Heterogeneous data fusion and machine learning for image understanding in lung cancer

用于肺癌图像理解的异构数据融合和机器学习

• 批准号: RGPIN-2020-06498
• 负责人: Mattonen, Sarah
• 金额: $ 1.75万
• 依托单位: University of Western Ontario
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=741253
• 关键词: Heterogeneous; data; fusion; machine; learning

Lung cancer remains the most common cause of cancer death worldwide. For patients with early-stage non-small cell lung cancer, where the tumour is small (less than 5 cm) and has not spread to other parts of the body, standard treatment is either surgery or high-dose radiation therapy. However, even when these cancers are diagnosed at an early-stage, up to half of patients may develop a recurrence after treatment, in which the cancer returns at the same spot or somewhere else in the body. One of the major problems with lung cancer is determining which patients will be cured of their disease following treatment. To solve this problem, this research proposes to develop a novel software tool to aid physicians in determining which patients are at a higher risk of recurrence following treatment. Prior to treatment patients receive imaging to determine the extent of their disease, including computed tomography (CT) and position emission tomography (PET). However, physicians typically only measure the diameter of the tumour on CT and look for areas where the cancer has spread on PET. We propose to develop an artificially intelligent computer system to help physicians extract more information from these medical images. A new area of artificial intelligence, known as deep learning is a type of artificial neural network, which is a software program that mimics the structure and function of biological neurons, such as those in the brain. Deep learning has shown promise in many areas of medicine, including understanding imaging data. We will develop a deep learning based artificial intelligence software system to integrate medical imaging and the non-imaging patient data to predict which patients are at a higher risk of treatment failure. A deep learning system can extract subtle features within the image, that may not be visible by the physician's eye, and combine it with other patient information. This model will integrate multi-modal and multi-scale information, including 3-dimensional medical imaging data (CT and PET), clinical parameters (e.g., age, smoking history), blood parameters, and tumour genomic information. This software system will integrate multiple sources of information about a patient and provide the physician with a prognosis for the patient, or a probability that the standard treatment will cure the patient's cancer. We will also develop, for the first time, a novel graphical user interface to visualize and display this information to the physician. Overall, the software tool developed within this research program will enable accurate computer-aided prognosis based on different types of lung imaging data and the integration of clinical, blood, and genomic information about a patient. This non-invasive and inexpensive software tool will allow for better prognostic characterization of lung cancer that can help physicians in identifying patients at higher risk of recurrence for indicating more aggressive or personalized treatment options.

肺癌仍然是全球癌症死亡的最常见原因。对于患有早期非小细胞肺癌的患者，肿瘤小（小于5 cm）并且尚未扩散到身体的其他部位，标准治疗是手术或高剂量放射治疗。但是，即使在早期诊断出这些癌症时，多达一半的患者可能会在治疗后复发，其中癌症在同一地点或体内其他地方返回。肺癌的主要问题之一是确定治疗后哪些患者将治愈其疾病。为了解决这个问题，这项研究建议开发一种新型的软件工具，以帮助医生确定哪些患者在治疗后患有更高的复发风险。在治疗之前，患者会接受成像以确定其疾病的程度，包括计算机断层扫描（CT）和位置发射断层扫描（PET）。但是，医师通常仅测量CT上肿瘤的直径，并寻找癌症在PET上扩散的区域。我们建议开发一种人为智能的计算机系统，以帮助医生从这些医学图像中提取更多信息。人工智能的新领域是一种人工神经网络，它是一种模仿生物神经元的结构和功能的软件程序，例如大脑中的神经元。深度学习在许多医学领域都表现出了希望，包括了解成像数据。我们将开发一个基于深度学习的人工智能软件系统，以整合医学成像和非成像患者数据，以预测哪些患者的治疗失败风险更高。深度学习系统可以在图像中提取微妙的特征，这可能是医生的眼睛看不见的，并将其与其他患者信息相结合。该模型将整合多模式和多尺度信息，包括3维医学成像数据（CT和PET），临床参数（例如，年龄，吸烟史），血液参数和肿瘤基因组信息。该软件系统将整合有关患者的多种信息来源，并为医生提供对患者的预后，或者标准治疗可以治愈患者癌症的可能性。我们还将首次开发一种新颖的图形用户界面，以可视化并将这些信息显示给医生。总体而言，该研究计划中开发的软件工具将基于不同类型的肺成像数据以及有关患者的临床，血液和基因组信息的整合来实现准确的计算机辅助预后。这种无创和廉价的软件工具将允许对肺癌进行更好的预后表征，以帮助医师识别出更高复发风险的患者，以表明更具侵略性或个性化的治疗选择。