Integrating Quantitative MRI and Artificial Intelligence to Improve Prostate Cancer Classification

项目总结 前列腺癌(PCa)在16%的男性中发生，是与癌症相关的第二大原因 美国男性死亡人数。虽然发病率很高，但前列腺癌呈现出广泛的攻击性 而且在许多情况下不会发展成威胁生命的侵袭性癌症。当前的诊断策略可能 未能发现所有具有临床意义的前列腺癌病例，并且准确区分临床的能力有限 由于信息不完整和不一致，导致懒惰的主成分分析产生重大影响。这不仅使患者受到 有害的并存，包括治疗过度和治疗不足，但也加剧了本已显著的 医疗保健成本。因此，临床迫切需要实现准确的检测和分类。 评估具有临床意义的前列腺癌，并确定适当的管理策略。 多参数磁共振成像(MP-MRI)，包括T2加权、扩散加权和动态增强 成像，已成为PCa非侵入性检测和分级的首选成像技术。 然而，目前MP-MRI的标准化评分系统，前列腺成像报告和数据系统 (PI-RADS)v2，区分惰性和临床有意义的PCa的能力有限，具有敏感性 特异性在60%~85%之间。这种次优的精确度和相当大的性能差异是 主要是因为目前的PI-RADS评分是基于定性分析和主观解释的 MP-MRI，被扫描仪和患者特定的变异所混淆，包括B1+不均质性、动脉输入 功能，以及磁化率和涡流效应。 该建议旨在通过建立一种新的基于MRI的方法来克服当前MP-MRI的这些关键限制 基于两个协同创新的人工智能：1)新的定量动态增强MRI 分析技术和扩散加权MRI采集方法，最大限度地减少扫描仪和患者的特定 2)新颖的多类深度学习模型，可以完全集成多标签定量MP-BP模型。 核磁共振信息。通过利用现有MP-MRI数据和要获取的定量MP-MRI数据之间的协同作用， MRI数据与随后绘制的所有病变的整体组织病理学图，建议基于MRI的深部 将对学习模型进行评估，以检测和分类具有临床意义的PCA，与 目前的护理标准，PI-RADS v2。 该项目的完成将导致创建、临床部署和关键验证新的基于MRI的 人工智能实现了对临床上有意义的疾病的检测和分类的前所未有的准确性 PCA，从而增加了将惰性PCA与显著的PCA分开的置信度，并减少了不必要的 活组织检查、治疗不足和治疗过度。

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.