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.

项目摘要/摘要 前列腺癌（PCA）是全球男性最常见的癌症之一，估计有160万例和366K 每年死亡[1]。在美国，有11％的男性在一生中被诊断出患有PCA，事件通常会增加 年龄[2]。前列腺成像报告和数据系统（PI-RADS）已成为使用PCA的标准工具 多参数MR图像（MP-MRI）。 Pi-Rads旨在标准化对癌症等级进行分类的方式。但是，Pi-Rads不使用临床和人口统计患者信息，并且MR图像被定性评估或最多评估 半定量性导致危险癌症的检测不足和无关紧要的癌症的过度检测。 该建议是开发人工智能（AI）算法以通过降低来提高检测准确性 评估变化并提供可信赖的预测。我们的算法将使用潜水员人口数据，并最终使用 更好的评估系统。这个新系统将输入MP-MRI，临床（数字直肠考试，PCA家族史）， 人口统计学（年龄，种族）和实验室（血清PSA）数据可为肉体内病变提供风险评分，并且 改善潜水员人群的患者管理。我们将开发的智能系统称为混合智能 以及可信赖的（命中） - Priprads和该提案的具体目的是三个方面： 首先，我们将开发一个新的预处理框架，以增强MP-MRI数据并最大程度地减少数据偏见。 MRI 质量差异很大，这使标准化非常困难。为了使MRI归一化，我们将纠正工件，删除 不均匀性和噪声作为预处理步骤。接下来，数据集偏见，例如种族的超额代表性不足 处理偏见会导致偏斜和不准确的结果。我们将检查数据中的不平衡并量化不确定性 为开发视觉偏见估计工具（vibet）的表示形式，以识别数据中的潜在偏见。第二，我们会的 使用MP-MRI的PCA开发关节分割，检测和分类算法。量化前列腺和 PCA对于病变识别，风险分层，活检指导和针对手术/局灶性疗法的病变靶向至关重要。 我们将使用基于创新胶囊的中性网络算法并扩展其强度以分析MP-MRI和非成像数据。此步骤将改善我们对所有风险群体，种族和年龄段的算法的概括。也会有 命中式PIRADAS中的一个解释模块：我们将嵌入射线图解释和视觉解释 进入基线命中率。第三，我们将回顾性地评估和验证命中率的效率 并前瞻性。我们将证明在7000多名患者数据中命中率的有效性（3846回顾性，3200 预期）。我们将严格评估变化的来源，并标准化命中率，以在诊所采用。 该项目的结果将是用于PCA检测和易于使用的推荐系统的首要方式 患者管理（HIT-PIRADS）可提供更准确，无偏，可重现的结果，以减少与PCA相关的 发病率和死亡率。从长远来看，我们预计命中率将在诊所中广泛采用，并触发其他治疗 ＆预防策略将根据命中率开发。