Hybrid Intelligence for Trustable Diagnosis And Patient Management of Prostate Cancer (HIT-PIRADS)

用于前列腺癌可信诊断和患者管理的混合智能 (HIT-PIRADS)

• 批准号: 10611212
• 负责人: Ulas Bagci
• 金额: $ 37.69万
• 依托单位: NORTHWESTERN UNIVERSITY AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-22 至 2027-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10611212
• 关键词: Adopted; Adoption; Age; Algorithms; Artificial Intelligence; Artificial Intelligence platform; Benchmarking; Biopsy; Cancer Detection; Cancer Etiology; Cancer Patient; Cancerous; Cessation of life; Classification; Clinic; Clinical; Community Hospitals; Dangerousness; Data; Data Reporting; Data Set; Demography; Detection; Diagnosis; Effectiveness; Evaluation; Expert Systems; Family Cancer History; Genitourinary system; Goals; Guidelines; Histology; Hybrids; Image; Incidence; Information Systems; Intelligence; International; Joints; Laboratories; Lesion; Locales; MRI Scans; Machine Learning; Magnetic Resonance Imaging; Malignant Neoplasms; Malignant neoplasm of prostate; Medical; Metadata; Minority Groups; Morbidity - disease rate; Morphologic artifacts; Nature; Noise; Operative Surgical Procedures; Outcome; Patients; Physicians; Population Heterogeneity; Predictive Value; Prevention strategy; Prostate; Race; Radiology Specialty; Reader; Recommendation; Rectum; Reporting; Reproducibility; Reproducibility of Results; Research; Risk; Role; Scanning; Screening for Prostate Cancer; Source; Standardization; System; Training; Trust; Uncertainty; United States National Institutes of Health; Universities; Variant; Visual; artificial intelligence algorithm; artificial intelligence method; cancer classification; cancer diagnosis; capsule; classification algorithm; clinical imaging; clinically significant; cohort; data acquisition; data curation; design; digital; efficacy validation; experience; high risk; improved; innovation; male; men; mortality; multi-task learning; neural network algorithm; novel; prospective; prostate biopsy; radiological imaging; radiologist; rectal; risk stratification; serum PSA; tool; treatment strategy; trustworthiness

Project Summary/Abstract Prostate Cancer (PCa) is among the most common cancers in men worldwide, with an estimated 1.6M cases and 366K deaths annually [1]. In the US, 11% of men are diagnosed with PCa over their lifetime, with incidence generally rising with age [2]. The Prostate Imaging Reporting and Data System (PI-RADS) has become a standard tool for diagnosing PCa using multi-parametric MR images (mp-MRI). PI-RADS aims to standardize the way to classify the cancer grades. However, PI-RADS does not use clinical and demographic patient information, and MR images are assessed qualitatively or at most semi-quantitatively causing under-detection of dangerous cancer and over-detection of insignificant cancer. This proposal is to develop artificial intelligence (AI) algorithms to improve the detection accuracy by reducing assessment variations and providing trustable predictions. Our algorithms will use diverse population data and eventually a far better evaluation system. This new system will input mp-MRI, clinical (digital rectal exam, PCa family history), demographic (age, race), and laboratory (serum PSA) data to provide risk scores for intraprostatic lesions, and improve patient management for diverse populations. The smart system we will develop is called Hybrid Intelligence and Trustable (HIT)-PIRADS and specific aims of this proposal are three-fold: First, we will develop a new pre-processing framework for enhancing mp-MRI data and minimizing data biases. MRI quality varies significantly, which makes standardization very difficult. To normalize MRI, we will correct artifacts, remove inhomogeneity and noise as the pre-processing step. Next, dataset bias, such as over/under-representation of race will be dealt with as biases cause skewed and inaccurate outcomes. We will examine imbalances and quantify uncertainties in data representation to develop a visual bias-estimation tool (ViBeT) to identify potential biases in the data. Second, we will develop joint segmentation, detection, and classification algorithms for PCa using mp-MRI. Quantification of prostate and PCa is essential for lesion identification, risk stratification, biopsy guidance, and lesion targeting for surgery/focal therapies. We will use our innovative capsule-based neural networks algorithms and extend its strength to analyze mp-MRI and nonimaging data. This step will improve generalization of our algorithms to all risk groups, races, and ages. There will be also an explanation module in the HIT-PIRADAS: we will embed both radiographical interpretations and visual explanations into the baseline HIT-PIRADS. Third, we will evaluate and validate the efficacy of the HIT-PIRADS both retrospectively and prospectively. We will prove the effectiveness of HIT-PIRADS in over 7000 patients’ data (3846 retrospective, 3200 prospective). We will rigorously evaluate sources of variations and standardize HIT-PIRADS for adoption in the clinics. The outcome of this project will be a first-of-its-kind and easy-to-use recommendation system for PCa detection and patient management (HIT-PIRADS) to provide more accurate, unbiased, reproducible results to reduce PCa related morbidity and mortality. In the long term, we expect HIT-PIRADS to be widely adopted in clinics and trigger other treatment & prevention strategies to be developed based on HIT-PIRADS.

