Breast Cancer Induced Bone Osteolysis Prediction Using Temporal Variational Auto-Encoders

TL;DR

This paper presents a deep learning model using temporal variational auto-encoders to predict the progression of bone osteolytic lesions in breast cancer metastasis from sequential CT images, aiding treatment planning.

Contribution

The study introduces a novel T-VAE model that effectively predicts future bone lesion development from sequential CT scans, outperforming existing models.

Findings

The T-VAE model achieves higher accuracy in predicting bone lesion progression.

It outperforms other deep learning models on the micro-CT dataset.

The model can visualize lesion development to assist clinical decisions.

Abstract

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.