Cyst-X: Interpretable Deep Learning Based Risk Stratification of Pancreatic Cystic Tumors

Cyst-X：基于可解释深度学习的胰腺囊性肿瘤风险分层

基本信息

批准号：
10689657
负责人：
Ulas Bagci
金额：
$ 48.87万
依托单位：
NORTHWESTERN UNIVERSITY AT CHICAGO
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-03-01 至 2025-02-28
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10689657
关键词：
Academic Medical Centers Algorithms Artificial Intelligence Biopsy Cancer Etiology Cessation of life Characteristics Clinic Clinical Colon Carcinoma Colonic Polyps Cyst Cystic Neoplasm Data Data Set Detection Development Diagnosis Diagnostic Disease Early Diagnosis Epithelial cyst European Evaluation Excision Goals Guidelines High Prevalence Histopathology Image In Situ Lesion Individual International Lesion MRI Scans Magnetic Resonance Imaging Malignant - descriptor Malignant Neoplasms Malignant neoplasm of pancreas Medical center Methods Modeling Morbidity - disease rate Mucinous Cystadenoma Mucinous Neoplasm Multicenter Studies Names Noninfiltrating Intraductal Carcinoma Operative Surgical Procedures Organ Outcome Outcomes Research Pancreas Pancreatectomy Pancreatic Cyst Pancreatic cystic neoplasia Papillary Patient Triage Patient-Focused Outcomes Patients Performance Prognosis Property Radiology Specialty Recommendation Reference Standards Research Risk Scanning Sensitivity and Specificity Series Serous Cystadenoma Side Structure Surveillance Program Survival Rate System Technology Testing Time Trust United States Universities Unnecessary Surgery Visual automated algorithm cancer invasiveness cancer type capsule clinical center clinical decision-making cost deep learning deep learning algorithm design detection method detection platform diagnostic accuracy diagnostic tool efficacy validation experimental study follow-up high risk improved learning strategy malignant breast neoplasm mortality novel diagnostics pancreatic neoplasm premalignant prognostic significance radiological imaging radiologist radiomics risk stratification screening stem tool

项目摘要

Project Summary The overall goal of this project is to develop a new diagnostic tool, called Cyst-X, for accurate detection and characterization of pre-cancerous pancreatic cysts and improve patient outcome through precise decisions (surgical resection or surveillance). Pancreatic cancer is the most fatal cancer among all cancers due to its poor prognosis and lack of early detection methods. Unlike other common cancers where precursor lesions are well known (colon polyps-colon cancer, ductal carcinoma in situ (DCIS)-breast cancer), pancreas cancer precursors (cysts) are poorly understood. Diagnosing pancreatic cancer at earlier stages may decrease mortality and morbidity rates of this lethal disease. One major approach for diagnosing pancreatic cancer at earlier stages is to target pancreatic precancerous pancreatic neoplasms (cysts) before they turn into invasive cancer. Once cysts are detected with radiology imaging such as magnetic resonance imaging (MRI), they should be characterized with respect to their malignant potential. Low-risk cysts remain harmless; hence, patients should remain under surveillance program. On the other hand, high-risk cysts can progress into an aggressive cancer, therefore, patients should undergo surgical resection if possible. Despite this, international guidelines for risk stratification of pancreatic cysts are woefully deficient (55-76% accuracy for determining characteristics of low-risk vs high risk cystic tumors, while only 40-50% accuracy detecting cysts with MRI). Combined, these critical barriers indicate that there is an urgent need for improving characterization of pancreatic cystic tumors. Based on our preliminary results, which support the development of an image-based diagnostic decision tool, we hypothesize that our proposed Cyst-X will produce higher diagnostic accuracy for characterizing pancreatic cysts and provide better patient management compared to the current guidelines. Towards this overarching hypothesis, we will first use powerful deep learning methods (specifically deep capsule networks) for automatically detecting and segmenting the pancreas and pancreatic cysts from multi-sequence MRI scans (Aim 1). Next, we will create an interpretable image-based diagnosis model for characterizing pancreatic cysts (Aim 2). Accurate characterization is necessary for such a diagnostic model; however, emphasis will also be placed on interpretability of the machine generated diagnostic model. Visual explanation of the discriminative features will help radiologists obtain higher decision rates in patient management. In Aim 3, we will validate the proposed Cyst-X framework in a multi-center study. A total of 1200 multi-sequence MRI scans will be collected from three participating clinical centers (Mayo Clinic, Columbia University Medical Center, Erasmus Medical Center). Comprehensive evaluations will be made to test the validity and generalizability of Cyst-X. All evaluations will be made with respect to the international guidelines and biopsy proven ground truths. Our proposed study has wide implications: specifically, in the long term, it will influence early diagnosis of pancreatic cancer and clinical decision making to improve survival rates of pancreatic cancer.

项目概要该项目的总体目标是开发一种新的诊断工具，称为 Cyst-X，用于准确检测和癌前胰腺囊肿的特征并通过精确的决策改善患者的治疗结果（手术切除或监测）。胰腺癌因其恶性程度低而成为所有癌症中死亡率最高的癌症预后和缺乏早期检测方法。与其他常见癌症不同的是，其前兆病变良好已知（结肠息肉-结肠癌、导管原位癌 (DCIS)-乳腺癌）、胰腺癌前兆（囊肿）人们知之甚少。早期诊断胰腺癌可能会降低死亡率这种致命疾病的发病率。早期诊断胰腺癌的一种主要方法是在胰腺癌前肿瘤（囊肿）转变为浸润性癌症之前将其靶向治疗。一旦出现囊肿通过放射学成像（例如磁共振成像（MRI））检测到，应对其进行表征关于它们的恶性潜力。低风险囊肿仍然无害；因此，患者应保持在监视计划。另一方面，高风险囊肿可能发展成侵袭性癌症，因此，如果可能的话，患者应接受手术切除。尽管如此，风险分层的国际指南的胰腺囊肿严重缺乏（确定低风险与高风险特征的准确度为 55-76%）风险囊性肿瘤，而 MRI 检测囊肿的准确率只有 40-50%）。综合起来，这些关键障碍表明迫切需要改善胰腺囊性肿瘤的特征。基于我们的我们假设初步结果支持基于图像的诊断决策工具的开发我们提出的 Cyst-X 将为表征胰腺囊肿提供更高的诊断准确性，并提供与现行指南相比，更好的患者管理。为了实现这一总体假设，我们将首先使用强大的深度学习方法（特别是深度胶囊网络）来自动检测和从多序列 MRI 扫描中分割胰腺和胰腺囊肿（目标 1）。接下来，我们将创建一个用于表征胰腺囊肿的可解释的基于图像的诊断模型（目标 2）。准确的对于这样的诊断模型来说，表征是必要的；然而，还将重点放在机器生成的诊断模型的可解释性。区分特征的视觉解释将帮助放射科医生在患者管理中获得更高的决策率。在目标 3 中，我们将验证提议的多中心研究中的 Cyst-X 框架。将从三个中心收集总共 1200 个多序列 MRI 扫描参与临床中心（梅奥诊所、哥伦比亚大学医学中心、伊拉斯谟医学中心）。将进行综合评估以测试Cyst-X的有效性和普遍性。所有评价将根据国际准则和活检证实的基本事实制定。我们提出的研究具有广泛的影响：具体而言，从长远来看，它将影响胰腺癌的早期诊断和临床提高胰腺癌生存率的决策。