This preliminary study investigated the feasibility of a combined model constructed using radiomic features based on computed tomography (CT) and clinical features to predict adverse clinical outcomes in acute pulmonary embolism (APE). Currently, there is no widely recognized predictive model. Patients with confirmed APE who underwent CT pulmonary angiography were retrospectively categorized into good and poor prognosis groups. Seventy-four patients were randomized into a training (n = 51) or validation (n = 23) cohort. Feature extraction was performed using 3D-Slicer software. The least absolute shrinkage and selection operator regression was used to identify the optimal radiomics features and calculate the radiomics scores; subsequently, the radiomics model was developed. A combined predictive model was constructed based on radiomics scores and selected clinical features. The predictive efficacy of the three models (radiomics, clinical and combined) was assessed by plotting receiver operating characteristic curves. Furthermore, the calibration curves were graphed and the decision curve analysis was performed. Four radiomic features were screened to calculate the radiomic score. Right ventricular to left ventricular ratio (RV/LV) ≥ 1.0 and radiomics score were independent risk factors for adverse clinical outcomes. In the training and validation cohorts, the areas under the curve (AUCs) for the RV/LV ≥ 1.0 (clinical) and radiomics score prediction models were 0.778 and 0.833 and 0.907 and 0.817, respectively. The AUCs for the combined model of RV/LV ≥ 1.0 and radiomics score were 0.925 and 0.917, respectively. The combined and radiomics models had high clinical assessment efficacy for predicting adverse clinical outcomes in APE, demonstrating the clinical utility of both models. Calibration curves exhibited a strong level of consistency between the predictive and observed probabilities of poor and good prognoses in the combined model. The combined model of RV/LV ≥ 1.0 and radiomics score based on CT could accurately and non-invasively predict adverse clinical outcomes in patients with APE.

这项初步研究调查了使用基于计算机断层扫描 (CT) 的放射组学特征和临床特征构建的组合模型来预测急性肺栓塞 (APE) 不良临床结果的可行性。目前，还没有广泛认可的预测模型。将接受 CT 肺动脉造影的确诊 APE 患者回顾性分为预后良好组和预后不良组。74 名患者被随机分为训练组 (n = 51) 或验证组 (n = 23)。使用 3D-Slicer 软件进行特征提取。使用最小绝对收缩和选择算子回归来识别最佳放射组学特征并计算放射组学分数；随后，开发了放射组学模型。根据放射组学评分和选定的临床特征构建了组合预测模型。通过绘制受试者工作特征曲线来评估三种模型（放射组学、临床和组合）的预测功效。此外，绘制了校准曲线并进行了决策曲线分析。筛选了四种放射组学特征以计算放射组学评分。右心室与左心室比值（RV/LV）≥1.0和放射组学评分是不良临床结果的独立危险因素。在训练和验证队列中，RV/LV ≥ 1.0（临床）和放射组学评分预测模型的曲线下面积 (AUC) 分别为 0.778 和 0.833 以及 0.907 和 0.817。RV/LV ≥ 1.0 和放射组学评分组合模型的 AUC 分别为 0.925 和 0.917。组合模型和放射组学模型对于预测 APE 不良临床结果具有较高的临床评估功效，证明了两种模型的临床实用性。校准曲线在组合模型中表现出不良预后和良好预后的预测概率和观察概率之间的高度一致性。RV/LV≥1.0和基于CT的放射组学评分的组合模型可以准确、无创地预测APE患者的不良临床结局。