Radiomics score predicts acute respiratory distress syndrome based on the initial CT scan after trauma

Abstract

Objectives 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. Materials and methods 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. Results 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. Conclusions 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. Key Points • 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.

Does Acuity and Severity of Injury Affect Trauma Whole-Body CT Report Turnaround Time? A Large-scale Study

“…Commercial CT-based tools are available for triage or detection and grading of intracranial hemorrhage and spine fractures. Proof-of-concept tools in earlier stages of the R&D pipeline include those for detection of active hemorrhage and aortic injury; detection and grading of pelvic fractures; detection, quanti cation, and grading of solid organ lacerations; as well as detection and quanti cation of traumatic extraperitoneal pelvic hematoma, hemoperitoneum, hemothorax, and pulmonary contusion [22,[42][43][44][45][46][47][48][49][50][51][52][53][54][55][56].…”

Lung Imaging and Artificial Intelligence in ARDS

“…In addition to COVID-19 pneumonia, blunt chest trauma is also currently associated with parenchymal lung injury to various extents, which may increase the risk of developing ARDS. Typically, the presence of an injury severity score (ISS) greater than 25 significantly increases the risk of developing ARDS [18]. Moreover, it has previously been shown that trauma patients with pulmonary contusions involving at least 20% of the total lung volume have a significantly higher risk of developing ARDS [40].…”

Lung Imaging and Artificial Intellingence in ARDS

“…Blunt chest trauma is currently associated with parenchymal lung injury at various extents, which may increase the risk of developing ARDS. Usually, the presence of an injury severity score (ISI) higher than 25 significantly increases the risk of developing ARDS [8]. In addition, it has previously been shown that trauma patients with pulmonary contusions involving at least 20% of the total lung volume had a significantly higher risk of developing ARDS [9].…”

“…One hundred twenty-three patients were enrolled. The model consisted in a fully automated ML and radiomics based approach, that had a higher accuracy compared to an established score (ISS and abbreviated injury score of the thorax) to identify ARDS in trauma patients [8]. In this line, a rapid automated chest CT volumetry assessment of pulmonary contusion in trauma patients showed a good accuracy to assess the risk of ARDS, length of intensive care and time on mechanical ventilation [10].…”