Geles: A Novel Imaging Informatics System for Generalizable Lesion Identification in Neuroendocrine Tumors

项目总结 胃肠胰脏神经内分泌肿瘤(GEP-Net)是一种很难发现的肿瘤，通常 出现在晚期，肝脏是最常见的转移部位。68Ga和64CuDOTATE 正电子发射断层扫描-计算机断层扫描(PET/CT)是最敏感的方法 生长抑素受体亚型2阳性GEP-Net和靶向多肽放射性核素受体治疗 对许多患者来说，177 Lu DOTATE是最有效的系统疗法。尽管有明显的优势， 无进展生存与以前的护理标准相比，绝大多数患者(99%)没有 完全缓解并需要额外的治疗。进一步开发治疗方法需要准确的 对治疗反应的评估。然而，目前还没有满足医疗对自动化、 68Ga DOTATE阳性疾病负担的标准化量化，这可能会对 新的治疗药物方案的开发。基于深度学习的方法最近被应用于 自动病变检测和量化，并实现了最先进的性能。这些方法， 但是，不要考虑训练数据和测试数据之间的数据集/域转换。在数据集/域转换中，数据 用于构建和训练模型的分布可能与用于模型测试的分布显著不同。 因此，不考虑域转移的模型不能很好地推广到看不见的数据，从而导致较差 病变检测性能。在这项拟议的研究中，我们将开发一种新的基于深度学习的成像方法 GEP-Net PET/CT肝脏自动化、通用化病变检测信息学系统GLES 成像。该系统将使用列表模式数据采集来产生大量、多样化的注释训练数据集， 其次是新颖的对抗性学习，以增强模型的泛化能力。 建议的GELES系统将由两个模块组成，即领域泛化和领域适配。目标1 将开发一个对抗性领域推广模块，可以推广到看不见的领域或资源。 该模块将构建一个领域对抗学习的深度神经网络，并提取领域不变量 用于单个病变识别的特征表示，使系统可以泛化到不可见的领域 数据，例如来自不同机构、设备、成像协议和其他变体的PET图像。目标2将 开发面向目标的领域适配模块，该模块可自动适应新的特定数据集 兴趣(即目标数据集)。给定来自某个目标数据集的一小部分未注释图像，此模块 将进行低资源的无监督领域适配，进一步提升病变检测性能。 具体地说，它将建立一个新颖的、增强的生成性对抗网络，用于图像到图像的翻译 低资源设置，以便GELE可以利用有限的、未注释的特定目标数据和行为 以目标为导向，增强的病变检测。

PROJECT SUMMARY Gastroenteropancreatic neuroendocrine tumors (GEP-NETs) are difficult to detect tumors which commonly present at advanced stages, with the liver as the most common site of metastases. 68Ga and 64Cu DOTATATE positron emission tomography-computed tomography (PET/CT) are the most sensitive methods to identify somatostatin receptor subtype 2 positive GEP-NETs, and targeted peptide radionuclide receptor therapy with 177Lu DOTATATE is the most effective systemic therapy for many patients. Despite the clear advantage in progression-free survival compared to prior standard of care, the vast majority of patients (99%) do not have complete response and require additional therapies. Further development of treatments requires an accurate assessment of the response to therapy. However, there is currently an unmet medical need for automated, standardized quantification of 68Ga DOTATATE positive disease burden, which could have a great impact on novel therapeutic drug regimen development. Deep learning-based approaches have recently been applied to automated lesion detection and quantification, and have achieved state-of-the-art performance. These methods, however, do not consider dataset/domain shifts between training and testing data. In dataset/domain shifts, data used to build and train models might have a significantly different distribution from that used for model testing. Therefore, models without considering domain shifts would not generalize well to unseen data, leading to poor lesion detection performance. In this proposed research, we will develop a novel deep learning-based imaging informatics system, termed Geles, for automated, Generalizable lesion detection for livers in GEP-NET PET/CT imaging. This system will use list-mode data acquisition to produce a large, diverse annotated training dataset, followed by novel adversarial learning to enhance model generalizability. The proposed Geles system will consist of two modules, domain generalization and domain adaptation. Aim 1 will develop an adversarial domain generalization module that is generalizable to unseen domains or resources. This module will build a deep neural network with domain-adversarial learning and extract domain-invariant feature representations for individual lesion identification, so that the system can generalize to unseen domain data, such as PET images from different institutions, devices, imaging protocols, and other variations. Aim 2 will develop a target-oriented domain adaptation module that is automatically adaptable to new specific datasets of interest (i.e., target datasets). Given a small set of unannotated images from a certain target dataset, this module will conduct low-resource unsupervised domain adaptation to further boost the lesion detection performance. Specifically, it will build a novel, augmented generative adversarial network for image-to-image translation in a low-resource setting, so that Geles can take advantage of limited, unannotated specific target data and conduct target-oriented, enhanced lesion detection.