Early Evaluation of Ovarian Cancer Prognosis by Fusing Radiographic and Histopathologic Imaging Information

项目3：融合放射学和组织学对卵巢癌预后的早期评估 成像信息 摘要 作为妇科肿瘤中最具侵袭性的恶性肿瘤，卵巢癌具有高度异质性， 肿瘤对特定化学疗法的反应在患者之间显著不同。然而，由于缺乏 准确的临床标志物，以分层患者，并预测谁可以和不能受益于某些类型的 由于使用化疗药物或方法治疗卵巢癌患者的疗效低，为了 为了应对和帮助解决这一临床挑战，本项目的总体目标是开发和 使用新的图像标记物验证早期预测肿瘤对化疗反应的新策略 由机器学习模型生成，所述机器学习模型使用定量图像特征进行训练，所述定量图像特征从 计算机断层扫描（CT）和数字组织病理学图像。基于放射组学的概念，病理组学 以及我们令人鼓舞的初步研究，我们假设最先进的数据分析技术可以 融合来自射线照相和病理图像的有价值的预后信息， 与卵巢癌化疗反应高度相关的影像学标记物 患者为了验证这一假设，我们提出了四个具体目标。目标1：基于多样化的患者数据库 在斯蒂芬森癌症中心，我们将收集一个回顾性和一个前瞻性数据集，包括 共420名接受过化疗的卵巢癌患者。数据集将包括CT图像， 每个患者的肿瘤样本的组织病理学图像和其他相关临床信息。目标2：我们 探索和识别从CT和病理图像计算的肿瘤异质性相关图像特征 在应用新的混合图像处理方案以准确地分割肿瘤体积和癌细胞之后。 目标3：我们将在初始CT/病理学特征池上应用特征选择方法，以识别两个最佳的 特征向量然后，预测模型（即，贝叶斯信念网络）将被训练以融合最优特征 载体和其他临床变量来预测肿瘤对早期治疗的反应。目标4：我们将 一项试点前瞻性研究，以评估预测模型的性能和稳健性。几种统计 方法（即考克斯比例风险分析、受试者操作特征曲线、混淆矩阵）将 用于评估通过融合CT和病理图像特征所带来的性能改善。我们还将 在现有标志物的背景下验证新模型提供的额外预后价值。为了 为了完成提出的目标和研究任务，组建了一个跨学科的团队，其中包括 来自俄克拉荷马州大学的医学影像学、妇科肿瘤学、放射学和病理学专家。如果 如果成功，该项目可以提供必要的初步数据和科学证据来支持研究 项目负责人（RPL）申请更全面的研究项目（即，NIH R 01）进一步优化和 验证第一个同类的、健壮的、易于使用的决策支持工具，该工具可以帮助临床医生（即， 放射科医师和肿瘤科医师）确定不同患者的最佳癌症治疗策略。

Project 3: Early Evaluation of Ovarian Cancer Prognosis by Fusing Radiographic and Histopathologic Imaging Information ABSTRACT As the most aggressive malignancy in gynecologic oncology, ovarian cancer is highly heterogeneous and the tumor response to a specific chemotherapy vary significantly among patients. However, due to the lack of accurate clinical markers to stratify patients and predict who can and cannot benefit from certain types of chemotherapy drugs or methods, efficacy of treating ovarian cancer patients using chemotherapy is low. In order to address and help solve this clinical challenge, the overarching objective of this project is to develop and validate a new strategy for early prediction of tumor response to chemotherapy using a novel image marker generated by a machine learning model that is trained using quantitative image features computed from computer tomography (CT) and digital histopathology images. Based on the concept of Radiomics, Pathomics and our encouraging preliminary studies, we hypothesize that the state-of-the-art data analysis technology can fuse the valuable prognostic information from both radiographic and pathological images to generate a new image marker, which has a high degree of association with the chemotherapy response of ovarian cancer patients. To validate this hypothesis, we propose 4 specific aims. Aim 1: Based on a diverse patient database at the Stephenson Cancer Center, we will assemble one retrospective and one prospective dataset, containing a total of 420 ovarian cancer patients who have undergone chemotherapies. The dataset will include CT images, histopathological images of tumor samples and other related clinical information of each patient. Aim 2: We will explore and identify tumor heterogeneity-related images features computed from both CT and pathology images after applying a new hybrid image processing scheme to accurately segment tumor volume and cancer cells. Aim 3: We will apply feature selection methods on the initial CT/pathology feature pools to identify two optimal feature vectors. Then, a prediction model (i.e., Bayesian belief network) will be trained to fuse optimal feature vectors and other clinical variables to predict tumor response to therapy at early stage. Aim 4: We will conduct a pilot prospective study to evaluate performance and robustness of the prediction model. Several statistical methods (i.e. Cox proportional hazards analysis, receiver operation characteristic curve, confusion matrix) will be used to evaluate the performance improvement by fusing the CT and pathology image features. We will also validate the added prognostic value provided by the new model in the context of the existing markers. In order to accomplish the proposed aims and research tasks, an interdisciplinary team is assembled, which includes experts in medical imaging, gynecologic oncology, radiology and pathology from the University of Oklahoma. If successful, this project can produce the essential preliminary data and scientific evidence to support the research project leader (RPL) to apply for a more comprehensive research project (i.e., NIH R01) to further optimize and validate a first-of-its-kind, robust, easy-to-use decision-making support tool, which can help clinicians (i.e., radiologists and oncologists) determine the optimal cancer treatment strategy for different patients.