Zhong Zhusi, Zhang Helen, Fayad Fayez H, Lancaster Andrew C, Sollee John, Kulkarni Shreyas, Lin Cheng Ting, Li Jie, Gao Xinbo, Collins Scott, Greineder Colin F, Ahn Sun H, Bai Harrison X, Jiao Zhicheng, Atalay Michael K
Department of Diagnostic Radiology, Rhode Island Hospital.
Warren Alpert Medical School of Brown University, Providence, RI.
J Thorac Imaging. 2025 Sep 1;40(5):e0831. doi: 10.1097/RTI.0000000000000831.
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 survival.
In total, 918 patients (median age 64 y, range 13 to 99 y, 48% male) with 3978 CTPAs were identified via retrospective review across 3 institutions. To predict survival, an AI model was used to extract disease-related imaging features from CTPAs. Imaging features and clinical variables were then incorporated into independent DL models to predict survival outcomes. Cross-modal fusion CoxPH models were used to develop multimodal models from combinations of DL models and calculated PESI scores. Five multimodal models were developed as follows: (1) using CTPA imaging features only, (2) using clinical variables only, (3) using both CTPA and clinical variables, (4) using CTPA and PESI score, and (5) using CTPA, clinical variables, and PESI score. Performance was evaluated using the concordance index (c-index). Kaplan-Meier analysis was performed to stratify patients into high-risk and low-risk groups. Additional factor-risk analysis was conducted to account for right ventricular (RV) dysfunction.
For both data sets, the multimodal models incorporating CTPA features, clinical variables, and PESI score achieved higher c-indices than PESI alone. Following the stratification of patients into high-risk and low-risk groups by models, survival outcomes differed significantly (both P <0.001). A strong correlation was found between high-risk grouping and RV dysfunction.
Multiomic DL models incorporating CTPA features, clinical data, and PESI achieved higher c-indices than PESI alone for PE survival prediction.
肺栓塞（PE）是美国死亡的一个重要原因。本研究的目的是使用计算机断层扫描肺动脉造影（CTPA）、临床数据和PE严重程度指数（PESI）评分来实施深度学习（DL）模型，以预测PE患者的生存率。
通过对3家机构的回顾性研究，共纳入918例患者（中位年龄64岁，范围13至99岁，48%为男性），有3978次CTPA检查。为了预测生存率，使用人工智能模型从CTPA中提取与疾病相关的影像特征。然后将影像特征和临床变量纳入独立的DL模型，以预测生存结果。使用跨模态融合CoxPH模型，从DL模型和计算出的PESI评分的组合中开发多模态模型。开发了五个多模态模型如下：（1）仅使用CTPA影像特征，（2）仅使用临床变量，（3）同时使用CTPA和临床变量，（4）使用CTPA和PESI评分，（5）使用CTPA、临床变量和PESI评分。使用一致性指数（c指数）评估性能。进行Kaplan-Meier分析，将患者分为高风险和低风险组。进行额外的因素风险分析，以考虑右心室（RV）功能障碍。
对于两个数据集而言，纳入CTPA特征、临床变量和PESI评分的多模态模型比单独使用PESI的c指数更高。通过模型将患者分为高风险和低风险组后，生存结果有显著差异（均P<0.001）。发现高风险分组与RV功能障碍之间存在很强的相关性。
纳入CTPA特征、临床数据和PESI的多组学DL模型在预测PE生存率方面比单独使用PESI的c指数更高。
