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Related Experiment Video

Updated: Nov 10, 2025

Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules
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The Effects of Perinodular Features on Solid Lung Nodule Classification.

José Lucas Leite Calheiros1, Lucas Benevides Viana de Amorim2, Lucas Lins de Lima2

  • 1Computing Institute, Federal University of Alagoas (UFAL), Maceió, AL, Brazil. lucaslc@uc.ufal.br.

Journal of Digital Imaging
|April 1, 2021
PubMed
Summary

Analyzing the area around lung nodules, known as the perinodular zone, significantly improves the classification of pulmonary nodules. This study demonstrates the value of perinodular radiomic features for more accurate lung cancer diagnosis.

Keywords:
CADxComputed tomographyLung nodule classificationMachine learningPerinodular zoneRadiomics

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Area of Science:

  • Medical Imaging
  • Radiology
  • Artificial Intelligence in Medicine

Background:

  • Lung cancer is a leading cause of cancer death worldwide, with diagnosis often challenging even for experienced radiologists.
  • Computed tomography (CT) is crucial for lung cancer detection, but accurate characterization of nodules remains complex.
  • Current computer-aided diagnosis (CAD) and radiomics tools primarily focus on the intranodular region, potentially overlooking valuable diagnostic information.

Purpose of the Study:

  • To evaluate the impact of incorporating the perinodular zone on the characterization accuracy of lung lesions.
  • To investigate the significance of radiomic attributes from the perinodular region in classifying solid pulmonary nodules.
  • To compare the diagnostic performance of models using both intranodular and perinodular features versus intranodular features alone.

Main Methods:

  • Utilized a large public dataset of solid lung nodule CT images for reproducible research.
  • Extracted fine-tuned radiomic attributes from both the perinodular and intranodular zones of lung nodules.
  • Developed and evaluated machine learning models for lung nodule classification.

Main Results:

  • The best-performing model, integrating perinodular and intranodular features, achieved an average Area Under the Curve (AUC) of 0.916.
  • This combined approach yielded an accuracy of 84.26%, sensitivity of 84.45%, and specificity of 83.84%.
  • Incorporating perinodular zone attributes led to significant improvements across all evaluated performance metrics compared to using only intranodular features.

Conclusions:

  • The perinodular zone contains crucial information for the accurate classification of solid pulmonary nodules.
  • Integrating radiomic features from both intranodular and perinodular regions enhances diagnostic performance in lung nodule characterization.
  • This study underscores the importance of considering the interaction between lung nodules and their surrounding environment for improved lung cancer diagnosis.