Multimodal MR-PET Machine Learning Approaches for Primary Prostate Cancer Characterization

项目摘要 前列腺癌（PCa）是美国男性中最常见的非皮肤癌。的选择 需要立即从适合积极监测的患者那里得到治疗的患者目前依赖非 具体和不准确的测量。一种让临床医生更有信心地区分 临床相关的非危及生命的肿瘤，需要改善患者的管理。多参数 磁共振成像（mpMRI）是用于表征 原发性前列腺癌然而，其检测临床显著PCa的准确性是可变的。我们建议解决 通过将mpMRI与正电子发射断层扫描（PET）和PCA特异性放射性示踪剂相结合， 并使用高级多模式机器学习模型（即放射组学和深度学习）来表征 基于成像数据的肿瘤侵袭性。最近，能够同时进行PET和MR的扫描仪 在人类受试者中的数据采集已经变得商业化。集成式MR-PET扫描仪是 用于比较MR和PET衍生图像特征的理想工具，以识别提供互补性的图像特征 该系统能够提供最新的临床信息并构建混合PET-mpMRI模型，以最准确地识别临床显著肿瘤。 虽然这种新技术允许获得完美配准的互补解剖结构， 功能和代谢数据在一个单一的成像会话，一个新的挑战需要首先解决， 获得定量准确的PET数据。在集成的MR-PET扫描仪中，PET所需的信息 衰减校正（AC）必须从MR数据和当前可用于此的方法中导出 任务不足以进行高级定量研究。我们建立了学术和工业合作伙伴关系， 加速将多模态MR-PET机器学习方法转化为PCa研究和临床 通过解决AC挑战和验证用于临床检测的机器学习模型， 与接受根治性乳腺癌切除术患者的金标准组织病理学相比， 具体而言，我们将：（1）开发和验证基于MR的方法，以获得定量准确的PET 数据我们假设衰减图与使用511 keV传输获得的衰减图一样准确 来源-PET AC的真正黄金标准-将获得;（2）确定多模式放射组学模型， 最准确地预测PCa的攻击性。我们假设这种方法的诊断准确性 将优于独立模式所提供的上级;（3）评估放射组学和深度学习 预测pPCa攻击性的方法。我们假设机器学习方法将 当应用于同时采集的数据时，比应用于顺序采集的数据时，实现更高的预测精度。

Project Summary Prostate cancer (PCa) is the most diagnosed form of non-cutaneous cancer in US men. The selection of patients who require immediate treatment from those suitable for active surveillance currently relies on non- specific and inaccurate measurements. A method that allows clinicians to more confidently discriminate clinically relevant from non-life-threatening tumors is needed to improve patient management. Multiparametric magnetic resonance imaging (mpMRI) is the preferred non-invasive imaging modality for characterizing primary PCa. However, its accuracy for detecting clinically significant PCa is variable. We propose to address this limitation by combining mpMRI with positron emission tomography (PET) with a PCa-specific radiotracer and using advanced multimodal machine learning models (i.e. radiomics and deep learning) to characterize tumor aggressiveness based on the imaging data. Recently, scanners capable of simultaneous PET and MR data acquisition in human subjects have become commercially available. An integrated MR-PET scanner is the ideal tool for comparing MR and PET derived image features to identify those that provide complementary information and build a hybrid PET-mpMRI model that most accurately identifies clinically significant tumors. While this novel technology allows the acquisition of perfectly coregistered complementary anatomical, functional and metabolic data in a single imaging session, a new challenge needs to first be addressed to obtain quantitatively accurate PET data. In an integrated MR-PET scanner, the information needed for PET attenuation correction (AC) has to be derived from the MR data and the methods currently available for this task are inadequate for advanced quantitative studies. We have formed an academic-industrial partnership to accelerate the translation of multimodal MR-PET machine learning approaches into PCa research and clinical applications by addressing the AC challenge and validating machine learning models for detecting clinically significant disease against gold standard histopathology in patients undergoing radical prostatectomy. Specifically, we will: (1) Develop and validate an MR-based approach for obtaining quantitatively accurate PET data. We hypothesize that attenuation maps as accurate as those obtained using a 511 keV transmission source – the true gold standard for PET AC – will be obtained; (2) Identify the multimodal radiomics model that most accurately predicts PCa aggressiveness. We hypothesize that the diagnostic accuracy of this approach will be superior to that offered by the stand-alone modalities; (3) Evaluate radiomics and deep learning approaches for predicting pPCa aggressiveness. We hypothesize that machine learning approaches will achieve a higher predictive accuracy when applied to data acquired simultaneously than sequentially.