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This conclusion affirms that multiomic Deep Learning (DL) models combining CTPA features and clinical data demonstrate superior performance to the PESI score for PE mortality prediction.This conclusion affirms that multiomic Deep Learning (DL) models combining CTPA features and clinical data demonstrate superior performance to the PESI score for PE mortality prediction.

AI for Risk Stratification: Multimodal DL Models Offer Enhanced Prognosis for Pulmonary Embolism

2025/10/04 00:15

Table of Links

Abstract

Introduction
Methods
Results
Discussion
Conclusions, Acknowledgments, and References

5. Conclusions

Multiomic DL models based on combined CTPA features and clinical variables demonstrated improved performance compared to PESI score alone for mortality prediction in PE. The addition of PESI to the multimodal model demonstrated only a marginal performance improvement, illustrating that AI-based models are sufficiently capable of survival prediction. The multimodal models similarly improved performance upon PESI alone in 30-day mortality risk estimation. Through NRI analysis, clinical and imaging data were both independently shown to contribute to improved performance of the multimodal model. These findings demonstrate the strength of a multimodal DL model in comparison to the current clinical standard of PESI, turning prognosis into an intelligent process that integrates greater clinical and imaging information. Additionally, we demonstrated concordance of our model with clinical indicators of mortality, such as RV dysfunction. Further analysis can shed more light on the connectedness of various risk factors with mortality in PE patients, and how this information can be leveraged for model development in survival prediction. However, the benefits of our model can only be confirmed by additional validation on larger and more diverse datasets, as well as prospective testing of the developed models.

\ Our study highlights the utility of DL-based models in prognostication and risk stratification in patients with PE. AI has the potential to improve the clinical workflow for radiologists and clinicians by providing rapid and accurate diagnostic and prognostic information. By offering timely yet accurate risk stratification for PE patients, AI may offer a substantial benefit to patients and providers by informing clinical decision-making, potentially improving patient outcomes.

Acknowledgments

None.

References

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Figure

Figure 1. Data Analysis Workflow. This Central Illustration provides an overview of the data analysis workflow, including the proposed Pulmonary Embolism (PE) deep survival analysis framework.

\ Figure 2. Class Activation Maps (CAMs). Class activation maps (CAMs) highlight the image areas most important for PE detection model decision-making.

\ Figure 3. Performance of deep survival analysis models. Comparison of deep survival analysis models’ overall performance on different testing datasets.PESI = Pulmonary Embolism Severity Index. INSTITUTION1ts = internal test set. INSTITUTION2-INSTITUTION3 = external test set.

\ Figure 4. Kaplan-Meier curves. Kaplan-Meier curves for INSTITUTION1ts (left) and INSTITUTION2- INSTITUTION3 (right) with patients stratified into high- and low-risk groupsby the PESI-fused model. INSTITUTION1ts = internal test set. INSTITUTION2-INSTITUTION3 = external test set.

\ Figure 5. Feature Importance. Predictive ability of each clinical feature (left) and feature importance in AI model (right).INSTITUTION1ts = internal test set. INSTITUTION2-INSTITUTION3 = external test set.

\ Figure 6. Predicted risk distribution of external testing set. Figure (a) showcases 16 patients with RV dysfunction, 68.8% of which are high-risk, and Figure (b) demonstrates a high correlation between high-risk identification and mortality. (a) Diamonds represent PE patients with RV dysfunction. (b) Triangles represent mortality.

\ Table 1. Patient characteristics.

\ Detailed patient characteristics of PESI clinical variables used to calculate PESI score for each patient.

\ All continuous variables are reported as median (interquartile range), and all categorical variables are reported as number (%). Statistically significant p-values are bolded (p < 0.05). Deceased status is not a PESI clinical variable.

\ BP = Blood Pressure. PESI = Pulmonary Embolism Severity Index.

\ Table 2. Overall survival prediction performance.

