Automatic Creation of lung cancer pathology reports

肺癌造成的死亡比任何其他类型的癌症都多]。它通常是在肺部CT扫描时首次发现的，但通常需要病理学家在显微镜下对提取的组织进行进一步的肉眼检查。肺癌的一般类型和潜在的形态特征严重影响临床预后，因此对其进行准确的判定至关重要。作出准确诊断的困难在于肿瘤内和肿瘤内的异质性。此外，病理学家之间的观察者间和观察者内的差异破坏了对诊断的信任。这就是计算机辅助诊断(CAD)工具的创建如此重要的原因。最近(2021年3月)获得FDA批准的Optellium的“虚拟结节诊所”就是这样一个用于肺部CT图像的极好的例子。我的论文项目旨在实现三个主要目标：1)自动生成报告。我计划使用人工智能和机器学习技术，从肺癌显微镜图像中自动生成标准化的病理报告，并进行验证。利用专家评审和NL 3)新的放射学特征识别相结合的方法，验证根据上述目标开发的关于肺癌筛查方案数据的报告生成工具。提高从CT和组织学图像中提取的肺部肿瘤的放射成像特征的价值。为了实现这些目标，我将在机器学习和计算机视觉方面开发新的方法。研究方法的主要流派如下：1)多层次注意。我计划探索四个图像实体，以关注从图像块中提取的特征、块中的空间位置、块本身和图像中的比例。以前考虑过使用多倍放大，但没有使用注意机制来选择要关注的放大倍数。2)连接病理学和放射学。另一个主要目标是改善肺癌诊断的非侵入性途径。将组织学切片直接映射到CT扫描上是一项具有挑战性的任务，因为这两种模式之间存在着巨大的差异。因此，需要研究除直接映射之外的其他方法。在改进我的组织学模型后，我计划研究从组织学图像中提取的特征与CT扫描之间的联系。理想情况下，这将允许绕过诊断的侵入性阶段。该项目将为提高诊断水平做出贡献，肺癌患者的治疗将受益于DPhil项目。“自动注释器”将被用来提高病理医生从组织学图像进行诊断的准确性和一致性，而CT-组织学连接将有望减少目前仍然需要的不必要的侵入性程序的数量。当然，总体影响是通过实现上述目标来降低患者死亡率。该项目属于EPSRC医疗保健技术研究领域。

Lung cancer is accountable for more death than any other type of cancer]. It is typically first picked up during a lung CT scan but usually requires a further visual examination of the extracted tissue under a microscope by a pathologist. The general type and the underlying morphological characteristics of lung cancer seriously affect clinical prognosis, so it is vital that they are accurately determined. The difficulty for making an accurate diagnosis lies in the inter- and intra-tumour heterogeneity. Furthermore, the trust in the diagnosis is undermined by the inter-and intra-observer variability among the pathologists. That is what makes the creation of a computer-aided diagnostics (CAD) tool so essential. An excellent example of such a tool for lung-CT images is the recently (March 2021) FDA-approved Optellium's "Virtual Nodule Clinic".My thesis project aims to achieve three primary goals:1) Automatic report generation. I plan to use artificial intelligence and machine learning to automate the creation of standardised pathology reports from lung-cancer microscopy images 2) Validation. Validate the report-generation tool developed under the above goal on data from the lung cancer screening programme by using a combination of expert reviews and NL 3) Identification of new radiology features. Improve the value of radiology imaging features of lung tumours extracted from CT and histology images.To achieve these aims, I will develop novel methods in machine learning and computer vision. The main genres of the methods to be explored are as follows:1) Multi-level Attention. I plan to explore four image entities to pay attention to features extracted from image patches, spatial positions within the patches, patches themselves, and scale within the images. Using multiple magnifications was considered previously, but no attention mechanism was used to choose the magnification to focus on.2) Connecting Pathology and Radiology. Another primary goal is to improve the non-invasive path of lung cancer diagnostics. Mapping a histology slide onto the CT scan directly is a challenging task due to a large-scale difference between the two modalities. Hence, methods other than direct mapping need to be researched. After improving my histology models, I plan to investigate the connection between the features extracted from histology images and CT scans. Ideally, this will allow bypassing the invasive stage of making a diagnosis.This project will make contributions to improving the diagnostics, and the treatment of patients with lung cancer will benefit from this DPhil project. The "Automatic Annotator" will be used to increase the accuracy and the consistency of diagnoses pathologists make from histology images, while the CT-Histology connection will hopefully reduce the number of unnecessary invasive procedures which are still needed now. The overall impact is, of course, to reduce patient mortality through the achievement of the above goals.This project falls within the EPSRC Healthcare technologies research area.