项目概要/摘要 前列腺癌 (PCa) 是全球男性最常见的癌症之一，估计有 160 万例病例和 36.6 万例 在美国，每年有 11% 的男性在一生中被诊断出患有前列腺癌，且发病率通常随着年龄的增长而上升。 前列腺影像报告和数据系统 (PI-RADS) 已成为诊断 PCa 的标准工具。 多参数 MR 图像 (mp-MRI) 旨在标准化癌症分级的分类方式，但是，PI-RADS 不使用临床和人口统计患者信息，并且 MR 图像最多进行定性评估。 半定量导致危险癌症的检测不足和微不足道的癌症的过度检测。 该提案旨在开发人工智能（AI）算法，通过减少 我们的算法将使用不同的人口数据并最终提供可靠的预测。 这个新系统将输入 mp-MRI、临床（直肠指检、PCa 家族史）、 人口统计（年龄、种族）和实验室（血清 PSA）数据，以提供前列腺内病变的风险评分，以及 我们将开发的智能系统称为混合智能，改善对不同人群的患者管理。 和可信（HIT）-PIRADS，该提案的具体目标有三个方面： 首先，我们将开发一个新的预处理框架，用于增强 mp-MRI 数据并最大程度地减少 MRI 数据偏差。 质量差异很大，这使得标准化非常困难，为了使 MRI 正常化，我们将纠正伪影、消除伪影。 接下来，数据集偏差（例如种族的过度/代表性不足）将被作为预处理步骤。 当偏见导致偏差和不准确的结果时，我们将检查不平衡情况并量化数据的不确定性。 其次，我们将开发视觉偏差估计工具（ViBeT）来识别数据中的潜在偏差。 使用 mp-MRI 量化前列腺癌和前列腺癌，开发联合分割、检测和分类算法。 PCa 对于病变识别、风险分层、活检指导和手术/局部治疗的病变靶向至关重要。 我们将使用我们创新的基于胶囊的神经网络算法，并扩展其分析 mp-MRI 和非成像数据的能力，这一步将提高我们的算法对所有风险群体、种族和年龄的泛化能力。 HIT-PIRADAS 中的解释模块：我们将嵌入射线照相解释和视觉解释 第三，我们将回顾性地评估和验证 HIT-PIRADS 的有效性。 我们将在超过 7000 名患者的数据（3846 名回顾性患者、3200 名患者）中前瞻性地证明 HIT-PIRADS 的有效性。 我们将严格评估变异来源并对 HIT-PIRADS 进行标准化以供临床采用。 该项目的成果将是首创且易于使用的 PCa 检测和推荐系统 患者管理 (HIT-PIRADS) 提供更准确、公正、可重复的结果，以减少 PCa 相关 从长远来看，我们预计 HIT-PIRADS 将在临床中广泛采用并引发其他治疗方法。 基于HIT-PIRADS制定的预防策略。