Purpose: Pulmonary embolism (PE) is a significant cause of mortality in the
United States. The objective of this study is to implement deep learning (DL)
models using Computed Tomography Pulmonary Angiography (CTPA), clinical data,
and PE Severity Index (PESI) scores to predict PE mortality. Materials and
Methods: 918 patients (median age 64 years, range 13-99 years, 52% female) with
3,978 CTPAs were identified via retrospective review across three institutions.
To predict survival, an AI model was used to extract disease-related imaging
features from CTPAs. Imaging features and/or clinical variables were then
incorporated into DL models to predict survival outcomes. Four models were
developed as follows: (1) using CTPA imaging features only; (2) using clinical
variables only; (3) multimodal, integrating both CTPA and clinical variables;
and (4) multimodal fused with calculated PESI score. Performance and
contribution from each modality were evaluated using concordance index
(c-index) and Net Reclassification Improvement, respectively. Performance was
compared to PESI predictions using the Wilcoxon signed-rank test. Kaplan-Meier
analysis was performed to stratify patients into high- and low-risk groups.
Additional factor-risk analysis was conducted to account for right ventricular
(RV) dysfunction. Results: For both data sets, the PESI-fused and multimodal
models achieved higher c-indices than PESI alone. Following stratification of
patients into high- and low-risk groups by multimodal and PESI-fused models,
mortality outcomes differed significantly (both p<0.001). A strong correlation
was found between high-risk grouping and RV dysfunction. Conclusions: Multiomic
DL models incorporating CTPA features, clinical data, and PESI achieved higher
c-indices than PESI alone for PE survival prediction.

通过使用 CTPA 成像、临床数据和 PE 严重指数（PESI）评分来预测 PE 的死亡率，研究采用深度学习模型来预测 PE 的死亡率，并将疾病相关的成像特征和 / 或临床变量纳入 DL 模型中进行预测，结果表明多组学 DL 模型在 PE 存活预测中的 c-index 值明显高于仅使用 PESI 指数的模型。