Development and Dissemination of KiNet: A Novel Imaging Informatics Tool for Gastrointestinal and Pancreatic Neuroendocrine Tumors

项目总结 神经内分泌肿瘤(NETs)是一种影响大多数器官系统的异质性癌症。篮网必须 进行正确的分级，以确保适当的治疗和患者管理。测量的增殖指数 胃肠道(GI)和胰腺网分级按Ki67核染色标准要求 世界卫生组织(WHO)。从病理图像中测量Ki67标记指数(Ki67 LI)需要 准确定量免疫阳性肿瘤、免疫阴性肿瘤和非肿瘤细胞。这个过程是一个 基础研究、转化研究、临床研究和常规临床实践中的基本程序。但是，当前 Ki67图像分析工具有一些缺点：1)Ki67 LI评估仍然主要通过以下方式实现 手工或半自动方法，导致劳动力成本增加、工作流程笨拙、吞吐量低 图像分析和观察者间和观察者内的显著潜在变异性；2)计算机辅助Ki67计数是 由于多阶段图像处理设计容易出错，其中每一阶段本身都是一项非常具有挑战性的任务； 3)当前的算法设计没有考虑到Ki67图像的特点，使得它具有 在Ki67染色图像中对不同类型的细胞进行分类的技术困难。在这项拟议的研究中，我们 寻求开发和传播一种新的基于深度学习的成像信息学系统，特别是 以便在胃肠道和胰腺网络中更好地自动测量Ki67LI。基奈将利用尖端技术 机器学习算法，深度完全卷积网络(FCN)，开发端到端，像素到像素 Ki67 LI单阶段评估模型。为此，我们将首先将Ki67计数作为细胞标识 在一种新的FCN网络中使用类感知结构化回归建模来解决问题。这个网络 将同时对免疫阳性肿瘤、免疫阴性肿瘤和非肿瘤细胞进行检测和分类。下一首, 我们将通过另一项相关任务来进一步增强细胞识别，即提取感兴趣区域(ROI)，该任务 将通过考虑Ki67的图像特征来区分肿瘤和非肿瘤区域。这些 两项任务将被统一到一个单一的神经网络中，并共同学习以使细胞识别和 区域分类。KNET将为准确评估Ki67Li提供一种新的计算方法，从而 能够及早发现疾病并进行有针对性的治疗。与手动计数和当前计数相比 Ki67图像分析方法，将显著提高图像分析的客观性、一致性、可靠性、重复性 和效率。此外，拟议的单级Ki67计数策略与 目前的多阶段Ki67图像分析流水线，将为Ki67图像量化提供新的视角。

PROJECT SUMMARY Neuroendocrine tumors (NETs) are one heterogeneous type of cancer affecting most organ systems. NETs must be correctly graded to ensure proper treatment and patient management. The proliferation index, as measured by Ki67 nuclear staining, is required for gastrointestinal (GI) and pancreatic NET grading per the criteria of the World Health Organization (WHO). Measuring the Ki67 labeling index (Ki67 LI) from pathology images requires accurate quantification of immunopositive tumor, immunonegative tumor and non-tumor cells. This process is an essential procedure in basic, translational and clinical research and in routine clinical practice. However, current Ki67 image analysis tools have a number of drawbacks: 1) Ki67 LI assessment is still mainly achieved with manual or semi-automated methods, leading to increased labor costs, awkward workflows, low-throughput image analysis and significant potential inter- and intra-observer variability; 2) computer-aided Ki67 counting is error-prone due to the multi-stage image processing design, where each stage itself is a very challenging task; 3) current algorithm design does not take into consideration the characteristics of Ki67 images such that it has technical difficulty in classifying different types of cells in Ki67 stained images. In this proposed research, we seek to develop and disseminate a novel deep learning-based imaging informatics system, KiNeT, specifically for better automated Ki67 LI measurement in GI and pancreatic NETs. KiNet will take advantage of cutting-edge machine learning algorithms, deep fully convolutional networks (FCNs), to develop an end-to-end, pixel-to-pixel model for single-stage Ki67 LI assessment. To this end, we will first formulate Ki67 counting as a cell identification problem and solve it using class-aware structured regression modeling within a novel FCN network. This network will simultaneously detect and classify immunopositive tumor, immunonegative tumor and non-tumor cells. Next, we will further enhance cell identification with another related task, extraction of regions of interest (ROIs), which will differentiate tumor from non-tumor regions by taking Ki67 image characteristics into consideration. These two tasks will be unified into one single neural network and jointly learned to benefit both cell identification and region classification. KiNet will provide a novel computational method for accurate Ki67 LI assessment, thereby enabling early detection and targeted treatments of the diseases. Compared to manual counting and current Ki67 image analysis methods, it will significantly improve the objectivity, consistency, reliability, reproducibility and efficiency. Additionally, the proposed single-stage Ki67 counting strategy, which is completely different from current multi-stage Ki67 image analysis pipelines, will provide a new perspective for Ki67 image quantification.