Deep radiomic decision support system for colorectal cancer

结直肠癌深度放射组学决策支持系统

• 批准号: 9764151
• 负责人: HIROYUKI YOSHIDA
• 金额: $ 46.17万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-15 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9764151
• 关键词: 3-Dimensional; Address; Adoption; Anatomy; Big Data; Biopsy; Cancer Etiology; Carcinoma; Categories; Cessation of life; Clinical; Colonoscopy; Colorectal; Colorectal Cancer; Computed Tomographic Colonography; Computer Simulation; Databases; Decision Support Systems; Detection; Development; Diagnosis; Diagnostic; Early Diagnosis; Evaluation; Goals; Guidelines; Human; Image; Lesion; Location; Machine Learning; Medical Imaging; Methods; Molecular; Multi-Institutional Clinical Trial; Optics; Pathway interactions; Performance; Phenotype; Prevention; Problem Solving; Process; Psychological Transfer; Reader; Retrieval; Safety; Scheme; Sensitivity and Specificity; Shapes; Specificity; System; Testing; Texture; Time; Training; United States; adenoma; base; cancer diagnosis; colorectal cancer prevention; computer aided detection; convolutional neural network; cost; deep learning; improved; innovation; minimally invasive; mortality; radiologist; radiomics; screening; success

Project Summary/Abstract Computer-aided detection (CADe) has been shown to increase readers’ sensitivity and reduce inter-observer variance in detecting abnormalities in medical images. However, they prompt relatively large numbers of false positives (FPs) that readers find tedious to review and, during this process, the readers can incorrectly dismiss true lesions prompted correctly to them by CADe systems. Thus, there is a demand for an advanced decision support system that would provide not only high detection sensitivity, but also high specificity while being able to explain why a specific location was prompted as a lesion. In this project, we propose to improve the detection specificity of CADe by deep convolutional neural networks (DCNNs) that can analyze the extrinsic radiomic phenotype, such as the context of local anatomy, of target lesions, whereas current CADe systems consider only the intrinsic radiomic phenotype, such as the shape and texture of detected lesions. Further, we can use DCNNs to provide an explanation of why a specific location was prompted by using anatomically meaningful object categories with similar-image retrieval of past diagnosed cases. In this project, we will focus on computed tomographic colonography (CTC), which is a minimally invasive screening method for early detection of colorectal lesions to prevent colorectal cancer (CRC), which is the second leading cause of cancer deaths in the United States. Historically, however, only adenomas were believed to be precursors of CRC. Recent studies have revealed a molecular pathway where also serrated lesions can develop into CRC. Recent studies have indicated that CTC can detect serrated lesions accurately based upon the phenomenon called contrast coating. Thus, the goal of this project is to develop a deep radiomic decision support (DeepDES) system that leverages deep learning for providing high sensitivity and specificity in the detection of colorectal lesions, in particular, serrated lesions, and for providing diagnostic information that explains why a specific location was prompted as a lesion to assist readers in assessing detected lesions correctly. To achieve the goal, we will explore the following specific aims: (1) Develop a radiomic deep-learning (RAID) scheme for the detection of colorectal lesions, (2) develop a DeepDES system for diagnosis of detected lesions, and (3) evaluate the clinical benefit of DeepDES system. Successful development of the proposed DeepDES system will provide an advanced decision support that addresses the current concerns about CADe by yielding both high detection sensitivity and high specificity while being able to explain why a specific location was prompted as a target lesion. Broad adoption and use of the DeepDES system will advance the prevention and early diagnosis of cancer, and thus will ultimately reduce mortality from colorectal cancer in the United States.

项目总结/摘要 计算机辅助检测（CADe）已被证明可以提高读者的灵敏度并减少观察者之间的差异 检测医学图像中的异常的方差。然而，他们提示相对大量的虚假 读者会觉得审查起来很乏味，在此过程中，读者可能会错误地忽略这些积极因素（FP） CADe系统正确提示的真实病变。因此，需要提前作出决定 不仅提供高检测灵敏度，而且提供高特异性，同时能够 以解释为什么特定位置被提示为病变。在这个项目中，我们建议改善 通过深度卷积神经网络（DCNN）检测CADe的特异性，该网络可以分析外部 放射组学表型，例如靶病变的局部解剖结构的背景，而当前的CADe系统 仅考虑内在放射组学表型，例如检测到的病变的形状和纹理。我们还 可以使用DCNN来解释为什么使用解剖学提示特定位置 有意义的对象类别与过去诊断病例的相似图像检索。在这个项目中，我们将重点 CT结肠成像（CTC），这是一种微创筛查方法， 检测结直肠病变以预防结直肠癌（CRC），这是癌症的第二大原因 死亡在美国。然而，历史上只有腺瘤被认为是CRC的前体。 最近的研究揭示了锯齿状病变也可以发展成CRC的分子途径。最近 研究表明，CTC可以根据称为 对比涂层因此，该项目的目标是开发一个深度放射性决策支持（DeepDES） 该系统利用深度学习在结直肠癌检测中提供高灵敏度和特异性 病变，特别是锯齿状病变，并提供诊断信息，解释为什么特定的 提示位置为病变，以帮助阅片师正确评估检测到的病变。实现 为了实现这一目标，我们将探索以下具体目标：（1）开发一个放射组学深度学习（RAID）方案， 检测结直肠病变，（2）开发DeepDES系统用于诊断检测到的病变，以及（3） 评价DeepDES系统的临床受益。成功开发DeepDES系统 将提供高级决策支持，解决当前对CADe的担忧， 同时能够解释为什么提示特定位置 作为靶病变。DeepDES系统的广泛采用和使用将促进预防和早期 因此，它将最终降低美国结直肠癌的死亡率。