Prostate Cancer Radio-Pathomics for Differentiating Clinically Significant Disease

前列腺癌放射病理学用于区分有临床意义的疾病

• 批准号: 10357756
• 负责人: Peter S LaViolette
• 金额: $ 58.25万
• 依托单位: MEDICAL COLLEGE OF WISCONSIN
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-03-01 至 2026-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10357756
• 关键词: Affect; Algorithms; Architecture; Biological Markers; Clinic; Clinical; Computational algorithm; Computer software; Consensus; Data Set; Dependence; Diagnosis; Diagnostic; Differential Diagnosis; Disease; Early Diagnosis; Exhibits; External Beam Radiation Therapy; Funding Opportunities; Gland; Gleason Grade for Prostate Cancer; Goals; Histologic; Histology; Image; Imaging Device; Individual; Indolent; Libraries; Location; MRI Scans; Magnetic Resonance Imaging; Malignant Neoplasms; Malignant neoplasm of prostate; Maps; Measures; Methods; Microscopic; Modeling; Operative Surgical Procedures; Pathology; Patients; Pattern; Performance; Physicians; Population; Prognosis; Prostate; Prostate Cancer therapy; Prostatic Neoplasms; Protocols documentation; Radiation; Radiation therapy; Radical Prostatectomy; Radio; Radiology Specialty; Recurrence; Risk; Sampling; Sensitivity and Specificity; Severity of illness; Stress Tests; System; Techniques; Technology; Testing; Therapeutic; Tissue Sample; Training; Translating; Validation; Vendor; base; cancer risk; clinical application; clinical decision-making; clinical imaging; clinically significant; disorder risk; evidence base; experimental study; high risk; image processing; imaging biomarker; imaging system; improved; individualized medicine; men; non-invasive imaging; overtreatment; personalized cancer therapy; predictive modeling; prostate cancer risk; quantitative imaging; resilience; response; screening; serial imaging; tool; treatment strategy; tumor

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.

摘要 前列腺癌是最常见的非皮肤癌，影响七分之一的男性。连 当接受根治性前列腺切除术时，历史上约有20%的患者出现肿瘤复发。这 提案将侧重于集成两个独立的、互补的数据集，以更好地区分高风险 患者：多参数磁共振成像(MP-MRI)和前列腺手术后的整体植入 病理样本。我们将开发能够预测潜在病理的放射病理算法 从非侵入性成像的特征，以鉴别前列腺癌与高转移潜力。我们的 最重要的假设是，前列腺癌的微观、异质性病理特征是可靠的 可用宏观定量MP-MRI检测和量化。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一种临床有用的工具，并有可能 以辐射为靶点。 该提案包括两个具体目标，以回应PAR-19-264中概述的目标。特定于 融资机会公告：AIM 1将开发用于定义成像的放射病理学方法- 基于生物标记物，能够区分侵袭性和惰性前列腺癌。这项工作将在 AIM 1.1进行组织学检查，AIM 1.2在宏观水平进行MP-MRI检查。目标1.3 将通过故意扰乱系统和算法来测试无线电病理算法的弹性。 将Rad-Path数据集与Aim 1.4中的临床变量相结合将寻求提高灵敏度和特异度 通过将我们的放射病理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，旨在进一步建立健壮性 放射病理算法的一部分。在目标2中，该项目将把放射病理算法转化为临床应用。 这将包括在AIM 2.1中使我们的算法适应两个临床MR成像系统(GE和西门子)， 在Aim 2.2中，开发了一种放射病理驱动的MRI协议，用于在组合的MR-LINAC上进行连续成像 该系统是美国仅有的两个运行中的系统之一。该项目的完成将提供一套强大的 为临床医生提供定量成像工具，以改善高危前列腺癌的鉴别诊断和治疗 测量对前列腺癌治疗的反应。