This content originally appeared on HackerNoon and was authored by Tomography
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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.
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Figure
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\ 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.
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\ 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.
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\ 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.
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\ Risk scores were calculated between imaging and multimodal (+Clinical), clinical and multimodal (+Imaging), and multimodal and PESI-fused (+PESI) models for each dataset.
\ INSTITUTION3 = INSTITUTION3. PESI = Pulmonary Embolism Severity Index. INSTITUTION1 = INSTITUTION1. INSTITUTION1tr = training set. INSTITUTION1ts = internal test set. INSTITUTION2 = INSTITUTION2. INSTITUTION2- INSTITUTION3 = external test set.
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:::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.
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:::info This paper is available on arxiv under CC BY 4.0 DEED license.
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This content originally appeared on HackerNoon and was authored by Tomography
Tomography | Sciencx (2025-10-03T16:15:03+00:00) AI for Risk Stratification: Multimodal DL Models Offer Enhanced Prognosis for Pulmonary Embolism. Retrieved from https://www.scien.cx/2025/10/03/ai-for-risk-stratification-multimodal-dl-models-offer-enhanced-prognosis-for-pulmonary-embolism/
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