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Related Concept Videos

Radiological Investigation III: Pulmonary Angiogram and PET Scan01:13

Radiological Investigation III: Pulmonary Angiogram and PET Scan

Radiological investigations are paramount in the diagnosis and management of various pulmonary diseases. Two essential investigations are the Pulmonary Angiogram and the Positron Emission Tomography (PET) Scan.
Pulmonary Angiogram
A Pulmonary Angiogram is an invasive procedure involving injecting a contrast medium through a catheter threaded into the pulmonary artery or the right side of the heart to visualize the pulmonary vasculature. Computed Tomography (CT) scans have mainly replaced this...

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Related Experiment Video

Updated: Jul 12, 2026

A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules
10:26

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Published on: May 19, 2023

Deep learning-based malignancy probability estimation of pulmonary nodules in PET/CT imaging.

Lars Leijten1, Erik H J G Aarntzen2,3, Roel L J Verhoeven4

  • 1Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands. lars.leijten@radboudumc.nl.

European Radiology
|July 10, 2026
PubMed
Summary

A new deep learning model (AITO-PETCT-MP) for pulmonary nodule malignancy estimation on [18F]FDG-PET/CT shows non-inferior performance to the Herder model. This imaging-only model performs comparably to expert clinicians, offering a potential improvement in diagnostic accuracy.

Keywords:
Deep learningLung neoplasmsMultiple pulmonary nodulesPositron emission tomography computed tomographySolitary pulmonary nodule

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Published on: January 10, 2025

Area of Science:

  • Radiology
  • Artificial Intelligence
  • Oncology

Background:

  • Current British Thoracic Society (BTS) guidelines recommend [18F]FDG-PET/CT followed by Herder model risk stratification for suspicious pulmonary nodules.
  • The Herder model's reliance on limited imaging features may impact diagnostic accuracy.

Purpose of the Study:

  • To develop and evaluate a deep learning (DL) model, AITO-PETCT-MP, for malignancy probability estimation in pulmonary nodules using PET/CT imaging.
  • To compare the diagnostic performance of AITO-PETCT-MP against the Herder model and expert clinicians.

Main Methods:

  • A retrospective study of 533 indeterminate pulmonary nodules (268 malignant) from 436 patients.
  • Histopathology or a 2-year benign registry follow-up served as the reference standard.
  • Diagnostic performance was assessed on a test set of 161 nodules (80 malignant) in a reader study comparing AITO-PETCT-MP, the Herder model, and seven clinicians.

Main Results:

  • AITO-PETCT-MP achieved an AUC of 0.78, demonstrating non-inferiority to the Herder model (AUC 0.73, p=0.005).
  • The average AUC for clinicians was 0.80.
  • The Herder model referred more benign nodules for treatment (26/81) compared to AITO-PETCT-MP and clinicians (3/81).
  • AITO-PETCT-MP and clinicians assigned more malignant cases to CT surveillance than the Herder model.

Conclusions:

  • The PET/CT-based deep learning model (AITO-PETCT-MP) shows comparable performance to expert clinicians and is non-inferior to the Herder model for pulmonary nodule malignancy estimation.
  • The study highlights potential differences in patient management recommendations between the Herder model and current clinical practice based on BTS follow-up categories.