基于观测锚定的选择性同化用于胶质瘤治疗后纵向肿瘤状态代理预测

中文摘要 AI 辅助

胶质瘤患者治疗后的MRI提供了系列观测，用于更新患者特定的肿瘤状态代理估计，但多变的影像表现和轨迹使预测变得复杂。我们将预测形式化为一种观测感知的数字孪生更新，其中中间观测锚定患者特定状态。在203名患者和594个随访时间点中，预定义的无新治疗标准保留了236个候选三元组中的120个，在患者层面分为81/24/15的训练/验证/测试三元组。每个时间点由从MRI病灶标签导出的连续体素级肿瘤状态代理图表示，取值范围为[0,1]。基于SegMamba的单步预测器从多模态源状态张量预测更新提议。观测锚定选择性同化（OASA）保留观测到的中间代理作为状态锚点，并通过验证选择的层级病例级规则和体素级软门控选择性应用更新。我们比较了初始扫描预测、无同化的滚动预测、最新观测持久化、直接预测、OASA、OASA+校准以及形态学扩张。检查点、OASA规则和校准阈值仅使用验证数据选择。在15个保留测试三元组上，跨三个随机种子，OASA在τ=0.2时的Dice与持久化相当（0.6071±0.0025对比0.6070），而在τ=0.5时数值上获得更高的Dice（0.4269±0.0079对比0.3981），RMSE略有增加。校准将τ=0.2时的Dice提高到0.6178±0.0025，增加了假阳性（FP）支持（11,836→18,663），并减少了假阴性（FN）支持（22,107→17,536）。这反映了接近阈值的支持校准，而非改进的生物预测能力。代码可在以下URL公开获取。

英文摘要

Post-treatment MRI in patients with glioma provides serial observations for updating patient-specific tumor-state proxy estimates, but variable appearances and trajectories complicate forecasting. We formulate forecasting as an observation-aware digital-twin update in which an intermediate observation anchors the patient-specific state. Among 203 patients and 594 follow-up time points, a predefined no-new-treatment criterion retained 120 of 236 candidate triplets, split into 81/24/15 training/validation/test triplets at the patient level. Each time point was represented by a continuous voxel-wise tumor-state proxy map in [0,1] derived from MRI lesion labels. A SegMamba-based single-step forecaster predicted update proposals from multimodal source-state tensors. Observation-Anchored Selective Assimilation (OASA) retained the observed intermediate proxy as the state anchor and selectively applied updates through a validation-selected tiered case-level rule and voxel-wise soft gate. We compared initial-scan forecasting, rollout without assimilation, latest-observation persistence, direct prediction, OASA, OASA + calibration, and morphological dilation. Checkpoints, OASA rules, and calibration thresholds were selected using validation data only. Across three seeds on 15 held-out test triplets, OASA maintained Dice at $τ$ = 0.2 comparable to persistence (0.6071 $\pm$ 0.0025 vs. 0.6070) while yielding numerically higher Dice at $τ$ = 0.5 (0.4269 $\pm$ 0.0079 vs. 0.3981), with a small RMSE increase. Calibration increased Dice at $τ$ = 0.2 to 0.6178 $\pm$ 0.0025, increased false-positive (FP) support (11,836$\rightarrow$18,663), and reduced false-negative (FN) support (22,107$\rightarrow$17,536). This reflects near-threshold support calibration rather than improved biological predictive capability. Code is publicly available at https://github.com/jsudg436/longitudinal-proxy-forecasting.

发表机构

• Yonsei University（延世大学）

机构由 AI 辅助整理，请以论文原文为准。