Deep radiomic decision support system for colorectal cancer

项目摘要/摘要 计算机辅助检测(CADE)已被证明可以提高阅读器的灵敏度并减少观察者之间的交互 在检测医学图像中的异常时的差异。然而，它们会提示相对大量的错误 读者觉得乏味乏味的正面(FP)，在此过程中，读者可能会错误地忽略 CADE系统正确提示真实病变。因此，需要提前做出决定。 支持系统，不仅提供高检测灵敏度，而且能够提供高特异度 以解释为什么将特定位置提示为病变。在这项计划中，我们建议改善 利用能分析外在信号的深层卷积神经网络(DCNN)检测CADE 放射表型，如局部解剖背景，靶区皮损，而目前的CADE系统 只考虑固有的放射表型，如检测到的病变的形状和质地。此外，我们 可以使用DCNN解释为什么通过使用解剖学方法提示特定位置 有意义的对象类别与过去诊断病例的图像检索相似。在这个项目中，我们将专注于 CT结肠成像(CTC)是早期结肠癌的一种微创筛查方法 检测大肠病变以预防结直肠癌(CRC)，结直肠癌是第二大致癌原因 美国的死亡人数。然而，从历史上看，只有腺瘤被认为是结直肠癌的先兆。 最近的研究揭示了一种分子途径，其中锯齿状病变也可以发展为结直肠癌。近期 研究表明，CTC可以基于一种称为 对比度涂层。因此，本项目的目标是开发一个深度放射决策支持系统(DeepDES) 利用深度学习在检测结直肠癌中提供高灵敏度和特异度的系统 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 位置被提示为病变，以帮助读者正确评估检测到的病变。要实现 为了实现这一目标，我们将探讨以下具体目标：(1)为 大肠病变的检测，(2)开发用于检测到的病变的诊断的DeepDES系统，以及(3) 评价DeepDES系统的临床效益。拟议的DeepDES系统的成功开发 将提供高级决策支持，解决当前对凯德的担忧， 高检测灵敏度和高特异度，同时能够解释提示特定位置的原因 作为目标损伤。DeepDES系统的广泛采用和使用将促进预防和早期 这项技术将有助于癌症的诊断，从而最终降低美国结直肠癌的死亡率。

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.