Pulmonary contusion: automated deep learning-based quantitative visualization.
Pulmonary contusion: automated deep learning-based quantitative visualization.
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DOI：
10.1007/s10140-023-02149-2
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发表时间：
2023-08
影响因子：
2.2
通讯作者：
Dreizin, David
中科院分区：
文献类型：
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作者：
Sarkar, Nathan;Zhang, Lei;Campbell, Peter;Liang, Yuanyuan;Li, Guang;Khedr, Mustafa;Khetan, Udit;Dreizin, David
肺部挫伤的快速自动CT容积测量可能预测急性呼吸窘迫综合征（ARDS）的进展，并有助于指导高危创伤患者的早期临床管理。本研究旨在训练和验证最先进的深度学习模型，以将肺部挫伤量化为占全肺体积的百分比（肺挫伤指数，或自动 - LCI），并评估自动 - LCI与相关临床结果之间的关系。
从2016年至2021年的报告中回顾性地确定了302名患有肺部挫伤的成年患者（年龄≥18岁）。nnU - Net在手动挫伤和全肺分割数据上进行训练。多变量回归的床旁候选变量包括入院时的血氧饱和度、心率和收缩压。逻辑回归用于评估ARDS风险，Cox比例风险模型用于确定重症监护病房（ICU）住院时间和机械通气时间的差异。
平均体积相似性指数和平均Dice分数分别为0.82和0.67。真实值与预测体积之间的组内相关系数和皮尔逊相关系数分别为0.90和0.91。38名（14%）患者发生了ARDS。在双变量分析中，自动 - LCI与ARDS（p < 0.001）、入住ICU（p < 0.001）以及机械通气需求（p < 0.001）相关。在多变量分析中，自动 - LCI与ARDS（p = 0.04）、在ICU停留时间更长（p = 0.02）以及机械通气时间更长（p = 0.04）相关。使用自动 - LCI和临床变量进行多变量回归预测ARDS的曲线下面积（AUC）为0.70，而仅使用自动 - LCI的AUC为0.68。
自动 - LCI值升高与ARDS风险增加、入住ICU时间延长以及机械通气时间延长相对应。
Rapid automated CT volumetry of pulmonary contusion may predict progression to Acute Respiratory Distress Syndrome (ARDS) and help guide early clinical management in at-risk trauma patients. This study aims to train and validate state-of-the-art deep learning models to quantify pulmonary contusion as a percentage of total lung volume (Lung Contusion Index, or auto-LCI) and assess the relationship between auto-LCI and relevant clinical outcomes. 302 adult patients (age ≥ 18) with pulmonary contusion were retrospectively identified from reports between 2016 and 2021. nnU-Net was trained on manual contusion and whole-lung segmentations. Point-of-care candidate variables for multivariate regression included oxygen saturation, heart rate, and systolic blood pressure on admission. Logistic regression was used to assess ARDS risk, and Cox proportional hazards models were used to determine differences in ICU length of stay and mechanical ventilation time. Mean Volume Similarity Index and mean Dice scores were 0.82 and 0.67. Interclass correlation coefficient and Pearson r between ground-truth and predicted volumes were 0.90 and 0.91. 38 (14%) patients developed ARDS. In bivariate analysis, auto-LCI was associated with ARDS (p < 0.001), ICU admission (p < 0.001), and need for mechanical ventilation (p < 0.001). In multivariate analyses, auto-LCI was associated with ARDS (p = 0.04), longer length of stay in the ICU (p = 0.02) and longer time on mechanical ventilation (p = 0.04). AUC of multivariate regression to predict ARDS using auto-LCI and clinical variables was 0.70 while AUC using auto-LCI alone was 0.68. Increasing auto-LCI values corresponded with increased risk of ARDS, longer ICU admissions, and longer periods of mechanical ventilation.
人体成分的体积标记可能会改善骨盆骨折后主要动脉出血的个性化预测：巴尔的摩CT预测模型队列的次要分析。
DOI：
10.1177/0846537120952508
发表时间：
2021-11
期刊：
Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes
影响因子：
--
作者：
Dreizin D;Rosales R;Li G;Syed H;Chen R
通讯作者：
Chen R