使用时间变分自动编码器预测癌症诱发的骨溶解。

IF 5 Q1 ENGINEERING, BIOMEDICAL BME frontiers Pub Date : 2022-04-02 eCollection Date: 2022-01-01 DOI:10.34133/2022/9763284
Wei Xiong, Neil Yeung, Shubo Wang, Haofu Liao, Liyun Wang, Jiebo Luo
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引用次数: 2

摘要

目标和影响声明。我们采用深度学习模型对小鼠乳腺癌症骨转移的计算机断层扫描(CT)图像进行骨溶解预测。给定先前时间步骤的骨CT扫描,该模型结合了从序列图像中学习到的骨癌相互作用,并生成未来的CT图像。它预测癌症侵袭骨中骨病变发展的能力可以帮助评估即将发生骨折的风险,并选择乳腺癌症骨转移的正确治疗方法。介绍癌症通常转移到骨骼,引起溶骨性病变,并导致骨骼相关事件(SRE),包括剧烈疼痛甚至致命骨折。尽管目前的成像技术可以检测宏观骨损伤,但预测骨损伤的发生和进展仍然是一个挑战。方法。我们采用了一种时间变分自动编码器(T-VAE)模型,该模型利用变分自动编码和长短期记忆网络的组合,在包含小鼠胫骨序列图像的微CT数据集上预测骨损伤的出现。考虑到小鼠胫骨在早期几周的CT扫描,我们的模型可以从数据中了解它们未来状态的分布。后果在骨损伤进展预测任务中,我们将我们的模型与其他基于深度学习的预测模型进行了比较。在各种评估指标下,我们的模型比现有模型产生了更准确的预测。结论我们开发了一个深度学习框架,可以准确预测和可视化溶骨性骨病变的进展。它将有助于规划和评估预防癌症患者SRE的治疗策略。
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Breast Cancer Induced Bone Osteolysis Prediction Using Temporal Variational Autoencoders.

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 autoencoder (T-VAE) model that utilizes a combination of variational autoencoders 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.

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审稿时长
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