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CT-based Radiologic Ternary Classification Model in Predicting Pathologic Invasiveness of Pulmonary Nonsolid Nodules.

Qi Wan1, Qiao Zou1, Chongpeng Sun1

  • 1Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, No. 151 Yanjiang West Rd, Yuexiu District, Guangzhou, Guangdong, China 510120.

Radiology
|December 23, 2025
PubMed
Summary

A new CT classification model accurately differentiates lung adenocarcinoma invasiveness in nonsolid nodules (NSNs). This tool aids clinical decisions by distinguishing preinvasive lesions, minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC).

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Area of Science:

  • Radiology
  • Oncology
  • Medical Imaging Analysis

Background:

  • Accurate assessment of lung adenocarcinoma invasiveness in nonsolid nodules (NSNs) using CT is crucial for clinical decision-making but remains challenging.
  • Distinguishing between preinvasive lesions, minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC) based on CT characteristics is clinically significant.

Purpose of the Study:

  • To identify CT imaging features of NSNs associated with pathologic invasiveness in lung adenocarcinoma.
  • To develop and validate a radiologic ternary classification model for differentiating NSN invasiveness subtypes.

Main Methods:

  • Retrospective analysis of 2125 NSNs from 1683 patients with pathologically confirmed lung adenocarcinoma on preoperative CT scans.
  • Radiologists evaluated NSN features including size, location, margin, density, lobulation, air bronchogram, and pleural retraction.
  • Statistical analysis using univariable ordinal regression and partial proportional odds models to develop a ternary classification model.

Main Results:

  • Key radiologic predictors of invasiveness included nodule diameter, number of intranodular vessels, CT attenuation, density heterogeneity, spiculation, lobulation, pleural retraction, bubble lucency, and air bronchogram.
  • The developed radiologic ternary classification model demonstrated excellent diagnostic performance with a C-index of 0.92.
  • Incorporating CT attenuation and morphologic features significantly improved model performance compared to nodule diameter alone.

Conclusions:

  • A novel radiologic ternary classification model shows excellent performance in differentiating preinvasive lesions, MIA, and IAC in NSNs on CT.
  • This model can aid clinicians in making more informed decisions regarding the management of lung adenocarcinoma NSNs.