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.

项目概要 前列腺癌（PCa）是美国男性中诊断最多的非皮肤癌。 需要立即接受适合主动监测的治疗的患者目前依赖于非 一种可以让忠诚者更加自信地进行区分的方法。 需要与非危及生命的肿瘤具有临床相关性来改善患者管理。 磁共振成像 (mpMRI) 是表征特征的首选非侵入性成像方式 然而，其检测具有临床意义的 PCa 的准确性是可变的。 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来克服这一限制 并使用先进的多模态机器学习模型（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，扫描仪能够同时进行 PET 和 MR。 集成 MR-PET 扫描仪已在人类受试者中进行数据采集。 比较 MR 和 PET 衍生图像特征以识别那些提供互补的图像特征的理想工具 信息并建立混合 PET-mpMRI 模型，最准确地识别具有临床意义的肿瘤。 虽然这项新技术可以获取完美配准的互补解剖结构， 在一次成像过程中获得功能和代谢数据，首先需要解决一个新的挑战 在集成 MR-PET 扫描仪中获得定量准确的 PET 数据。 衰减校正 (AC) 必须从 MR 数据和当前可用的方法中得出 任务不足以进行高级定量研究。我们已经建立了学术与工业合作伙伴关系。 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 挑战并验证用于临床检测的机器学习模型来应用 在接受根治性前列腺切除术的患者中，存在与金标准组织病理学不符的显着疾病。 具体来说，我们将： (1) 开发并验证基于 MR 的方法，以获得定量准确的 PET 我们捕获的衰减图与使用 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) 确定多模态放射组学模型 我们发现这种方法的诊断准确性最高。 (3) 评估放射组学和深度学习 我们发现机器学习方法将预测 pPCa 的攻击性。 当应用于同时获取的数据时，比顺序获取的数据获得更高的预测精度。