Prostate Cancer Radio-Pathomics for Differentiating Clinically Significant Disease

摘要 前列腺癌是最常见的非皮肤癌，影响七分之一的男性。甚至 当用根治性膀胱切除术治疗时，历史上约20%的患者表现出肿瘤复发。这 该提案将侧重于整合两个独立的互补数据集，以更好地区分高风险 患者：多参数磁共振成像（MP-MRI）和手术后全载片前列腺 病理学样本我们将开发能够预测潜在病理学的放射病理学算法， 通过非侵入性成像检查前列腺癌的特征，以区分具有高转移潜力的前列腺癌。我们 总体假设是，前列腺癌的微观异质病理学特征是可靠的， 用宏观定量MP-MRI可检测和定量。非侵入性地映射这些特征将 为区分侵袭性前列腺癌和惰性前列腺癌提供了临床有用的工具， 以辐射为目标 该提案包括两个具体目标，以响应PAR-19-264中概述的目标。特定于 资助机会公告：Aim 1将开发用于定义成像的放射病理学方法- 基于能够区分侵袭性前列腺癌和惰性前列腺癌的生物标志物。这将在 目标1.1中的显微镜水平与组织学，然后目标1.2中的宏观水平与MP-MRI。目标1.3 将通过故意干扰系统和算法来测试无线电病理学算法的弹性。 将Rad-Path数据集与目标1.4中的临床变量相结合，以提高灵敏度和特异性 通过将我们的放射病理学图谱与其他组学相关联，进行早期检测和鉴别诊断。 此外，目标1中还包括广泛的验证实验，旨在进一步建立稳健性 无线电病理学算法在目标2中，该项目将把放射病理学算法应用于临床。 这将包括在Aim 2.1中将我们的算法调整为两种临床MR成像系统（GE和Siemens）， 以及在目标2.2中，开发用于在组合的MR-LINAC上进行连续成像的放射病理学驱动的MRI协议 这是美国仅有的两个系统之一。该项目的完成将提供一套强大的 定量成像工具，以临床医生改善分化的高风险前列腺癌， 测量对前列腺癌治疗的反应。

Abstract Prostate cancer is the most commonly diagnosed non-cutaneous cancer, affecting one in seven men. Even when treated with a radical prostatectomy, historically about 20% of patients exhibit tumor recurrence. This proposal will focus on the integration of two separate, complimentary datasets to better differentiate high risk patients: multi-parametric magnetic resonance imaging (MP-MRI) and whole-mount post-surgical prostate pathology samples. We will develop radio-pathomic algorithms capable of predicting underlying pathomic features from non-invasive imaging in order to differentiate prostate cancer with high metastatic potential. Our overarching hypothesis is that microscopic, heterogeneous pathomic features of prostate cancer are reliably detectable and quantifiable with macroscopic quantitative MP-MRI. Non-invasively mapping these features will provide a clinically useful tool for differentiating aggressive from indolent prostate cancer, and for potentially targeting with radiation. This proposal includes two specific aims in response to the goals outlined in PAR-19-264. Specific to the funding opportunity announcement: Aim 1 will develop radio-pathomic approaches for defining imaging- based biomarkers capable of distinguishing aggressive from indolent prostate cancer. This will be done at the microscopic level in Aim 1.1 with histology, and then at the macroscopic level in Aim 1.2 with MP-MRI. Aim 1.3 will test the resilience of the radio-pathomic algorithm by intentionally perturbing the system and algorithms. Combining the Rad-Path datasets with clinical variables in Aim 1.4 will look to improve sensitivity and specificity for early detection and differential diagnosis, by correlating our radio-pathomic maps with other omics. Additionally, included in Aim 1, are extensive validation experiments meant to further establish the robustness of the radio-pathomic algorithm. In Aim 2, this project will translate the radio-pathomic algorithms to the clinic. This will include in Aim 2.1 adapting our algorithms to two clinical MR imaging systems (GE and Siemens), and in Aim 2.2 developing a radio-pathomic driven MRI protocol for serial imaging on a combined MR-LINAC system, one of only two operational in the US. Completion of this project will provide a powerful set of quantitative imaging tools to clinicians for improved differentiation of high-risk prostate cancer and for measuring response to prostate cancer therapy.