  Objective and Impact Statement. We adopt a deep learning model for bone
osteolysis prediction on computed tomography (CT) images of murine breast
cancer bone metastases. Given the bone CT scans at previous time steps, the
model incorporates the bone-cancer interactions learned from the sequential
images and generates future CT images. Its ability of predicting the
development of bone lesions in cancer-invading bones can assist in assessing
the risk of impending fractures and choosing proper treatments in breast cancer
bone metastasis. Introduction. Breast cancer often metastasizes to bone, causes
osteolytic lesions, and results in skeletal related events (SREs) including
severe pain and even fatal fractures. Although current imaging techniques can
detect macroscopic bone lesions, predicting the occurrence and progression of
bone lesions remains a challenge. Methods. We adopt a temporal variational
auto-encoder (T-VAE) model that utilizes a combination of variational
auto-encoders and long short-term memory networks to predict bone lesion
emergence on our micro-CT dataset containing sequential images of murine
tibiae. Given the CT scans of murine tibiae at early weeks, our model can learn
the distribution of their future states from data. Results. We test our model
against other deep learning-based prediction models on the bone lesion
progression prediction task. Our model produces much more accurate predictions
than existing models under various evaluation metrics. Conclusion. We develop a
deep learning framework that can accurately predict and visualize the
progression of osteolytic bone lesions. It will assist in planning and
evaluating treatment strategies to prevent SREs in breast cancer patients.
