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.

项目摘要/摘要 计算机辅助检测（CADE）已显示可提高读者的敏感性并降低观察者间 检测医学图像异常的差异。但是，它们促使相对较大的虚假 读者认为繁琐的审查，在此过程中，读者可能会错误地驳回 CADE系统正确地向它们提示了真实病变。那是对高级决定的需求 支持系统不仅可以提供高检测敏感性，而且还提供高特异性的同时也可以 解释为什么要引发特定位置作为病变。在这个项目中，我们建议改善 通过深卷积神经网络（DCNN）对CADE的检测特异性，可以分析外部 靶向病变的放射素表型，例如局部解剖结构的背景，而当前的CADE系统 仅考虑固有的放射素表型，例如检测到的病变的形状和质地。此外，我们 可以使用dcnns提供解释为什么通过解剖学上提示特定位置的解释 有意义的对象类别，具有相似的图像检索过去被诊断的病例。在这个项目中，我们将集中精力 在计算机断层扫描（CTC）上，这是早期侵入性筛查方法 检测结直肠癌的结直肠癌（CRC），这是癌症的第二大原因 在美国死亡。然而，从历史上看，只有腺瘤被认为是CRC的前体。 最近的研究揭示了一种分子途径，其中也可以在CRC中发展为锯齿状病变。最近的 研究表明，CTC可以根据所谓的现象准确检测锯齿状病变 对比涂层。这是该项目的目的是建立深层的放射线决策支持（DEEPDES） 利用深度学习的系统，以在检测结直肠癌的检测中提供高灵敏度和特异性 尤其是病变，特别是锯齿状的病变，以及提供诊断信息，解释了为什么特定的 提示位置作为病变，以帮助读者正确评估检测到的病变。实现 目标，我们将探讨以下特定目的：（1）为该人开发放射学深度学习（RAID）方案 检测结直肠病变，（2）开发用于检测病变诊断的DeepDES系统，（3） 评估DeepDES系统的临床益处。拟议的DeepDES系统的成功开发 将提供高级决策支持，以解决当前对CADE的担忧 高检测灵敏度和高特异性，同时可以解释为什么提示特定位置 作为目标病变。大广泛采用和使用DeepDES系统将提高预防和早期 诊断癌症，因此最终将降低美国大肠癌的死亡率。