School of Medicine and Public Health, Department of Medical Physics, University of Wisconsin, Madison, WI, United States of America.
Department of Neuroimaging, Barrow Neurological Institute, Phoenix, AZ, United States of America.
Phys Med Biol. 2021 Jul 30;66(15). doi: 10.1088/1361-6560/ac1457.
Metastatic cancer presents with many, sometimes hundreds of metastatic lesions through the body, which often respond heterogeneously to treatment. Therefore, lesion-level assessment is necessary for a complete understanding of disease response. Lesion-level assessment typically requires manual matching of corresponding lesions, which is a tedious, subjective, and error-prone task. This study introduces a fully automated algorithm for matching of metastatic lesions in longitudinal medical images. The algorithm entails four steps: (1) image registration, (2) lesion dilation, (3) lesion clustering, and (4) linear assignment. In step (1), 3D deformable registration is used to register the scans. In step (2), lesion contours are conformally dilated. In step (3), lesion clustering is evaluated based on local metrics. In step (4), matching is assigned based on non-greedy cost minimization. The algorithm was optimized (e.g. choice of deformable registration algorithm, dilatation size) and validated on 140 scan-pairs of 32 metastatic cancer patients from two independent clinical trials, who received longitudinal PET/CT scans as part of their treatment response assessment. Registration error was evaluated using landmark distance. A sensitivity study was performed to evaluate the optimal lesion dilation magnitude. Lesion matching performance accuracy was evaluated for all patients and for a subset with high disease burden. Two investigated deformable registration approaches (whole body deformable and articulated deformable registrations) led to similar performance with the overall registration accuracy between 2.3 and 2.6 mm. The optimal dilation magnitude of 25 mm yielded almost a perfect matching accuracy of 0.98. No significant matching accuracy decrease was observed in the subset of patients with high lesion disease burden. In summary, lesion matching using our new algorithm was highly accurate and a significant improvement, when compared to previously established methods. The proposed method enables accurate automated metastatic lesion matching in whole-body longitudinal scans.
转移性癌症在全身有许多（有时多达数百个）转移性病变，这些病变对治疗的反应往往存在异质性。因此，为了全面了解疾病的反应，需要对病变水平进行评估。病变水平的评估通常需要手动匹配相应的病变，这是一项繁琐、主观且容易出错的任务。本研究介绍了一种用于纵向医学图像中转移性病变匹配的全自动算法。该算法包括四个步骤：（1）图像配准，（2）病变膨胀，（3）病变聚类，和（4）线性分配。在步骤（1）中，使用 3D 可变形配准来注册扫描。在步骤（2）中，病变轮廓被保形膨胀。在步骤（3）中，基于局部度量来评估病变聚类。在步骤（4）中，根据非贪婪成本最小化分配匹配。该算法在 32 名接受纵向 PET/CT 扫描作为治疗反应评估一部分的转移性癌症患者的 140 对扫描中进行了优化（例如，可变形注册算法的选择、膨胀大小）和验证，这些患者来自两个独立的临床试验。使用标志点距离评估配准误差。进行了一项敏感性研究，以评估最佳病变膨胀幅度。评估了所有患者以及病变负担较高的子集的病变匹配性能准确性。两种研究的可变形配准方法（全身可变形和关节可变形配准）的性能相似，整体配准精度在 2.3 到 2.6 毫米之间。25 毫米的最佳膨胀幅度几乎产生了完美的匹配准确性（0.98）。在病变负担较高的患者亚组中，没有观察到匹配准确性显著下降。总之，与之前建立的方法相比，使用我们新算法的病变匹配具有高度准确性和显著提高。该方法能够在全身纵向扫描中实现准确的自动转移性病变匹配。
