans Affairs Healthcare System. 313/968 had lung cancer recurrence, of which 221/776 were Stage 1. The pre-operative survival model was trained to predict the likelihood of recurrence at each time-point using radiomic features extracted from CT images and relevant clinical variables. An 8-fold cross-validation strategy was used and performance evaluated using the time-dependent Area-Under-the-ROC-Curve (AUC), disease-free survival (DFS), hazard ratio (HR) and log-rank test against clinical staging alone.

Results

The ML survival model was better able to stratify patients into high and low-risk (HR=2.7, p

Conclusions

The ML survival model outperforms clinical staging in patient risk-stratification and time-dependent lung cancer recurrence prediction. With further development, this algorithm could prove a valuable tool to aid the management of lung cancer patients.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

The authors.

Funding

Optellum.

Disclosure

A. Gasimova, B. Heames, N. Waterfield Price: Financial Interests, Institutional, Other, Employee: Optellum. G. Hodgkinson: Financial Interests, Institutional, Other, Employee: Medtronic. L. Freitag: Financial Interests, Institutional, Advisory Board: Optellum. All other authors have declared no conflicts of interest.