论文标题
使用放射线学预测Covid-19中的临床结果，并深入学习胸部X光片：一项多机构研究
Predicting Clinical Outcomes in COVID-19 using Radiomics and Deep Learning on Chest Radiographs: A Multi-Institutional Study
论文作者
论文摘要
我们使用针对冠状病毒病（COVID-19）患者的胸部X光片（CXR）的计算模型预测机械通气需求和死亡率。这项两中心的回顾性研究分析了515例COVID-19患者在Stony Brook大学医院和2020年8月之间在Stony Brook University医院和纽瓦克Beth Israel Medical Center治疗的530例CXR中进行了分析。DL和机器学习分类器，以预测使用CXRS患者进行机械风险和死亡的要求和死亡率。还探索了一个新型的放射线嵌入框架以进行预测。将所有结果与CXRS（区域专家严重程度得分）的放射科医生分级进行比较。放射素和DL分类模型的MAUCS为0.78 +/- 0.02和0.81 +/- 0.04，而专家得分MAUCS的MAUCS分别为0.75 +/- 0.02和0.79 +/- 0.05，分别用于机械通气需求和死亡率预测。对于每个预测任务，使用放射线学和专家严重程度得分的合并分类器都导致0.79 +/- 0.04和0.83 +/- 0.04的MAUCS，仅对人工智能或放射科医生的解释进行了改善。我们的结果还表明，将放射线特征纳入DL可以改善模型预测，这可能在其他病理中探讨。本研究中提出的模型及其提供的预后信息可能有助于医师在COVID-19大流行期间的医师决策和资源分配。
We predict mechanical ventilation requirement and mortality using computational modeling of chest radiographs (CXRs) for coronavirus disease 2019 (COVID-19) patients. This two-center, retrospective study analyzed 530 deidentified CXRs from 515 COVID-19 patients treated at Stony Brook University Hospital and Newark Beth Israel Medical Center between March and August 2020. DL and machine learning classifiers to predict mechanical ventilation requirement and mortality were trained and evaluated using patient CXRs. A novel radiomic embedding framework was also explored for outcome prediction. All results are compared against radiologist grading of CXRs (zone-wise expert severity scores). Radiomic and DL classification models had mAUCs of 0.78+/-0.02 and 0.81+/-0.04, compared with expert scores mAUCs of 0.75+/-0.02 and 0.79+/-0.05 for mechanical ventilation requirement and mortality prediction, respectively. Combined classifiers using both radiomics and expert severity scores resulted in mAUCs of 0.79+/-0.04 and 0.83+/-0.04 for each prediction task, demonstrating improvement over either artificial intelligence or radiologist interpretation alone. Our results also suggest instances where inclusion of radiomic features in DL improves model predictions, something that might be explored in other pathologies. The models proposed in this study and the prognostic information they provide might aid physician decision making and resource allocation during the COVID-19 pandemic.
