Learning MRI and histology image mappings for cancer diagnosis and prognosis

该项目旨在利用机器学习的最新进展来解决癌症管理中的急性问题-最直接的是前列腺癌。目前通过活检和组织学做出治疗决定的标准方法有两个主要局限性；它是侵入性的，主观的/不一致的。我们将开发支持解决这两个问题的新解决方案的计算工具。具体来说，我们的目标是使非侵入性MRI成为主要的诊断工具。这将避免大量不必要的活组织检查，这些活组织检查有可能带来改变生命的重大副作用，只对边缘病例进行检查。我们还计划将MR信号与定量组织特征联系起来，以便进行一致的评估，从而做出更可靠的治疗决策。磁共振成像在前列腺癌中的应用仅在最近几年才成为常规。因此，MRI与患者预后（如5-10年生存率）相关的数据是不可用的。然而，我们在获得i)相关的MRI和组织学图像以及ii)相关的组织学和患者预后方面具有独特的优势。结合起来，这些支持两步学习和估计过程：从MRI到组织学特征；从组织学特征到患者预后。这样的映射可以为临床决策提供宝贵的新信息，以及指导设计最大限度地提供信息的未来MRI方案。这些协议将使长期数据收集倡议成为可能，支持从MRI到结果的直接映射。该项目涉及工程挑战，需要在基于图像的机器学习技术的前沿进行创新：i)适应训练图像对齐中的不确定性；Ii)学习模型输出中的不确定性的量化和可视化；Iii)高维输入数据的显著特征选择；Iv)开发由隐式计算模型（如神经网络）驱动的实验设计优化算法。我们以深度学习的最新理念为基础来应对这些挑战。我们针对前列腺癌的直接问题量身定制解决方案，但扩展到癌症成像和一般医学成像的相关任务。

This project aims to exploit recent advances in machine learning to address acute problems in cancer management - most directly prostate cancer. The current standard approach of making treatment decisions via biopsy and histology has two key limitations; it is invasive and subjective/inconsistent. We will develop the computational tools supporting new solutions that resolve both issues. Specifically, we aim to enable non-invasive MRI to become the primary diagnostic tool. This would avoid a large number of unnecessary biopsies, which carry significant risk of life-changing side-effects, reserving the procedure for only marginal cases. We also plan to relate MR signals to quantitative tissue features enabling consistent assessment and thus more reliable treatment decisions.The use of MRI in prostate cancer has become routine only in the last few years. Thus, data relating MRI to patient outcome (e.g. 5-10 year survival) is not available. However, we are uniquely positioned to obtain i) associated MRI and histology images, and ii) associated histology and patient outcome. In combination, these support a two-step learning and estimation process: from MRI to histological features; and from histological features to patient prognosis. Such mappings can provide invaluable new information for clinical decision making, as well as guide the design of maximally informative future MRI protocols. Such protocols will enable long-term data collection initiatives that support direct mappings from MRI to outcome.The project involves engineering challenges that demand innovations at the cutting edge of image-based machine learning technology: i) accommodating uncertainty in the alignment of training images; ii) quantification and visualization of uncertainty in the output of learned models; iii) salient feature selection in high-dimensional input data; iv) development of experiment design optimization algorithms driven by implicit computational models (such as neural networks). We build on the latest ideas in deep learning to address these challenges. We tailor solutions relevant to the immediate problems at hand in prostate cancer, but that extend to related tasks in cancer imaging and medical imaging in general.