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

尽管前列腺癌盛行，但目前可用于管理这种疾病的工具继续 让医生和他们的病人处于过度诊断和过度治疗的境地。追求更多的信心 像主动监测这样的保守方法是有限的，因为活组织检查被认为低估了分级 和疾病的程度，这两个因素对于风险分层都很重要。通过核磁共振进行靶向活组织检查- 指导，正在成为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。这些有针对性的活检利用了 多参数磁共振成像(MpMRI)，包括解剖和功能信息， 互补和共同提高了癌症检测的敏感性和特异性。然而，能够 有效地使用mpMRI需要专门的培训，而正确使用多重MRI的标准 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 提供量化的、独立于用户的mpMRI数据摘要(QMRI)，以直观地显示疾病和 评估其攻击性。使用定量MRI，生成复合生物标记物得分(CBS)图， 与任何单一的qMRI相比，对肿瘤检测的敏感度和特异度都有明显提高 参数。我们的主要目标是将这种可预测的qMRI模型集成到计算机辅助诊断(CAD)中 系统(简称CBS-CAD)，以改进mpMRI在PCA管理中的使用。采用量化 磁共振成像(QMRI)可以解决定性图像分析的问题，如果采用它的主要障碍可以 被征服。为了解决障碍和实施CBS-CAD系统，我们将进行以下工作 具体目标：1)开发用于评估qMRI性能和翻译CBS-CAD方法的分析管道， 2)执行多供应商、多站点定量成像技术性能评估；3)执行 评估CBS-CAD性能的多中心临床验证研究。我们对此的预期结果 学术界和产业界的合作将是将几种新技术集成到一个全面的CAD中 由体模和自动化软件组成的系统，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。

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.