Bourdoncle Sylvain, Eche Thomas, McGale Jeremy, Yiu Kevin, Partouche Ephraïm, Yeh Randy, Ammari Samy, Rousseau Hervé, Dercle Laurent, Mokrane Fatima-Zohra
Radiology Department, Rangueil University Hospital, 1 avenue du Professeur Jean, Poulhes, 31059, Toulouse France.
Columbia University Vagellos College of Physicians and Surgeons, Department of Radiology, New York, New York City, USA. Department of Radiology New York Presbyterian Hospital, United States.
Res Diagn Interv Imaging. 2022 Mar;1:100004. doi: 10.1016/j.redii.2022.100004. Epub 2022 Mar 31.
Amidst this current COVID-19 pandemic, we undertook this systematic review to determine the role of medical imaging, with a special emphasis on computed tomography (CT), on guiding the care and management of oncologic patients.
Study selection focused on articles from 01/02/2020 to 04/23/2020. After removal of irrelevant articles, all systematic or non-systematic reviews, comments, correspondence, editorials, guidelines and meta-analysis and case reports with less than 5 patients were also excluded. Full-text articles of eligible publications were reviewed to select all imaging-based publications, and the existence or not of an oncologic population was reported for each publication. Two independent reviewers collected the following information: ( 1) General publication data; (2) Study design characteristics; (3) Demographic, clinical and pathological variables with percentage of cancer patients if available; (4) Imaging performances. The sensitivity and specificity of chest CT (C-CT) were pooled separately using a random-effects model. The positive predictive value (PPV) and negative predictive value (NPV) of C-CT as a test was estimated for a wide range of disease prevalence rates.
A total of 106 publications were fully reviewed. Among them, 96 were identified to have extractable data for a two-by-two contingency table for CT performance. At the end, 53 studies (including 6 that used two different populations) were included in diagnosis accuracy analysis ( = 59). We identified 53 studies totaling 11,352 patients for whom the sensitivity (95CI) was 0.886 (0.880; 0.894), while specificity remained low: in 93% of cases (55/59), specificity was ≤ 0.5. Among all the 106 reviewed studies, only 7 studies included oncologic patients and were included in the final analysis for C-CT performances. The percentage of patients with cancer in these studies was 0.3% (34/11352 patients), lower than the global prevalence of cancer. Among all these studies, only 1 (0.9%, 1/106) reported performance specifically in a cohort of cancer patients, but it however only reported true positives.
There is a concerning lack of COVID-19 studies involving oncologic patients, showing there is a real need for further investigation and evaluation of the performance of the different medical imaging modalities in this specific patient population.
在当前的新冠疫情大流行期间，我们进行了这项系统评价，以确定医学影像，特别是计算机断层扫描（CT）在指导肿瘤患者护理和管理中的作用。
研究选择集中在2020年2月1日至2020年4月23日期间发表的文章。在剔除无关文章后，所有系统评价或非系统评价、评论、通信、社论、指南、荟萃分析以及患者少于5例的病例报告也被排除。对符合条件的出版物的全文进行审查，以选择所有基于影像的出版物，并报告每份出版物中是否存在肿瘤患者群体。两名独立的评审员收集了以下信息：（1）一般出版数据；（2）研究设计特征；（3）人口统计学、临床和病理变量，如有可用的癌症患者百分比；（4）影像表现。使用随机效应模型分别汇总胸部CT（C-CT）的敏感性和特异性。针对广泛的疾病患病率估计了C-CT作为一项检查的阳性预测值（PPV）和阴性预测值（NPV）。
共对106篇出版物进行了全面审查。其中，96篇被确定有可提取的数据用于CT表现的四格表分析。最后，53项研究（包括6项使用不同人群的研究）纳入诊断准确性分析（n = 59）。我们确定了53项研究，共11352例患者，其敏感性（95%CI）为0.886（0.880；0.894），而特异性仍然较低：在93%的病例（55/59）中，特异性≤0.5。在所有106项审查的研究中，只有7项研究纳入了肿瘤患者，并纳入了C-CT表现的最终分析。这些研究中癌症患者的百分比为0.3%（34/11352例患者），低于全球癌症患病率。在所有这些研究中，只有1项（0.9%，1/106）专门报告了癌症患者队列中的表现，但它只报告了真阳性。
令人担忧的是，缺乏涉及肿瘤患者的新冠研究，这表明确实需要进一步调查和评估不同医学影像模式在这一特定患者群体中的表现。
