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)是世界上最常见的男性癌症之一，估计有160万病例和36.6万人 每年死亡人数[1]。在美国，11%的男性在一生中被诊断为前列腺癌，发病率通常随着 年龄[2]。前列腺成像报告和数据系统(PI-RADS)已成为诊断前列腺癌的标准工具 多参数磁共振成像(MP-MRI)。PI-RADS旨在标准化癌症分级的方法。然而，PI-RADS不使用临床和人口统计学患者信息，MR图像是定性或至多进行评估的 半定量地导致对危险癌症的低估和对微不足道的癌症的过度探测。 这一建议是开发人工智能(AI)算法，以提高检测精度，通过减少 评估差异，并提供可信的预测。我们的算法将使用不同的人口数据，并最终 更好的评估体系。这个新系统将输入MP-MRI、临床(直肠指检、前列腺癌家族史)、 人口统计学(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 改善对不同人群的患者管理。我们将开发的智能系统称为混合智能 和可信赖(HIT)-本提案的PIRADS和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来增强MP-MRI数据，并将数据偏差降至最低。磁共振成像 质量差异很大，这使得标准化变得非常困难。为了使核磁共振正常化，我们将纠正伪影，移除 图像的非均质性和噪声作为前处理步骤。接下来，数据集偏差，如种族表示过高/过低 就像偏见会导致扭曲和不准确的结果一样处理。我们将检查不平衡并量化数据中的不确定性 开发一种视觉偏差估计工具(ViBeT)来识别数据中的潜在偏差。第二，我们将 开发基于MP-MRI的PCA联合分割、检测和分类算法。前列腺癌和前列腺癌的定量 PCA对于病变识别、风险分层、活检指导和手术/局部治疗的病变定位是必不可少的。 我们将使用我们创新的基于胶囊的神经网络算法，并将其扩展到分析MP-MRI和非成像数据。这一步将提高我们的算法对所有风险群体、种族和年龄的普适性。也会有 HIT-PIRADAS中的一个解释模块：我们将嵌入放射解释和视觉解释 进入基准命中率-PIRADS。第三，我们将对HIT-PIRADS的疗效进行回顾性评估和验证 而且是前瞻性的。我们将在超过7000名患者的数据中证明HIT-PIRADS的有效性(3846例回顾，3200例 预期)。我们将严格评估变异来源，并将HIT-PIRADS标准化，以便在临床上采用。 该项目的成果将是第一个同类和易于使用的建议系统，用于PCA检测和 患者管理(HIT-PIRADS)提供更准确、无偏见、可重复性的结果，以减少与PCA相关的结果 发病率和死亡率。从长远来看，我们预计HIT-PIRADS将在临床上广泛采用，并引发其他治疗 &将根据HIT-PIRADS制定预防战略。