Machine Learning Models to Predict Bone Metastasis Risk in Patients With Lung Cancer

Abstract

Introduction

The aim of this study was to find the most appropriate variables to input into machine learning algorithms to identify those patients with primary lung malignancy with high risk for metastasis to the bone.

Patient Inclusion

Patients with either histological or radiological diagnoses of lung cancer were included in this study.

Results

The patient cohort comprised 1864 patients diagnosed from 2016 to 2021. A total of 25 variables were considered as potential risk factors. These variables have been identified in previous studies as independent risk factors for bone metastasis. Treatment methods for lung cancer were taken into account during model development. The outcome variable was binary, (presence or absence of bone metastasis) with follow-up until death or 12-month survival, whichever is the sooner. Results showed that American Joint Committee on Cancer staging, the use of EGFR inhibitor, age, T-staging, and lymphovascular invasion were the five input features contributing the most to the model algorithm. High AJCC staging (OR 1.98; p < 0.05), the use of EGFR inhibitor (OR 6.14; p < 0.05), high T-staging (OR 1.47; p < 0.05), and the presence of lymphovascular invasion (OR 4.92; p < 0.05) increase predicted risk of bone metastasis. Conversely, older age reduces predicted bone metastasis risk (OR 0.98; p < 0.05).

Conclusion

The machine learning model developed in this study can be easily incorporated into the hospital's Clinical Management System so that input variables can be immediately utilized to give an accurate prediction of bone metastatic risk, therefore informing clinicians on the best treatment strategy for that individual patient.