Integrating Quantitative MRI and Artificial Intelligence to Improve Prostate Cancer Classification

项目摘要 前列腺癌（PCa）发生在16%的男性中，是癌症相关疾病的第二大原因。 在美国男性死亡。虽然发病率很高，但PCa表现出广泛的侵袭性 并且在许多情况下不会发展成威胁生命的侵袭性癌症。目前的诊断策略可能 未能检测到临床显著PCa的所有情况，并且准确区分临床 由于信息不完整和不一致，惰性PCa显著。这不仅使患者 有害的合并症，包括过度治疗和治疗不足，但也加剧了已经显着 医疗费用。因此，临床上迫切需要实现准确的检测和分类 具有临床意义的前列腺癌并确定适当的管理策略。 多参数MRI（mp-MRI），包括T2加权、弥散加权和动态对比增强 成像已经成为用于PCa的非侵入性检测和分级的优选成像技术。 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区分无痛性和临床显著PCa的能力有限，敏感性 特异性为60- 85%。这种次优的精度和性能上的相当大的变化， 主要是由于目前的PI-RADS评分基于定性分析和主观解释 mp-MRI，受到扫描仪和患者特异性变化的混淆，包括B1+不均匀性、动脉输入 函数、磁化率和涡流效应。 该提案旨在通过建立一种新的基于MRI的 基于两项协同创新的人工智能：1）新的定量动态对比增强MRI 分析技术和弥散加权MRI采集方法，最大限度地减少扫描仪和患者特异性 变体，以及2）新颖的多类深度学习模型，其可以完全整合多标记的定量MP， MRI信息。通过利用现有mp-MRI数据和待获取的定量mp-MRI数据之间的协同作用， MRI数据以及随后在整体组织病理学上对所有病变进行的标测， 将评估学习模型对临床显著PCa的检测和分类，与 当前标准治疗，PI-RADS v2。 该项目的完成将导致新的基于MRI的 人工智能，实现了前所未有的准确性，用于检测和分类临床显著 PCa，从而增加将惰性PCa与显著PCa分离的置信度，并减少不必要的PCa。 活组织检查治疗不足和过度治疗

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.