Computer Aided Diagnostic System for Prostate Cancer Detection Using Quantitative Multiparametric MRI

使用定量多参数 MRI 检测前列腺癌的计算机辅助诊断系统

• 批准号: 10705180
• 负责人: Gregory John Metzger
• 金额: $ 61.75万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-23 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10705180
• 关键词: Address; Adoption; Affect; Anatomy; Biological Markers; Biopsy; Blood Tests; Cancer Detection; Cancer Etiology; Cessation of life; Clinic; Clinical; Clinical Trials; Collaborations; Computer software; Data; Data Set; Detection; Development; Devices; Diagnosis; Diagnostic Procedure; Digital Rectal Examination; Disease; Disease Management; Ensure; Environment; Evaluation; Future; Goals; Healthcare Systems; Histopathology; Image Analysis; Imaging Phantoms; Incontinence; Lesion; Life; Location; Magnetic Resonance Imaging; Malignant Neoplasms; Malignant neoplasm of prostate; Manufacturer; Maps; Measures; Methods; Modeling; Outcome; Patients; Performance; Physicians; Positioning Attribute; Prevalence; Procedures; Process; Prostate; Prostate-Specific Antigen; Protocols documentation; Quality of life; Quantitative Evaluations; Radiology Specialty; Reporting; Reproducibility; Sampling; Screening for Prostate Cancer; Sensitivity and Specificity; Site; System; Technology; Training; Translating; Translations; Transrectal Ultrasound; Validation; Vendor; analysis pipeline; blind; clinical application; clinical translation; computer aided detection; computer-assisted diagnostics; cost; design; detection platform; diagnostic platform; diagnostic tool; experience; imaging modality; improved; industry partner; interest; male; men; new technology; non-invasive monitor; novel; predictive modeling; prostate lesions; quantitative imaging; radiologist; risk stratification; serum PSA; tool; tumor specificity; validation studies; visual map

Despite the prevalence of prostate cancer, the current tools available to manage the disease continues to leave physicians and their patients in a position to overdiagnose and overtreat. The confidence to pursue more conservative approaches like active surveillance are limited, as biopsy is known to underestimate the grade and extent of disease, both of which are important for risk stratification. Targeted biopsies, by means of MRI- guidance, are becoming the preferred way to ensure the most aggressive appearing lesions are sampled in the hopes of avoiding some of the issues with standard biopsy approaches. These targeted biopsies make use of multi-parametric MRI (mpMRI) which includes both anatomical and functional information that are complimentary and together increase the sensitivity and specificity for cancer detection. However, the ability to effectively use mpMRI requires specialized training while the standards for properly using the multiple MRI datasets are still being developed. To address this issue, we have developed an alternative method that would provide a quantitative, user-independent, summary of the mpMRI data (qMRI) to visually “map” disease and assess its aggressiveness. Using quantitative MRI, a Composite Biomarker Score (CBS) map is generated, with a demonstrated increase in sensitivity and specificity for tumor detection compared to any single qMRI parameter. Our primary goal is to integrate this predictive qMRI model into a computer-aided diagnostic (CAD) system (referred to as CBS-CAD) to improve the use of mpMRI in PCa management. Employing quantitative MRI (qMRI) can address the issues of a qualitative image analysis if the major roadblocks to its adoption can be overcome. To address the roadblocks and implement the CBS-CAD system we will pursue the following specific aims: 1) develop an analysis pipeline to evaluate qMRI performance and translate CBS-CAD methods, 2) perform a multi-vendor, multi-site quantitative imaging technical performance evaluation and 3) perform a multi-center clinical validation study assessing CBS-CAD performance. Our expected outcome of this academic-industry partnership will be the integration of several novel technologies into a comprehensive CAD system consisting of a phantom and automated software for 1) qMRI system validation and 2) clinical translation of novel models for detecting cancer and assessing aggressiveness.

尽管前列腺癌的流行率，但目前可用于管理该疾病的工具仍在继续 使医生及其患者无法诊断和过度诊断。追求更多的信心 诸如主动监视之类的保守方法受到限制，因为众所周知活检会低估该等级 疾病的程度，这两种疾病对于风险分层都很重要。有针对性的活检，通过MRI- 指导，正在成为确保在 希望避免使用标准活检方法的某些问题。这些有针对性的活检利用 多参数MRI（MPMRI）包括解剖和功能信息 免费并共同提高癌症检测的敏感性和特异性。但是，能够 有效使用mpmri需要专门的培训，而标准可以正确使用多个MRI 数据集仍在开发中。为了解决这个问题，我们开发了一种替代方法 提供MPMRI数据（QMRI）的定量，独立的，依赖用户的摘要，以视觉上“地图”疾病和 评估其侵略性。使用定量MRI，生成复合生物标志物评分（CBS）图， 与任何单个QMRI相比 范围。我们的主要目标是将这种预测QMRI模型集成到计算机辅助诊断（CAD）中 系统（称为CBS-CAD）改善MPMRI在PCA管理中的使用。使用定量 如果采用主要的障碍可以，MRI（QMRI）可以解决定性图像分析的问题 克服。为了解决路障并实施CBS-CAD系统，我们将追求 具体目的：1）开发分析管道来评估QMRI性能并翻译CBS-CAD方法， 2）执行多站点定量成像技术性能评估，3）执行 多中心临床验证研究评估CBS-CAD性能。我们的预期结果 学术行业伙伴关系将是将几种新型技术集成到全面的CAD中 由幻影和自动化软件组成的系统1）QMRI系统验证和2）临床 转化用于检测癌症和评估侵略性的新型模型。