Artificial Intelligence for Lung Cancer Characterization in HIV affected populations in Uganda and Tanzania

摘要-项目3 年龄标准化率（ASR）显示，乌干达和坦桑尼亚的肺癌发病率稳步上升 与其他癌症相比。不幸的是，没有建立肺癌筛查计划， 坦桑尼亚或乌干达。所记录的肺癌病例大多数是在胸部计算机上偶然发现的 进行断层扫描（CT）以确定患者呼吸道疾病的原因。的这个问题 在坦桑尼亚和乌干达，由于结核病的高发病率， (TB)其可在肺中引起慢性肉芽肿反应，表现为良性肺结节 CT和X光检查熟练的人员能够获得高质量的胸部X光和CT图像并对其进行解释， 乌干达和坦桑尼亚的大多数三级卫生中心都缺乏。此外，与艾滋病患者一起生活的人数 艾滋病毒艾滋病继续上升，据报道，2014年坦桑尼亚有1，411，829人感染艾滋病毒 艾滋病然而，人们对非洲的肺癌和艾滋病毒知之甚少。随着目前观察到的 由于肺癌发病率高，迫切需要研究乌干达肺癌与艾滋病毒之间的联系 坦桑尼亚。另一个有趣的问题是，肺癌的放射学诊断标准是否 应统一对艾滋病毒阳性和阴性患者进行筛查。 我们的小组一直在开发新的放射组学（射线照相的计算机特征分析）类别。 扫描）特征，用于改善恶性肺结节与良性肺结节的区分。例如，我们已经证明， 结节脉管系统的迂曲度在良性结节和恶性结节之间有很大的不同。 此外，我们已经表明，结节周围表面（肺外）的放射学特征 结节）与活检组织标本的免疫应答程度相关。给定 艾滋病患者往往具有较低的免疫细胞群体，一个合理的推测是， 射线照相扫描上的特征将反映免疫特征的不存在。 在这个项目中，我们将开发一个基于放射组学的机器分类器，称为LunIRiS（肺部图像风险 评分）用于预测胸部CT或X线扫描结节的恶性风险。我们假设新的 放射性生物标志物可以改善乌干达和坦桑尼亚的非侵入性肺部诊断， 结核病发病率较高，因此结核病诱发的肉芽肿。此外，我们将努力利用这些工具， 确定HIV+和HIV-肺之间在CT和胸部X光片上放射学表型的可能差异 癌症患者，并利用这些差异来开发HIV状态特异性肺癌筛查模型。 最后，第四个目标将是创建一个基于网络的LunIRiS部署，以实现决策支持， 克利夫兰与乌干达和坦桑尼亚之间的远程放射学服务，以改善肺结节诊断 进行LDCT扫描这种伙伴关系将允许转让技术和放射学专业知识 （通过门户网站）改善乌干达和坦桑尼亚的肺癌筛查。

ABSTRACT – Project 3 The age standardized rates (ASRs) show a steady rise in the incidence of lung cancer in Uganda and Tanzania compared to other cancers. Unfortunately, there is no established lung cancer screening program in either of Tanzania or Uganda. The cases of lung cancer recorded have mostly been found incidentally on chest computed tomography (CT) scans done to establish the cause of patients' respiratory symptomatology. This problem of diagnostic specificity is exacerbated in Tanzania and Uganda on account of the high incidence of tuberculosis (TB) which can cause a chronic granulomatous reaction in the lungs manifesting as benign pulmonary nodules on CT and X-rays. Skilled personnel to acquire good quality chest x-ray and CT images and to interpret them is lacking in most tertiary health centers in Uganda and Tanzania. Additionally, the number of people living with HIV AIDS continues to rise, and in 2014, it was reported that Tanzania had 1,411,829 people living with HIV AIDS. However, very little is known about lung cancer and HIV in Africa. With the currently observed increasing incidence rates of lung cancer, there is an urgent need to study the link between lung cancer and HIV in Uganda and Tanzania. An additional intriguing question is whether the same radiographic criteria for lung cancer screening should be uniformly applied across both HIV+ and HIV- patients. Our group has been developing new classes of radiomic (computerized feature analysis of radiographic scans) features for improved discrimination of malignant from benign lung nodules. For instance, we have shown that the tortuosity of nodule vasculature is substantially different between benign and malignant nodules. Additionally, we have shown that radiomic features of the peri-nodular surface (immediately outside the lung nodule on CT and X-rays) were associated with degree of immune response on biopsy tissue specimens. Given that HIV patients tend to have a low immune cell population, a reasonable conjecture is that the radiomic signature on radiographic scans will reflect the absence of an immune signature. In this project we will develop a radiomics based machine classifier called LunIRiS (Lung Image Risk Score) for predicting risk of malignancy for a nodule on a chest CT or X-ray scan. We hypothesize that the new radiomic biomarkers can enable improved non-invasive lung diagnosis in Uganda and Tanzania which has a higher prevalence of TB and hence TB induced granulomas. Additionally, we will seek to employ these tools to identify possibly differences in the radiographic phenotype on CT and chest X-rays between HIV+ and HIV- lung cancer patients and to employ these differences to develop HIV status specific lung cancer screening models. Finally, the fourth objective will be to create a web-based deployment of LunIRiS to enable decision support and teleradiology based services between Cleveland and Uganda and Tanzania for improving lung nodule diagnosis on screening LDCT scans. This partnership will allow for transference of technology and radiology expertise (through the web portal) for improved lung cancer screening in Uganda and Tanzania.