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：评估肿瘤对系统性治疗的反应是PET/CT的主要应用之一。常规操作中，仅从众多病灶中选取一小部分指标病灶进行分析。然而，这种依赖于操作者的选择可能因不同转移灶对治疗的反应存在显著异质性而导致结果偏差。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，从而提供更优的肿瘤反应评估。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，于全身基线和随访[⁶⁸Ga]Ga-或[¹⁸F]F-PSMA PET/CT扫描中追踪大量骨病灶。数据预处理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，并将其输入训练好的孪生卷积神经网络，以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二维和三维孪生网络。该卷积神经网络模型成功学会了病灶匹配分类，在最佳配置下达到83%的病灶追踪准确率，AUC值为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促进PSMA PET/CT扫描中多病灶的追踪。未来需通过临床研究验证该方法是否能改善治疗结果的预测。