  Lung cancer is a leading cause of cancer-related deaths worldwide. The spread
of the disease from its primary site to other parts of the lungs, known as
metastasis, significantly impacts the course of treatment. Early identification
of metastatic lesions is crucial for prompt and effective treatment, but
conventional imaging techniques have limitations in detecting small metastases.
In this study, we develop a bioclinical model for predicting the spatial spread
of lung cancer's metastasis using a three-dimensional computed tomography (CT)
scan. We used a three-layer biological model of cancer spread to predict
locations with a high probability of metastasis colonization. We validated the
bioclinical model on real-world data from 10 patients, showing promising 74%
accuracy in the metastasis location prediction. Our study highlights the
potential of the combination of biophysical and ML models to advance the way
that lung cancer is diagnosed and treated, by providing a more comprehensive
understanding of the spread of the disease and informing treatment decisions.
