用概率模型预测脑癌影像随时间变化，自动处理不确定性。
Probabilistic approach to longitudinal response prediction: application to radiomics from brain cancer imaging
- 构建概率模型融合基线与中期影像特征进行动态预测
- 在脑癌数据集上表现优于现有方法，且无需中间时间点数据
- 适合需要量化预测置信度的临床研究与个性化治疗决策
纵向影像分析可追踪疾病进展与治疗反应，提供治疗效果和疾病演变的动态洞察。从医学影像中提取的放射组学特征有助于研究疾病进展并实现临床结局的纵向预测。本研究提出一种概率模型用于纵向响应预测，整合基线特征与中期随访信息。该模型的概率特性天然适用于处理疾病进展预测中的内在不确定性。我们在合成场景和一个脑癌数据集上评估了所提模型，结果表明该方法在性能上具有竞争力，同时能有效控制问题维度增长，并在不依赖中间随访数据的情况下实现预测。该模型显著提升了对预测不确定性的建模能力。
原文摘要 · Abstract (English)
Longitudinal imaging analysis tracks disease progression and treatment response over time, providing dynamic insights into treatment efficacy and disease evolution. Radiomic features extracted from medical imaging can support the study of disease progression and facilitate longitudinal prediction of clinical outcomes. This study presents a probabilistic model for longitudinal response prediction, integrating baseline features with intermediate follow-ups. The probabilistic nature of the model naturally allows to handle the instrinsic uncertainty of the longitudinal prediction of disease progression. We evaluate the proposed model against state-of-the-art disease progression models in both a synthetic scenario and using a brain cancer dataset. Results demonstrate that the approach is competitive against existing methods while uniquely accounting for uncertainty and controlling the growth of problem dimensionality, eliminating the need for data from intermediate follow-ups.
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