MRI Imaging and Biomarkers for Early Detection of Aggressive Prostate Cancer

用于早期检测侵袭性前列腺癌的 MRI 成像和生物标志物

• 批准号: 10018835
• 负责人: Alan Pollack
• 金额: $ 60.47万
• 依托单位: UNIVERSITY OF MIAMI SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-16 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10018835
• 关键词: 3-Dimensional; Algorithms; Attention; Biological Markers; Biopsy; Blood; Body Fluids; Cells; Cessation of life; Characteristics; Classification; Clinical; Clinical Trials; Collaborations; Complement; Data; Detection; Diagnosis; Diagnostic; Early Detection Research Network; Early Diagnosis; Early identification; Evolution; Funding; Gene Expression; Genomics; Gland; Gleason Grade for Prostate Cancer; Goals; Habitats; Healthcare Systems; Histopathologic Grade; Histopathology; Image; Imaging Techniques; Individual; Indolent; Information Systems; Kinetics; Laboratories; Learning; Lesion; Magnetic Resonance Imaging; Malignant Neoplasms; Malignant neoplasm of prostate; Measures; Methods; Modeling; Multiparametric Analysis; Neoplasm Circulating Cells; Neoplasm Metastasis; Outcome; Pathologic; Patients; Performance; Predictive Value; Procedures; Process; Prostate; Prostatectomy; Protocols documentation; Publishing; Radiogenomics; Reporting; Research Personnel; Risk; Risk stratification; Sampling; Scoring Method; Screening for Prostate Cancer; Serum; System; Techniques; Testing; Tissues; Tumor Volume; Ultrasonography; Universities; Urine; Validation; Vendor; Work; adverse outcome; base; blood-based biomarker; cancer risk; cohort; cost; deep learning; digital; early detection biomarkers; genomic signature; high risk; imaging biomarker; improved; macrophage; men; novel; overtreatment; prognostic; prospective; prostate biopsy; prostate lesions; quantitative imaging; radiomics; reconstruction; rectal; tool; tumor; tumor heterogeneity; urinary

Abstract Oversampling and overdiagnosis of prostate cancer are significant management and cost issues that burden our health care system and the individual at risk with unnecessary biopsies and potential complications. The proposed studies will validate recent advances in quantitative prostate multiparametric MRI (mpMRI) techniques, blood biomarkers of aggressive prostate cancer and radiogenomics that relate to increased aggressive cancer risk by our group and collaborators. The overarching goal is to increase the negative predictive value (NPV) for significant prostate cancer and consequently reduce unnecessary biopsies. Central to the proposal are key collaborations between investigators from the Consortium for Imaging and Biomarkers (CIB), Early Detection Research Network (EDRN), and Jet Propulsion Laboratories (JPL). Novel automated techniques for quantitative analysis of mpMRI that identify prostate habitats at risk of harboring significant prostate cancer (Gleason score 3+4 and above or Grade Group (GG)2+) will be combined with improvements in mpMRI-ultrasound fusion biopsies. Our automated pixel-by-pixel 3D prostate habitat risk scoring (HRS) system is superior to the standard prostate lesion classification system, PIRADSv2, and is hypothesized to improve the Negative Predictive Value (NPV) for significant GG2+ cancers (Aim 1). Radiomics will be applied in Aim 1 to refine HRS in the University of Miami MDSelect protocol of 250 men (discovery=150; validation=100). Just as PIRADSv2 is suboptimal because it does not incorporate quantitative imaging information in risk stratification, models of risk based only on histopathologic grading ignore the underlying genomic determinants of outcome. We have shown that radiomics features are associated with underlying gene expression markers of adverse outcome. We propose in Aim 2 to apply newer criteria that incorporate Decipher® score with clinical-pathologic factors to improve the identification of aggressive prostate cancer. Radiomic features associated with these published criteria, termed the Spratt criteria, will improve the NPV for nonaggressive prostate cancer in the MDSelect cohort. We will also collaborate with investigators involved in the EDRN ID-430 clinical trial to test our models in a cohort (n=200) in a less rigorously controlled multi-institutional group with more variability in imaging techniques, vendors and machines. There is also opportunity to further improve risk classification through the analysis of blood-based markers (Aim 3) such as 4Kscore, circulating tumor cells (CTCs) and circulating cancer associated macrophage like (CAML) cells that are early biomarkers of aggressive cancer. The proposed work will test the incremental benefit of adding these serum-based biomarkers to improve the NPV models for significant prostate cancer.

摘要 前列腺癌的过度采样和过度诊断是重要的管理和成本问题， 不必要的活检和潜在的并发症给我们的卫生保健系统和处于危险中的个人带来负担。 拟议的研究将验证定量前列腺多参数MRI（mpMRI）的最新进展 技术，侵袭性前列腺癌的血液生物标志物和放射基因组学， 积极的癌症风险由我们的小组和合作者。首要目标是增加负面影响 预测值（NPV），从而减少不必要的活检。中央 该提案的关键是来自成像和生物标志物联盟的研究人员之间的合作 (CIB)早期探测研究网络（EDRN）和喷气推进实验室（JPL）。 用于定量分析mpMRI的新型自动化技术，可识别有前列腺增生风险的前列腺生境 将合并患有显著前列腺癌（Gleason评分3+4及以上或分级组（GG）2+）的患者 在mpMRI超声融合活检方面取得了进步。我们的自动逐像素3D前列腺栖息地风险 评分（HRS）系统上级于标准前列腺病变分类系统PIRADSv 2， 假设用于改善显著的GG 2+癌症的阴性预测值（NPV）（目的1）。放射组学 将应用于目标1，以完善迈阿密大学MDSelect方案中250名男性的HRS（发现=150; validation=100）。 正如PIRADSv 2是次优的，因为它不包含定量成像信息， 风险分层，仅基于组织病理学分级的风险模型忽略了潜在的基因组 结果的决定因素。我们已经表明，放射组学特征与潜在基因相关， 不良结果的表达标志物。我们在目标2中建议采用更新的标准， Decipher®评分结合临床病理因素，以提高侵袭性前列腺癌的识别率。 与这些公布的标准相关的放射组学特征，称为Spratt标准，将提高NPV， MDSelect队列中的非侵袭性前列腺癌。 我们还将与参与EDRN ID-430临床试验的研究人员合作，以测试我们的模型 在一个队列（n=200）中，在一个控制不太严格的多机构组中，成像变异性更大 技术、供应商和机器。 还有机会通过分析基于血液的疾病， 标记物（Aim 3），如4K评分、循环肿瘤细胞（CTC）和循环癌症相关的 巨噬细胞样（CAML）细胞是侵袭性癌症的早期生物标志物。拟议的工作将测试 添加这些基于血清的生物标志物以改善NPV模型的增量益处， 前列腺癌