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

Geles：一种用于神经内分泌肿瘤普遍病变识别的新型影像信息学系统

• 批准号: 10740578
• 负责人: BENNETT B CHIN
• 金额: $ 38.85万
• 依托单位: UNIVERSITY OF COLORADO DENVER
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-19 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10740578
• 关键词: Acceleration; Address; Algorithms; Artificial Intelligence; Clinical Research; Communities; Data; Data Set; Detection; Development; Devices; Diagnosis; Disease; Eligibility Determination; Emission-Computed Tomography; Enhancing Lesion; Hepatic; Human; Image; Image Analysis; In complete remission; Individual; Informatics; Institution; Learning; Lesion; Liver; Manuals; Medical; Medical Imaging; Medical Research; Methods; Modeling; Molecular Target; Neoplasm Metastasis; Neuroendocrine Tumors; Noise; Patient-Focused Outcomes; Patients; Peptides; Performance; Pharmaceutical Preparations; Positron-Emission Tomography; Progression-Free Survivals; Protocols documentation; Radioisotopes; Regimen; Reproducibility; Research; Residual Neoplasm; Residual state; Resolution; Resource-limited setting; Resources; Site; Somatostatin Receptor; Standardization; System; Systemic Therapy; Technology; Testing; Therapeutic; Training; Variant; X-Ray Computed Tomography; algorithm development; anticancer research; burden of illness; clinical practice; cost; data acquisition; data resource; deep learning; deep learning model; deep neural network; design; gastroenteropancreatic neuroendocrine tumor; generative adversarial network; image reconstruction; image translation; imaging informatics; imaging system; improved; informatics tool; interest; learning strategy; molecular imaging; novel; novel therapeutics; open source; receptor; reconstruction; standard of care; task analysis; therapy development; treatment response; tumor

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.

项目概要 胃肠胰神经内分泌肿瘤（GEP-NET）是很难检测到的肿瘤，通常 处于晚期，肝脏是最常见的转移部位。 68Ga 和 64Cu DOTATATE 正电子发射断层扫描-计算机断层扫描 (PET/CT) 是最灵敏的识别方法 生长抑素受体亚型 2 阳性 GEP-NET，以及靶向肽放射性核素受体治疗 177Lu DOTATATE 对许多患者来说是最有效的全身疗法。尽管优势明显 与之前的护理标准相比，绝大多数患者 (99%) 没有出现无进展生存期 完全缓解并需要额外的治疗。治疗方法的进一步发展需要准确的 评估对治疗的反应。然而，目前自动化、自动化的医疗需求尚未得到满足。 68Ga DOTATATE 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已被应用于 自动化病变检测和量化，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有显着不同。 因此，不考虑域转移的模型将无法很好地推广到未见过的数据，从而导致较差的结果 病灶检测性能。在这项拟议的研究中，我们将开发一种新颖的基于深度学习的成像 信息学系统，称为 Geles，用于 GEP-NET PET/CT 中肝脏的自动化、通用病变检测 成像。该系统将使用列表模式数据采集来生成大型、多样化的带注释的训练数据集， 其次是新颖的对抗性学习，以增强模型的通用性。 拟议的 Geles 系统将由两个模块组成：领域泛化和领域适应。目标1 将开发一个对抗性域泛化模块，该模块可泛化到未见过的域或资源。 该模块将构建一个具有领域对抗学习的深度神经网络并提取领域不变性 用于单个病变识别的特征表示，以便系统可以推广到未见过的领域 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 开发一个面向目标的领域适应模块，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组未注释的图像，该模块 将进行低资源无监督域适应，以进一步提高病变检测性能。 具体来说，它将构建一个新颖的增强生成对抗网络，用于图像到图像的翻译 低资源设置，使 Geles 可以利用有限的、未注释的特定目标数据并进行 以目标为导向，增强病变检测。