\ Overall c-index values and corresponding 95% confidence intervals of PESI and prediction models.

\ INSTITUTION3 = INSTITUTION3. PESI = Pulmonary Embolism Severity Index. RSF = Random Survival Forest. INSTITUTION1 = INSTITUTION1. INSTITUTION1tr = training set. INSTITUTION1ts = internal test set. INSTITUTION2 = INSTITUTION2. INSTITUTION2- INSTITUTION3 = external test set.

\ Table 3. Short term survival prediction performance.

\ Short term (30-day) survival prediction performance as measured by c-index values and corresponding 95% confidence intervals of PESI and prediction models.

\ INSTITUTION3 = INSTITUTION3. PESI = Pulmonary Embolism Severity Index. INSTITUTION1 = INSTITUTION1. INSTITUTION1tr = training set. INSTITUTION1ts = internal test set. INSTITUTION2 = INSTITUTION2. INSTITUTION2- INSTITUTION3 = external test set.

\ Table 4. Net Reclassification Improvement (NRI) analysis.

\ Risk scores were calculated between imaging and multimodal (+Clinical), clinical and multimodal (+Imaging), and multimodal and PESI-fused (+PESI) models for each dataset.

:::info Authors:

(1) Zhusi Zhong, BS, a Co-first authors from Department of Diagnostic Radiology, Rhode Island Hospital, Providence, RI, 02903, USA, Warren Alpert Medical School of Brown University, Providence, RI, 02903, USA, and School of Electronic Engineering, Xidian University, Xi’an 710071, China;

(2) Helen Zhang, BS, a Co-first authors from Department of Diagnostic Radiology, Rhode Island Hospital, Providence, RI, 02903, USA and Warren Alpert Medical School of Brown University, Providence, RI, 02903, USA;

(3) Fayez H. Fayad, BA, a Co-first authors from Department of Diagnostic Radiology, Rhode Island Hospital, Providence, RI, 02903, USA and Warren Alpert Medical School of Brown University, Providence, RI, 02903, USA;

(4) Andrew C. Lancaster, BS, Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA and Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA;

(5) John Sollee, BS, Department of Diagnostic Radiology, Rhode Island Hospital, Providence, RI, 02903, USA and Warren Alpert Medical School of Brown University, Providence, RI, 02903, USA;

(6) Shreyas Kulkarni, BS, Department of Diagnostic Radiology, Rhode Island Hospital, Providence, RI, 02903, USA and Warren Alpert Medical School of Brown University, Providence, RI, 02903, USA;

(7) Cheng Ting Lin, MD, Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA;

(8) Jie Li, PhD, School of Electronic Engineering, Xidian University, Xi’an 710071, China;

(9) Xinbo Gao, PhD, School of Electronic Engineering, Xidian University, Xi’an 710071, China;

(10) Scott Collins, Department of Diagnostic Radiology, Rhode Island Hospital, Providence, RI, 02903, USA and Warren Alpert Medical School of Brown University, Providence, RI, 02903, USA;

(11) Colin Greineder, MD, Department of Pharmacology, Medical School, University of Michigan, Ann Arbor, MI, 48109, USA;

(12) Sun H. Ahn, MD, Department of Diagnostic Radiology, Rhode Island Hospital, Providence, RI, 02903, USA and Warren Alpert Medical School of Brown University, Providence, RI, 02903, USA;

(13) Harrison X. Bai, MD, Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA;

(14) Zhicheng Jiao, PhD, Department of Diagnostic Radiology, Rhode Island Hospital, Providence, RI, 02903, USA and Warren Alpert Medical School of Brown University, Providence, RI, 02903, USA;

(15) Michael K. Atalay, MD, PhD, Department of Diagnostic Radiology, Rhode Island Hospital, Providence, RI, 02903, USA and Warren Alpert Medical School of Brown University, Providence, RI, 02903, USA.

:::

:::info This paper is available on arxiv under CC BY 4.0 DEED license.

:::

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