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

基本信息

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
项目类别：
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

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

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项目摘要

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。