Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.
Computational Imaging Research Lab, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, A-1090, Vienna, Austria.
Eur Radiol. 2021 Aug;31(8):5443-5453. doi: 10.1007/s00330-020-07635-6. Epub 2021 Mar 17.
Acute respiratory distress syndrome (ARDS) constitutes a major factor determining the clinical outcome in polytraumatized patients. Early prediction of ARDS is crucial for timely supportive therapy to reduce morbidity and mortality. The objective of this study was to develop and test a machine learning-based method for the early prediction of ARDS derived from the first computed tomography scan of polytraumatized patients after admission to the hospital.
One hundred twenty-three patients (86 male and 37 female, age 41.2 ± 16.4) with an injury severity score (ISS) of 16 or higher (31.9 ± 10.9) were prospectively included and received a CT scan within 1 h after the accident. The lungs, including air pockets and pleural effusions, were automatically segmented using a deep learning-based algorithm. Subsequently, we extracted radiomics features from within the lung and trained an ensemble of gradient boosted trees (GBT) to predict future ARDS.
Cross-validated ARDS prediction resulted in an area under the curve (AUC) of 0.79 for the radiomics score compared to 0.66 for ISS, and 0.68 for the abbreviated injury score of the thorax (AIS-thorax). Prediction using the radiomics score yielded an f1-score of 0.70 compared to 0.53 for ISS and 0.57 for AIS-thorax. The radiomics score achieved a sensitivity and specificity of 0.80 and 0.76.
This study proposes a radiomics-based algorithm for the prediction of ARDS in polytraumatized patients at the time of admission to hospital with an accuracy that competes and surpasses conventional scores despite the heterogeneous, and therefore more realistic, scanning protocols.
• Early prediction of acute respiratory distress syndrome in polytraumatized patients is possible, even when using heterogenous data. • Radiomics-based prediction resulted in an area under the curve of 0.79 compared to 0.66 for the injury severity score, and 0.68 for the abbreviated injury score of the thorax. • Highlighting the most relevant lung regions for prediction facilitates the understanding of machine learning-based prediction.
急性呼吸窘迫综合征（ARDS）是决定多发伤患者临床转归的主要因素。ARDS 的早期预测对于及时进行支持治疗以降低发病率和死亡率至关重要。本研究的目的是开发和验证一种基于机器学习的方法，用于预测从入院后首次 CT 扫描中提取的多发伤患者 ARDS 的发生。
本研究前瞻性纳入 123 例（86 例男性，37 例女性，年龄 41.2±16.4 岁）ISS 评分≥16 分（31.9±10.9 分）的患者，且均在受伤后 1 h 内行 CT 扫描。采用基于深度学习的算法自动对肺（包括气腔和胸腔积液）进行分割。随后，我们从肺内提取放射组学特征，并训练梯度提升树（GBT）集成来预测未来 ARDS 的发生。
在交叉验证中，放射组学评分预测 ARDS 的 AUC 为 0.79，而 ISS 和胸部损伤严重程度评分（AIS-thorax）的 AUC 分别为 0.66 和 0.68。与 ISS 和 AIS-thorax 的 AUC（分别为 0.53 和 0.57）相比，放射组学评分的 f1 评分为 0.70。放射组学评分的灵敏度和特异度分别为 0.80 和 0.76。
本研究提出了一种基于放射组学的算法，用于预测入院时多发伤患者的 ARDS，其准确性可与传统评分相媲美，甚至超越传统评分，尽管该评分采用了异质（因此更真实）的扫描方案。
· 即使使用异质数据，多发伤患者的急性呼吸窘迫综合征也可实现早期预测。
· 与 ISS（0.66）和 AIS-thorax（0.68）相比，基于放射组学的预测 AUC 为 0.79。
· 强调预测中最相关的肺区有助于理解基于机器学习的预测。
