用影像动态轨迹预测乳腺癌新辅助化疗疗效，提升个体化治疗决策
Temporal Representation Learning of Phenotype Trajectories for pCR Prediction in Breast Cancer
- 从早期MRI影像构建患者疾病响应的时序表征，捕捉治疗动态变化
- 融合多时间点数据后，预测准确率提升至86.1%，优于仅用初始数据
- 适合关注精准医疗与肿瘤影像分析的研究者与临床医生
有效的治疗决策依赖于对个体治疗反应的准确预测。由于疾病进展和治疗反应在患者间差异显著，这一任务极具挑战性。本文提出从乳腺癌新辅助化疗（NACT）患者的影像数据中学习早期治疗反应的时序表征，以预测病理完全缓解（pCR）。利用磁共振成像（MRI）的纵向变化在潜在空间形成轨迹，作为预测依据。所提出的多任务模型能表征影像外观、保持时序连续性，并应对非应答者群体中较高的异质性。在公开的ISPY-2数据集上，仅使用基线数据（T0）时线性分类器的平衡准确率为0.761；加入早期响应数据（T0 + T1）后提升至0.811；使用四个时间点（T0 → T3）数据时达到0.861。代码将在论文接收后公开。
原文摘要 · Abstract (English)
Effective therapy decisions require models that predict the individual response to treatment. This is challenging since the progression of disease and response to treatment vary substantially across patients. Here, we propose to learn a representation of the early dynamics of treatment response from imaging data to predict pathological complete response (pCR) in breast cancer patients undergoing neoadjuvant chemotherapy (NACT). The longitudinal change in magnetic resonance imaging (MRI) data of the breast forms trajectories in the latent space, serving as basis for prediction of successful response. The multi-task model represents appearance, fosters temporal continuity and accounts for the comparably high heterogeneity in the non-responder cohort.In experiments on the publicly available ISPY-2 dataset, a linear classifier in the latent trajectory space achieves a balanced accuracy of 0.761 using only pre-treatment data (T0), 0.811 using early response (T0 + T1), and 0.861 using four imaging time points (T0 -> T3). The code will be made available upon paper acceptance.
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