Machine learning-based quantitative image, tissue, and clinical data analysis for lesion detection and characterization on prostate cancer imaging

基于机器学习的定量图像、组织和临床数据分析，用于前列腺癌成像的病变检测和表征

• 批准号: RGPIN-2019-06756
• 负责人: Ward, Aaron
• 金额: $ 2.99万
• 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: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=688295
• 关键词: Machine; learning; based; quantitative; image

Prostate cancer is often treated by whole-prostate radiation treatment or surgery, with side effects including erectile dysfunction and urinary incontinence. However, not all men need such invasive treatment; some prostate cancers are sufficiently slow-growing to never need treatment, and some are concentrated in one spot and treatment can be targeted to the tumours only. One of the major problems in prostate cancer is how to determine the right treatment for each man. This is currently done using a blood test and a needle biopsy. Because the blood test has limited accuracy and the small biopsy needles may miss tumours, physicians need to select treatments using incomplete information. To solve this problem, this research proposes three-dimensional (3D) magnetic resonance imaging (MRI) and positron emission tomography (PET) to completely map the prostate. A new form of PET imaging, called prostate specific membrane antigen (PSMA) PET imaging, is showing tremendous promise in its ability to help physicians detect cancer within the prostate. Although MRI and PSMA PET are the best imaging technologies for prostate imaging, seeing prostate tumours on MRI can still be extremely for the physician difficult because of the complexity of the images, and there are no current guidelines for physicians to follow in interpreting PSMA PET images of the prostate. We propose the development of an artificially intelligent computer system to help doctors to translate the complex images into a simple 3D cancer map that will enable guiding of biopsies to targets and choosing the right treatment for each patient. This system will be based on artificial neural networks, which are software programs that mimic aspects of the visual systems in human brains. A new type of artificial neural network with a “deep learning” architecture has shown tremendous promise in machine vision tasks for everyday objects; for instance, many mobile phone cameras use deep learning for face recognition. We will, for the first time, develop a deep learning-based artificial intelligence system that will integrate mpMRI and PSMA PET images with all of the clinical parameters (e.g. blood test results) that the physician would use to assist in cancer detection. In addition, we will form a more robust interface between the physician and the machine by eye tracking technology to get a window into the physician's mind. Eye gaze locations suggest regions of suspicion or uncertainty, and the deep learning system will exploit these eye gaze data to sharpen its assessment of image regions where the physician's attention dwells. This will result in a hybrid human-machine vision system drawing on the combined strengths of human and artificial intelligence to finally develop an inexpensive software tool that will avoid overtreatment of prostate cancer in men for whom this would compromise quality life with no added benefit, and detect aggressive prostate cancer early while it is still curable.

前列腺癌通常采用全前列腺放射治疗或手术治疗，副作用包括勃起功能障碍和尿失禁。然而，并不是所有的男性都需要这种侵入性治疗；一些前列腺癌生长非常缓慢，永远不需要治疗，有些前列腺癌集中在一个部位，治疗只能针对肿瘤。前列腺癌的主要问题之一是如何为每个人确定正确的治疗方法。目前，这是通过血液测试和针吸活组织检查完成的。由于血液检测的准确性有限，而且细小的活检针可能会漏掉肿瘤，医生需要使用不完整的信息来选择治疗方法。为了解决这一问题，本研究提出了三维(3D)磁共振成像(MRI)和正电子发射断层扫描(PET)来完整地绘制前列腺。一种名为前列腺特异性膜抗原(PSMA)PET成像的新形式的PET成像在帮助医生检测前列腺癌方面显示出巨大的前景。虽然MRI和PSMA PET是最好的前列腺成像技术，但由于图像的复杂性，在MRI上看到前列腺肿瘤对医生来说仍然是非常困难的，而且目前还没有医生在解释PSMA PET前列腺图像时可以遵循的指南。我们建议开发一种人工智能计算机系统，以帮助医生将复杂的图像转换为简单的3D癌症地图，从而能够将活检引导到目标位置，并为每个患者选择正确的治疗方法。该系统将基于人工神经网络，人工神经网络是模仿人脑视觉系统的软件程序。一种具有深度学习结构的新型人工神经网络在日常物体的机器视觉任务中显示出巨大的前景；例如，许多手机摄像头使用深度学习进行人脸识别。我们将首次开发一个基于深度学习的人工智能系统，将mpMRI和PSMA PET图像与所有临床参数(例如血液测试结果)集成在一起，医生将使用这些参数来辅助癌症检测。此外，我们将通过眼球跟踪技术在医生和机器之间形成更强大的界面，以了解医生的大脑。眼睛凝视的位置暗示着怀疑或不确定的区域，深度学习系统将利用这些眼睛凝视数据来加强对医生注意力所在图像区域的评估。这将导致一种混合的人机视觉系统，利用人类和人工智能的组合优势，最终开发一种廉价的软件工具，以避免过度治疗前列腺癌的男性，这将损害男性的生活质量，而不会带来额外的好处，并在侵袭性前列腺癌仍可治愈的情况下及早发现。