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: 10066138
• 负责人: Peter S LaViolette
• 金额: $ 63.2万
• 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/10066138
• 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.

抽象的 前列腺癌是最常见的非乳腺癌癌症，影响了七分之一的男性。甚至 当用根治性前列腺切除术治疗时，历史上约有20％的患者出现肿瘤复发。这 提案将集中于两个单独的免费数据集的集成，以更好地区分高风险 患者：多参数磁共振成像（MP-MRI）和整个安装后术后前列腺 病理样本。我们将开发能够预测潜在的悲伤的无线电行为算法 从非侵入性成像的特征是为了区分具有高转移性潜力的前列腺癌。我们的 总体假设是前列腺癌的微观，异质的致病特征是可靠的 可检测和用宏观定量MP-MRI进行量化。这些功能无创映射将 为将侵略性与顽固的前列腺癌区分开来提供临床上有用的工具，并有可能 用辐射靶向。 该提案包括两个特定目标，以响应Par-19-264中概述的目标。特定于 资金机会公告：AIM 1将开发定义成像的无线电方法 - 基于能够将侵略性与顽固前列腺癌区分开的生物标志物。这将在 AIM 1.1的微观水平与组织学，然后在MACROSCOPIC水平上使用MP-MRI在AIM 1.2中。目标1.3 将通过故意扰动系统和算法来测试无线电算法的弹性。 在AIM 1.4中将RAD-PATH数据集与临床变量相结合，以提高灵敏度和特异性 为了提前检测和差异诊断，通过将我们的无线电图与其他OMICS相关联。 此外，AIM 1中包括的是旨在进一步建立鲁棒性的广泛验证实验 无线电神经算法的。在AIM 2中，该项目将将无线电算法转化为诊所。 这将包括在AIM 2.1中将我们的算法调整为两个临床MR成像系统（GE和SIEMENS）， 在AIM 2.2中，开发了无线电驱动的MRI协议，用于在合并的MR-LINAC上进行串行成像 系统是美国仅有的两个运营之一。该项目的完成将提供一组强大的 临床医生的定量成像工具改善了高危前列腺癌的分化和 测量对前列腺癌疗法的反应。