Image Analysis Tools for mpMRI Prostate Cancer Diagnosis Using PI-RADS

使用 PI-RADS 进行 mpMRI 前列腺癌诊断的图像分析工具

• 批准号: 10256757
• 负责人: John Aaron Onofrey
• 金额: $ 80.31万
• 依托单位: EIGEN HEALTH SERVICES LLC
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-05-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10256757
• 关键词: Accounting; Address; Agreement; Algorithms; Atlases; Benign; Biopsy; Cancer Diagnostics; Cancer Etiology; Categories; Cessation of life; Classification; Clinical; Collaborations; Communication; Computer Assisted; Consumption; Data; Data Collection; Databases; Diagnosis; Diagnostic; Diffusion; Diffusion Magnetic Resonance Imaging; Environment; Goals; Image; Image Analysis; Image Enhancement; Information Systems; Interobserver Variability; Intraobserver Variability; Label; Lesion; Letters; Localized Lesion; Magnetic Resonance Imaging; Malignant - descriptor; Malignant Neoplasms; Malignant neoplasm of prostate; Manuals; Maps; Meta-Analysis; Methodology; Methods; Modeling; Monitor; Multimodal Imaging; Performance; Phase; Procedures; Process; Prostate; Protocols documentation; Psyche structure; Radiologic Finding; Radiology Specialty; Reader; Reading; Reporting; Reproducibility; Research; Site; Source; Specific qualifier value; Specificity; Staging; Standardization; System; T2 weighted imaging; Time; Training; Validation; Variant; Work; Workload; base; cancer diagnosis; clinical decision support; clinical diagnostics; clinical imaging; clinical practice; clinical research site; commercialization; contrast enhanced; deep learning; design; disease heterogeneity; image guided; image registration; imaging biomarker; improved; innovation; machine learning algorithm; machine learning method; medical specialties; men; mortality; multimodality; novel; prostate biopsy; radiological imaging; radiologist; research clinical testing; support tools; tool

Project Summary Prostate cancer is one of the most commonly occurring forms of cancer, accounting for 21% of all cancer in men. The Prostate Imaging Reporting and Data System (PI-RADS) aims to standardize reporting of prostate cancer using multi-parametric magnetic resonance imaging (mpMRI). However, the in-depth analysis, as demanded by PI-RADS, remains challenging due to the complexity and heterogeneity of the disease, and it is a clinically burdensome task subject to both significant intra- and inter-reader variability. Auxiliary tools based on machine learning methods such as deep learning can reduce diagnostic variability and increase workload efficiency by automatically performing tasks and presenting results to a radiologist for the purpose of decision support. In particular, automated identification and classification of lesion candidates using imaging data can be performed with respect to PI-RADS scoring. In Phase I of this project, we developed two automated methods to reduce the intra- and inter-observer variability while interpreting mpMRI images using the PI-RADS protocol: (i) a method to co-register mpMRI data, and (ii) a method to geometrically segment the prostate gland into the PI-RADS protocol sector map. The overarching goal of this Phase II project is to develop machine learning algorithms that incorporate both co-registered multi-modal imaging biomarkers and PI-RADS sector map information into an automated clinical diagnostic aid. The innovation in this project lies in the use of deep learning to automatically predict PI-RADS classification. This project is significant in that it has the potential to improve clinical efficiency and reduce diagnostic variation in prostate cancer diagnosis. In Aim 1 of this project, we will develop a deep learning approach to localize and classify lesions in mpMRI. In Aim 2, we will integrate this diagnostic tool into the ProFuseCAD system and perform rigorous multi-site validation to quantify PI-RADS classification performance. Both aims will utilize a database of over 1,000 existing mpMRI images from multiple clinical sites to develop and validate the algorithms. Ultimately, enhancements from this project will create a novel feature for Eigen's (the applicant company's) FDA 510(k)-cleared imaging product, ProFuseCAD, in order to improve the diagnosis and reporting of prostate cancer.

项目摘要 前列腺癌是最常见的癌症形式之一，占男性所有癌症的21％。 前列腺成像报告和数据系统（PI-RADS）旨在标准化前列腺癌的报告 使用多参数磁共振成像（MPMRI）。但是，按要求的深入分析 由于疾病的复杂性和异质性，pi-rads仍然受到挑战，这是临床上的 负担重大的任务均受到显着的内部和读取器变异性的约束。基于机器的辅助工具 诸如深度学习之类的学习方法可以减少诊断可变性，并通过 自动执行任务并将结果呈现给无线电主义者，以实现决策支持的目的。在 可以使用成像数据对病变候选物进行特殊的自动识别和分类 关于Pi-Rads得分。在该项目的第一阶段，我们开发了两种自动化方法来减少 使用PI-RADS协议解释MPMRI图像时观察者内和观察者的可变性：（i）一种方法 共同注册mPMRI数据，（ii）几何将前列腺分割到Pi-Rads中的方法 协议扇区图。该第二阶段项目的总体目标是开发机器学习算法 将共同注册的多模式成像生物标志物和Pi-Rads扇区映射信息纳入一个 自动临床诊断辅助工具。该项目的创新在于使用深度学习自动 预测Pi-Rads分类。该项目显着，因为它有可能提高临床效率 并减少前列腺癌诊断的诊断变化。在该项目的目标1中，我们将发展一个深刻的 在mpMRI中局部和分类病变的学习方法。在AIM 2中，我们将将此诊断工具集成到 PROFUSECAD系统并执行严格的多站点验证，以量化Pi-Rads分类性能。 这两个目标都将利用来自多个临床站点的1,000多个现有MPMRI图像的数据库来开发和 验证算法。最终，该项目的增强功能将为eigen创造一个新颖的功能（ 申请人公司）FDA 510（k）切实成像产品，ProfusEcad，以改善诊断和 报道前列腺癌。