TopoGate: 用于纵向低剂量CT新病变预测的质量感知拓扑稳定门控融合
TopoGate: Quality-Aware Topology-Stabilized Gated Fusion for Longitudinal Low-Dose CT New-Lesion Prediction
AI总结：
TopoGate通过质量感知门控融合外观和减影视图，提升纵向低剂量CT新病变预测的准确性和可靠性。
摘要：
纵向低剂量CT随访在噪声、重建内核和配准质量上存在差异。这些差异会破坏减影图像并可能触发虚假的新病变警报。我们提出了TopoGate，这是一个轻量模型，将随访外观视图与减影视图结合，并通过一个学习的质量感知门控来控制它们的影响。该门控由三个病例特定的信号驱动：CT外观质量、配准一致性以及通过拓扑度量测量的解剖拓扑稳定性。在包含152对来自122名患者的NLST--New-Lesion--LongCT队列中，TopoGate在单视图基线之上提高了区分能力和校准能力，实现了ROC曲线下的面积为0.65，标准差为0.05，Brier分数为0.14。去除被污染或低质量的配对，通过质量评分识别后，ROC曲线下的面积从0.62增加到0.68，Brier分数从0.14降低到0.12。该门控对退化反应预测性地进行响应，当噪声增加时，更重视外观，这与放射科医生的实践相吻合。该方法简单、可解释且实用，适用于可靠的纵向LDCT分选。
英文摘要：
Longitudinal low-dose CT follow-ups vary in noise, reconstruction kernels, and registration quality. These differences destabilize subtraction images and can trigger false new lesion alarms. We present TopoGate, a lightweight model that combines the follow-up appearance view with the subtraction view and controls their influence through a learned, quality-aware gate. The gate is driven by three case-specific signals: CT appearance quality, registration consistency, and stability of anatomical topology measured with topological metrics. On the NLST--New-Lesion--LongCT cohort comprising 152 pairs from 122 patients, TopoGate improves discrimination and calibration over single-view baselines, achieving an area under the ROC curve of 0.65 with a standard deviation of 0.05 and a Brier score of 0.14. Removing corrupted or low-quality pairs, identified by the quality scores, further increases the area under the ROC curve from 0.62 to 0.68 and reduces the Brier score from 0.14 to 0.12. The gate responds predictably to degradation, placing more weight on appearance when noise grows, which mirrors radiologist practice. The approach is simple, interpretable, and practical for reliable longitudinal LDCT triage.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
