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: 10493089
• 负责人: Gregory John Metzger
• 金额: $ 56.93万
• 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/10493089
• 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; Impotence; Incontinence; Investigation; Lesion; Life; Location; Magnetic Resonance Imaging; Malignant Neoplasms; Malignant neoplasm of prostate; Manufacturer Name; 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; base; blind; clinical application; clinical center; clinical translation; computer aided detection; computer-assisted diagnostics; cost; design; detection platform; diagnostic platform; diagnostic tool; experience; image guided; imaging modality; imaging system; improved; industry partner; interest; male; men; new technology; non-invasive monitor; novel; predictive modeling; prostate lesions; quantitative imaging; radiologist; risk stratification; serum PSA; targeted imaging; tool; tumor specificity; validation studies

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.

尽管前列腺癌很普遍，但目前可用于控制该疾病的工具仍在不断发展 让医生和他们的病人有信心去追求更多。 主动监测等保守方法是有限的，因为众所周知活检会低估等级 和疾病范围，这两者对于通过 MRI 进行的有针对性的活检都很重要。 指导，是确保最具侵袭性的病变在 希望避免标准活检方法的一些问题。 多参数 MRI (mpMRI)，包括解剖学和功能信息 然而，互补并共同提高了癌症检测的敏感性和特异性。 有效使用 mpMRI 需要专门培训，而正确使用多重 MRI 的标准 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、独立于用户的摘要，以直观地“绘制”疾病和 使用定量 MRI 评估其侵袭性，生成综合生物标志物评分 (CBS) 图， 与任何单一 qMRI 相比，肿瘤检测的灵敏度和特异性均得到提高 我们的主要目标是将这种预测性 qMRI 模型集成到计算机辅助诊断 (CAD) 中。 系统（称为 CBS-CAD）以提高 mpMRI 在 PCa 管理中的应用。 如果采用 MRI (qMRI) 的主要障碍能够解决，则它可以解决定性图像分析的问题 为了解决障碍并实施 CBS-CAD 系统，我们将采取以下措施。 具体目标：1) 开发一个分析流程来评估 qMRI 性能并转化 CBS-CAD 方法， 2) 执行多供应商、多站点定量成像技术性能评估，以及 3) 执行 评估 CBS-CAD 性能的多中心临床验证研究。 学术与工业合作伙伴关系将把多项新技术集成到一个全面的 CAD 中 系统由模型和自动化软件组成，用于 1) qMRI 系统验证和 2) 临床 翻译用于检测癌症和评估侵袭性的新模型。