Predicting Outcomes for Uterine Fibroid Embolization by using Deep Learning of Paired MRI Scans

使用配对 MRI 扫描的深度学习预测子宫肌瘤栓塞的结果

• 批准号: 10724513
• 负责人: Bobak Mosadegh
• 金额: $ 46.61万
• 依托单位: WEILL MEDICAL COLL OF CORNELL UNIV
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-21 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10724513
• 关键词: Ablation; Accounting; Adoption; African American; African American population; Aftercare; Algorithms; Benign; Biomedical Engineering; Characteristics; Clinical; Clinical Trials; Communities; Conventional Surgery; Data; Data Set; Databases; Diagnostic; Disease; Economic Burden; Effectiveness; Ethnic Origin; Fertility; Fibroid Tumor; Financial cost; Goals; Healthcare Systems; High Prevalence; Hysterectomy; Insurance Coverage; Interdisciplinary Study; Lead; MRI Scans; Machine Learning; Magnetic Resonance Imaging; Manuals; Measures; Menopause; Methods; Minority; Minority Groups; Modeling; National Institute of Child Health and Human Development; Operative Surgical Procedures; Outcome; Output; Patient-Focused Outcomes; Patients; Performance; Physicians; Positioning Attribute; Postoperative Period; Prejudice; Procedures; Race; Radiology Specialty; Recovery; Reporting; Risk; Scanning; Screening procedure; Surveys; Symptoms; Therapeutic Embolization; Tissues; Training; Treatment Effectiveness; Ultrasonography; Uterine Fibroids; Uterine myomectomy; Vascularization; Woman; Work; autoencoder; combat; cost; cost effective treatment; deep learning; deep learning model; follow-up; insight; learning strategy; low socioeconomic status; machine learning method; minimally invasive; minority patient; neglect; noninvasive diagnosis; novel; outcome prediction; predictive modeling; radiologist; radiomics; reduce symptoms; screening; side effect; success; tool; tumor

PROJECT SUMMARY Uterine fibroids represent the highest prevalence of benign tumors in women, with reports ranging anywhere from 4.5% to 68.6%, with a significant bias towards African American women. It is estimated that the economic burden on the healthcare system from symptomatic women with uterine fibroids is up to $34 million. Currently, uterine fibroid embolization (UFE) is considered a highly effective minimally invasive procedure with up to an 85% success rate. However, hysterectomies are the most commonly performed procedures, accounting for nearly 600,000 annually, while only 14,000 UFE procedures are performed annually. It has been well documented that minorities are less likely to be referred for minimally invasive procedures, even though there is universal insurance coverage for them. Furthermore, women in lower socio-economic status, particularly African Americans, have been disproportionately referred for open surgery. Therefore, automated tools, like the ones in this proposal, that can provide unbiased referrals will be significant advantage at combating this unfortunate bias. This proposal will specifically explore the use of machine learning and deep learning methods to leverage a novel retrospective dataset that compiles features extracted from paired pre-operative and post-operative magnetic resonance imaging (MRI) scans of up to 700 patients who underwent a UFE. These models will provide a UFE treatment effectiveness score that will provide an objective and quantitative metric to decide whether a patient is good candidate for UFE. The short term impact of this proposal will be the creation of a curated database of paired UFE MRI scans that have been analyzed for various metrics regarding fibroid positions and patient characteristics, that will allow the clinical community to begin providing quantitative methods to determine UFE candidates. The long-term impact of this proposal will be realized in subsequent clinical trials that validate these AI models properly to predict which patients should be leveraging UFE as a non-surgical alternative for treating fibroids.

项目摘要 子宫肌瘤代表女性良性肿瘤的最高患病率，报告范围在任何地方 从4.5％到68.6％，对非裔美国妇女有很大的偏见。据估计，经济 有症状的子宫肌瘤女性的医疗保健系统负担高达3400万美元。 目前，子宫肌瘤栓塞（UFE）被认为是一种高效的最低侵入性手术 成功率最高85％。但是，子宫切除术是最常见的程序， 每年占近600,000个，而每年仅执行14,000个UFE程序。它一直 有充分的文献证明，少数族裔不太可能被转介给最低侵入性程序，即使 他们有通用的保险范围。此外，处于社会经济状况较低的妇女，特别是 非洲裔美国人的开放手术不成比例地转诊。因此，自动化工具，例如 在此提案中，可以提供公正的转介将是对抗这一问题的重要优势 不幸的偏见。 该建议将专门探讨机器学习和深度学习方法的使用来利用 新型回顾性数据集编译了从配对前和术后提取的特征 磁共振成像（MRI）扫描多达700名接受UFE的患者。这些模型将提供 UFE治疗有效性得分将提供一个客观和定量指标，以决定是否a 患者是UFE的好候选人。 该提案的短期影响将是创建配对UFE MRI扫描的策划数据库 已经分析了有关肌瘤位置和患者特征的各种指标，这将允许 临床社区开始提供定量方法来确定UFE候选者。长期 该提案的影响将在随后的临床试验中实现，这些试验适当验证这些AI模型 预测哪些患者应利用UFE作为治疗肌瘤的非手术替代方法。