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

项目摘要 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， 通过精确的决策来表征癌前胰腺囊肿并改善患者预后 （手术切除或监视）。胰腺癌是所有癌症中最致命的癌症，因为它的穷人 预后差和缺乏早期检测方法。不像其他常见的癌症， 已知（结肠息肉-结肠癌、导管原位癌（DCIS）-乳腺癌）、胰腺癌前体 （囊肿）知之甚少。早期诊断胰腺癌可以降低死亡率， 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 针对胰腺癌前胰腺肿瘤（囊肿），在它们变成浸润性癌症之前。一旦囊肿 通过放射学成像（如磁共振成像（MRI））检测，应对其进行表征 关于它们的恶性潜能。低风险囊肿仍然无害;因此，患者应继续接受治疗 监视程序另一方面，高风险的囊肿可以进展为侵袭性癌症，因此， 如有可能，病人应接受手术切除。尽管如此，国际风险分层指南 胰腺囊肿的低危与高危特征的诊断准确率为55- 76 风险囊性肿瘤，而只有40-50%的准确性检测囊肿与MRI）。这些关键障碍加在一起 表明迫切需要改善胰腺囊性肿瘤特征。基于我们 初步结果，支持基于图像的诊断决策工具的发展，我们假设 我们提出的Cyst-X将产生更高的诊断准确性，为胰腺囊肿的特征，并提供 与现行指南相比，患者管理更好。对于这个总体假设，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 表征是必要的，这样一个诊断模型;然而，重点也将放在 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，我们将验证所提出的 多中心研究中的Cyst-X框架。将从三个区域收集总计1200个多序列MRI扫描。 参与临床中心（马约诊所、哥伦比亚大学医学中心、伊拉斯谟医学中心）。 对Cyst-X的有效性和可推广性进行综合评价。所有的评价都将 根据国际指南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：具体来说，从长远来看，它将影响胰腺癌的早期诊断和临床 提高胰腺癌的生存率。

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.