Integrating Quantitative MRI and Artificial Intelligence to Improve Prostate Cancer Classification

整合定量 MRI 和人工智能以改进前列腺癌分类

• 批准号: 10360679
• 负责人: Kyung Hyun Sung
• 金额: $ 53.41万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-03-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10360679
• 关键词: Area; Artificial Intelligence; Biopsy; Cancer Etiology; Cancer Patient; Cessation of life; Classification; Clinical; Data; Decision Making; Detection; Diffusion; Diffusion Magnetic Resonance Imaging; Gleason Grade for Prostate Cancer; Goals; Health Care Costs; Histologic; Histopathology; Image; Image Enhancement; Imaging Techniques; Incidence; Indolent; Information Systems; Interobserver Variability; Label; Lead; Lesion; Life; Magnetic Resonance Imaging; Malignant Neoplasms; Malignant neoplasm of prostate; Methods; Modeling; Non-Invasive Cancer Detection; Patients; Performance; Phase; Predisposition; Prostate; ROC Curve; Radiology Specialty; Reporting; Reproducibility; Research Project Grants; Scanning; Sensitivity and Specificity; Standardization; System; Techniques; Technology; Texture; Uncertainty; United States; Validation; Variant; artificial intelligence algorithm; base; cancer classification; clinically significant; comorbidity; contrast enhanced; convolutional neural network; deep learning model; design; diagnostic strategy; diffusion weighted; imaging modality; improved; innovation; machine learning classification; male; men; novel; overtreatment; prisma; prospective; prostate biopsy; standard of care; synergism

PROJECT SUMMARY Prostate cancer (PCa) develops in sixteen percent of males and is the second leading cause of cancer-related death in men in the United States. While incidence is high, PCa presents with a wide range of aggressiveness and in many cases does not develop into life-threatening aggressive cancer. Current diagnostic strategies may fail to detect all instances of clinically significant PCa and have limited ability to accurately distinguish clinically significant from indolent PCa due to incomplete and inconsistent information. This not only subjects patients to detrimental co-morbidities including overtreatment and undertreatment, but also exacerbates already significant healthcare costs. Consequently, there is an urgent clinical need to achieve accurate detection and classification of clinically significant PCa and determine the appropriate management strategy. Multi-parametric MRI (mp-MRI), consisting of T2-weighted, diffusion-weighted, and dynamic contrast-enhanced imaging, has emerged as the preferred imaging technique for non-invasive detection and grading of PCa. However, the current standardized scoring system for mp-MRI, Prostate Imaging Reporting and Data System (PI-RADS) v2, has limited ability to distinguish between indolent and clinically significant PCa, with sensitivity and specificity in the range of 60-85%. This suboptimal accuracy and considerable variation in performance is mainly due to the fact that current PI-RADS scoring is based on qualitative analysis and subjective interpretation of mp-MRI, confounded by scanner- and patient-specific variations, including B1+ inhomogeneity, arterial input function, and susceptibility and eddy current effects. This proposal aims to overcome these critical limitations of current mp-MRI by establishing a new MRI-based artificial intelligence based on two synergistic innovations: 1) new quantitative dynamic contrast-enhanced MRI analysis techniques and diffusion-weighted MRI acquisition methods that minimize scanner- and patient-specific variations, and 2) novel multi-class deep learning models that can fully integrate the multi-labeled quantitative mp- MRI information. By leveraging the synergy between existing mp-MRI data and to-be-acquired quantitative mp- MRI data with subsequent mapping of all lesions at whole-mount histopathology, the proposed MRI-based deep learning model will be evaluated for detection and classification of clinically significant PCa, compared with the current standard-of-care, PI-RADS v2. Completion of this project will lead to the creation, clinical deployment, and pivotal validation of a new MRI-based artificial intelligence that achieves unprecedented accuracy for detection and classification of clinically significant PCa, thereby increasing confidence in separating indolent PCa from significant PCa and reducing unnecessary biopsies, undertreatment, and overtreatment.

项目总结