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Cardiac computed tomography (CT) scanning is an advanced cardiac imaging technique that utilizes CT technology, with or without intravenous (IV) contrast, to produce accurate cross-sectional virtual slices of specific areas of the heart, coronary circulation, and major blood vessels such as the aorta, pulmonary veins, and arteries. The computer processes these slices to generate three-dimensional images. Multidetector CT (MDCT) is a rapid form of CT scanning that captures multiple slices...
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Explainable Machine Learning for Estimating the Contrast Material Arrival Time in Computed Tomography Pulmonary

Xiang-Pan Meng1, Haomei Yu1, Changjie Pan2

  • 1Department of Radiology, the Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Nanjing Medical University.

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|September 8, 2025
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Summary

An explainable machine learning model accurately predicts pulmonary artery contrast arrival time (TARR) using CTPA features. This approach aids in personalizing CT pulmonary angiography scans for better patient outcomes.

Keywords:
angiographyinterpretable modelmachine learningpulmonary embolismtomographyx-ray computed

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Accurate contrast arrival time (TARR) in CT pulmonary angiography (CTPA) is crucial for diagnosis.
  • Predicting TARR using non-invasive features can optimize scan protocols.

Purpose of the Study:

  • To develop an explainable machine learning (ML) model for predicting pulmonary artery TARR in CTPA.
  • Utilize patient and noncontrast CT features for TARR prediction.

Main Methods:

  • Retrospective study of 666 patients undergoing CTPA.
  • Employed recursive feature elimination and XGBoost with SHAP for ML modeling.
  • External validation was performed to assess model generalizability.

Main Results:

  • ML models achieved high performance (AUC > 0.83) in predicting abnormal TARR (<7s or >10s).
  • SHAP analysis highlighted vena cava and pulmonary artery measurements as key predictors.
  • The models demonstrated strong performance on both testing and external validation sets.

Conclusions:

  • An explainable ML algorithm accurately identifies normal and abnormal pulmonary artery TARR.
  • This approach facilitates personalized CTPA scan protocols.