Racially-associated MRI analysis and modeling for predicting aggressive prostate cancer

项目总结 非洲裔美国人(AA)男性前列腺癌(PCA)的发病率和死亡率在 美国。前列腺多参数磁共振成像(Mpmri)是一种非侵入性的成像技术，可以敏感地 通过整合解剖和功能信息来检测前列腺癌。现行的标准化方案 用于解释mpMRI的是前列腺成像报告和数据系统(PI-RADS)。但是，检测到 在PI-RADS中，癌性病变目前还不能解释种族相关的MRI特征。 我们的初步数据显示，在检测有临床意义的前列腺癌(CsPCA)方面，AA和AA之间存在显著差异 当肿瘤位于移行区时，CA患者使用PI-RADS(分别为67%和80%，P=0.026)。 此外，AA和CA男性之间的PCA血流灌注(即KTRANS)有显著差异，当 采用定量动态增强磁共振成像(QDCE)进行测量。当基于PI-RADS的解释被 结合为AA男性指定的KTRANS阈值，#年过渡区的CsPCA检测率 AA男性改善到76%，与CA男性在统计学上没有差异(p=0.180)。 为了提高QDCE的重复性，我们开发了一种名为P4的护理点式便携式灌注体模 在不同的研究所进行测量。基于P4的纠错显著降低了 在两个研究所的三台MRI扫描仪上进行QDCE测量，并提高了KTRANS对csPCA的特异性 检测率从86%提高到93%。我们假设在PCA诊断中种族差异可以通过使用 基于P4的纠错后与种族相关的qDCE测量。 我们建议在加州大学洛杉矶分校的多机构环境中测试这一假设 加州大学洛杉矶分校和阿拉巴马大学伯明翰分校。我们的团队将收集和连接临床，放射学， 以及使用患者特定3D打印的前列腺模具、软件注册和 前列腺癌根治术前后的专家解说。高度精选的放射病理学数据集将是 用于(1)在AA和CA组中表征与肿瘤微环境相关的QDCE测量， 在基于P4的误差校正后使用共同定位的定量放射学-病理学分析，(2)调查 无论种族相关的基于MRI的组织表征是否改善了侵袭性PCA的检测， (3)开发种族/民族特定的深度学习模型，以改进攻击性PCA的检测。 当这些目标成功实现时，AA和CA男性的PCA检测的改进是 与传统策略相比，预期在检测侵袭性前列腺癌方面减少种族差异。

PROJECT SUMMARY African American (AA) men have the highest incidence and mortality rate from prostate cancer (PCa) in the United States. Prostate multi-parametric MRI (mpMRI) is a non-invasive imaging technique that can sensitively detect prostate tumors by integrating anatomical and functional information. The current standardized scheme for interpreting mpMRI is the Prostate Imaging Reporting and Data System (PI-RADS). However, detecting cancerous lesions currently does not account for racially associated MRI characteristics in PI-RADS. Our preliminary data showed a significant difference in detecting clinically significant PCa (csPCa) between AA and CA men using PI-RADS when the tumors are in the transition zone (67% vs. 80%, respectively, p=0.026). In addition, there was a distinctive difference in the PCa perfusion (that is, Ktrans) between AA and CA men, when measured by quantitative dynamic contrast-enhanced MRI (qDCE). When PI-RADS-based interpretation was combined with the Ktrans threshold value specified for AA men, the csPCa detection rate in the transition zone in AA men was improved to 76%, becoming not statistically different from that in CA men (p=0.180). We developed a point-of-care portable perfusion phantom named P4 to improve the reproducibility of qDCE measurement across different institutes. The P4-based error correction significantly reduced the variability in qDCE measurement across three MRI scanners in two institutes and improved the specificity of Ktrans for csPCa detection from 86% to 93%. We hypothesize that the racial disparity in PCa diagnosis can be reduced by using racially associated qDCE measurement after P4-based error correction. We propose to test this hypothesis in a multi-institutional setting at the University of California, Los Angeles (UCLA) and the University of Alabama at Birmingham (UAB). Our team will collect and link clinical, radiologic, and histopathologic information using patient-specific 3D-printed prostate molds, software registration, and expert annotation before and after radical prostatectomy. The highly curated radiology-pathology dataset will be used (1) to characterize the qDCE measurement associated with tumor microenvironment in AA and CA groups, using co-localized quantitative radiology-pathology analyses after P4-based error correction, (2) to investigate whether the racially associated MRI-based tissue characterization improves the detection of aggressive PCa, and (3) to develop the race/ethnicity-specific deep learning model for the improved detection of aggressive PCa. When the Aims are successfully accomplished, the improved detection of PCa in both AA and CA men is anticipated, compared to conventional strategies, reducing the racial disparity in detecting aggressive PCa.