Assessing tumor response to systemic therapies is one of the main applications of PET/CT. Routinely, only a small subset of index lesions out of multiple lesions is analyzed. However, this operator dependent selection may bias the results due to possible significant inter-metastatic heterogeneity of response to therapy. Automated, AI based approaches for lesion tracking hold promise in enabling the analysis of many more lesions and thus providing a better assessment of tumor response. This work introduces a Siamese CNN approach for lesion tracking between PET/CT scans. Our approach is applied on the laborious task of tracking a high number of bone lesions in full-body baseline and follow-up [68Ga]Ga- or [18F]F-PSMA PET/CT scans after two cycles of [177Lu]Lu-PSMA therapy of metastatic castration resistant prostate cancer patients. Data preparation includes lesion segmentation and affine registration. Our algorithm extracts suitable lesion patches and forwards them into a Siamese CNN trained to classify the lesion patch pairs as corresponding or non-corresponding lesions. Experiments have been performed with different input patch types and a Siamese network in 2D and 3D. The CNN model successfully learned to classify lesion assignments, reaching a lesion tracking accuracy of 83 % in its best configuration with an AUC = 0.91. For remaining lesions the pipeline accomplished a re-identification rate of 89 %. We proved that a CNN may facilitate the tracking of multiple lesions in PSMA PET/CT scans. Future clinical studies are necessary if this improves the prediction of the outcome of therapies.

翻译：评估肿瘤对全身治疗的反应是PET/CT的主要应用之一。常规操作中，仅从多个病灶中分析一小部分指标病灶。然而，由于不同转移灶对治疗的反应可能存在显著的异质性，这种依赖于操作者的选择可能导致结果偏差。基于人工智能的自动化病灶追踪方法有望实现对更多病灶的分析，从而提供更佳的肿瘤反应评估。本研究提出了一种用于PET/CT扫描间病灶追踪的孪生卷积神经网络方法。该方法应用于一项繁琐的任务：在转移性去势抵抗性前列腺癌患者接受两个周期[¹⁷⁷Lu]Lu-PSMA治疗后，于全身基线及随访[⁶⁸Ga]Ga-或[¹⁸F]F-PSMA PET/CT扫描中追踪大量骨病灶。数据准备包括病灶分割和仿射配准。我们的算法提取合适的病灶图像块，并将其输入到一个经过训练的孪生卷积神经网络中，该网络用于将病灶图像块对分类为对应或非对应病灶。实验采用了不同的输入图像块类型以及2D和3D孪生网络。卷积神经网络模型成功学会了病灶匹配分类，在其最佳配置下达到了83%的病灶追踪准确率，AUC = 0.91。对于其余病灶，该流程实现了89%的重新识别率。我们证明卷积神经网络可以促进PSMA PET/CT扫描中多病灶的追踪。未来需要临床研究来验证这是否能改善治疗结果的预测。