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Related Experiment Video

Updated: Jun 12, 2026

A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules
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Published on: May 19, 2023

Assessing Invasiveness of Ground-Glass Nodules Using Ternary-Class CT Radiomics Models: A Multi-Center Study with

Pingshan Zhao1, Haoran Chen2, Hongxian Gu3

  • 1Department of Radiology, Shaoxing Hospital of Traditional Chinese Medicine, Shaoxing, Zhejiang, People's Republic of China.

Cancer Management and Research
|June 11, 2026
PubMed
Summary

Machine learning models accurately classify ground-glass nodules (GGNs) invasiveness. The multi-layer perceptron (MLP) model shows promise for stratifying GGNs into precursor glandular lesions (PGL), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC) subtypes.

Keywords:
computed tomographyground glass noduleinvasivenesslung adenocarcinomaradiomics

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Oncology

Background:

  • Ground-glass nodules (GGNs) require tailored management based on invasiveness.
  • Accurate stratification of GGNs is crucial for effective clinical protocols.

Purpose of the Study:

  • To develop and evaluate machine learning models for ternary classification of GGNs.
  • To stratify GGNs into precursor glandular lesions (PGL), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC).

Main Methods:

  • A multi-center, retrospective study of 1130 GGN patients.
  • Extraction of computed tomography (CT) radiomics features.
  • Development of six ternary classification models, including Logistic Regression, RandomForest, ExtraTrees, XGBoost, LightGBM, and multi-layer perceptron (MLP).

Main Results:

  • The MLP model achieved the highest ternary classification accuracy (training: 0.712, testing: 0.658).
  • MLP model demonstrated superior AUCs (micro: 0.877 train, 0.808 test; macro: 0.861 train, 0.799 test).
  • MLP model outperformed other evaluated models in predictive performance.

Conclusions:

  • Ternary machine learning models, especially the MLP model, can effectively stratify GGN invasiveness.
  • This stratification aids in optimizing clinical decision-making for GGNs.
  • The models support precision therapeutic planning and personalized management strategies for GGNs.