Fusing Data from CT Deep Learning, CT Radiomics and Peripheral Blood Immune profiles to Diagnose Lung Cancer in Symptomatic Patients

Background: Lung cancer is the leading cause of cancer-related deaths. Diagnosis at late stages is common due to the largely non-specific nature of presenting symptoms contributing to high mortality. There is a lack of specific, minimally invasive low-cost tests to screen patients ahead of the diagnostic biopsy. Patients and Methods: 344 symptomatic patients from the lung clinic of Lister hospital suspected of lung cancer were recruited. Predictive covariates were successfully generated on 170 patients from Computed Tomography (CT) scans using CT Texture Analysis (CTTA) and Deep Learning Autoencoders (DLA) as well as from peripheral blood data for immunity using high depth flow-cytometry and for exosome protein components. Study: “Improving the Early Detection of Lung Cancer by Combining Exosomal Analysis of Hypoxia with Standard of Care Imaging (LungExoDETECT)” (https://clinicaltrials.gov/ct2/show/NCT04629079)All procedures were performed in compliance with relevant laws and institutional guidelines and have been approved by the UK NHS HRA, and informed consent obtained from the subjects (REC reference: 19/EE/0357 20^th Feb 2020). <br>

研究背景：肺癌是癌症相关死亡的首要病因。由于患者首发症状多缺乏特异性，多数患者确诊时已处于晚期，进而导致较高的死亡率。目前尚缺乏特异性强、微创且低成本的检测手段，无法在诊断性活检前对患者进行肺癌筛查。患者与方法：共招募344名来自李斯特医院（Lister Hospital）肺科门诊、疑似肺癌的有症状患者。研究团队针对其中170名患者，通过计算机断层扫描（Computed Tomography, CT）影像，采用CT纹理分析（CT Texture Analysis, CTTA）与深度学习自编码器（Deep Learning Autoencoders, DLA）成功生成预测协变量；同时借助高深度流式细胞术获取外周血免疫相关数据，并通过分析外泌体蛋白组分生成对应预测协变量。研究：本研究为《通过联合缺氧外泌体分析与标准诊疗影像技术提升肺癌早期检测效能（LungExoDETECT）》（临床试验链接：https://clinicaltrials.gov/ct2/show/NCT04629079）。所有研究操作均严格遵循相关法律法规与机构指南开展，已通过英国国民保健署健康研究管理局（UK NHS HRA）审批，并已获取所有受试者的书面知情同意（REC编号：19/EE/0357，2020年2月20日）。