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

用于原发性前列腺癌表征的多模态 MR-PET 机器学习方法

• 批准号: 10358651
• 负责人: Ciprian Catana
• 金额: $ 68.22万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-02-21 至 2024-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10358651
• 关键词: 3-Dimensional; Address; Affect; Algorithms; Anatomy; Biological; Biopsy; Cancer Death Rates; Cancer Patient; Classification; Computer software; Data; Data Set; Devices; Diagnosis; Diagnostic; Discrimination; Disease; Early Diagnosis; Early treatment; FOLH1 gene; Gold; Guidelines; Hand; Histopathology; Hybrids; Image; Individual; Interobserver Variability; Kinetics; Lesion; Life Expectancy; Ligands; Machine Learning; Magnetic Resonance Imaging; Malignant Neoplasms; Malignant neoplasm of prostate; Maps; Measurement; Meta-Analysis; Metabolic; Methodology; Methods; Modality; Modeling; Morphology; PSA level; Patient Selection; Patients; Pelvis; Performance; Phenotype; Physiological; Positron-Emission Tomography; Prostate; Quality of life; Radical Prostatectomy; Reproducibility; Risk; Scanning; Source; Testing; Time; Translations; Work; anticancer research; attenuation; base; bone imaging; cancer classification; cancer imaging; clinical application; clinically relevant; clinically significant; data acquisition; deep learning; diagnostic accuracy; human subject; imaging modality; improved; industry partner; machine learning model; men; multimodal data; multimodality; new technology; non-invasive imaging; radiologist; radiomics; radiotracer; tool; transmission process; tumor

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.

项目摘要 前列腺癌（PCA）是美国男性中最诊断出的非乳腺癌形式。选择 需要立即接受适合主动监测患者的患者，目前依赖于非 - 特定和不准确的测量。一种允许临床医生更自信地歧视的方法 需要与非生命威胁性肿瘤有关的临床相关以改善患者管理。多参数 磁共振成像（MPMRI）是表征的首选非侵入性成像方式 主要PCA。但是，它准确检测具有临床意义的PCA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）与PCA特异性放射性示踪剂相结合，这种限制 并使用高级多模式的机器学习模型（即放射线和深度学习）来表征 基于成像数据的肿瘤侵略性。最近，扫描仪能够同时使用宠物和MR 人类受试者中的数据获取已成为商业上。集成的MR-PET扫描仪是 比较MR和PET衍生的图像功能以识别提供完整的图像功能的理想工具 信息并建立一个最准确地识别临床意义的肿瘤的混合动力宠物MPMRI模型。 尽管这种新颖的技术允许获得完整的核心策略完整的解剖学，但 在单个成像会话中，功能和代谢数据，首先需要解决新的挑战 获得定量准确的宠物数据。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数据和当前可用的方法得出 对于高级定量研究，任务不足。我们已经建立了学术工业合作伙伴关系 加速多模式MR-PET机器学习方法的翻译方式，以PCA研究和临床 通过应对交流挑战并验证机器学习模型来申请临床检测 在接受根治性前列腺切除术的患者中，针对黄金标准组织病理学的重要疾病。 具体而言，我们将：（1）开发和验证一种基于MR的方法来获得定量准确的PET 数据。我们假设该衰减图与使用511 KEV传输获得的衰减图一样准确 来源 - 将获得PET AC的真正黄金标准； （2）确定多模式放射线学模型 最准确地预测PCA侵略性。我们假设这种方法的诊断准确性 将优于独立方式所提供的； （3）评估放射线学和深度学习 预测PPCA侵略性的方法。我们假设机器学习方法将 仅仅将获取的数据应用于比顺序获得的数据时，实现更高的预测精度。