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Updated: Jan 18, 2026

Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

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Published on: May 19, 2023

Prediction of Pulmonary Ground-Glass Nodule Progression State on Initial Screening CT Using a Radiomics-Based Model.

Liang Jin¹, Zhongsheng Liu², Yingli Sun¹

¹Radiology Department, Huadong Hospital, Fudan University, Shanghai, China.

Respirology (Carlton, Vic.)

|September 8, 2025

Summary

This study developed a radiomics-based machine learning model to predict the progression of pulmonary ground-glass nodules (GGNs) on CT scans. The model accurately forecasts GGN absorption or persistence, aiding clinical decision-making.

Keywords:

absorption radiomics solitary pulmonary nodule x‐ray computed tomography

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Published on: December 19, 2020

Area of Science:

Radiology
Medical Imaging
Machine Learning

Background:

Diagnosing pulmonary ground-glass nodules (GGNs) on chest CT is challenging.
GGN progression (absorption or persistence) dictates treatment.
Accurate prediction of GGN state is crucial for patient management.

Purpose of the Study:

To develop and validate a radiomics-based machine learning model.
To predict the progressive state (absorption or persistence) of pulmonary GGNs.
To improve clinical treatment and decision-making for GGNs.

Main Methods:

Retrospective analysis of 672 patients with GGNs.
Extraction of radiomic features from chest CT imaging.
Development of three predictive models: Rad-score, clinical factors, and combined model.
Validation using receiver operating characteristic curves, accuracy, sensitivity, and specificity.

Main Results:

The combined model (Model 3) demonstrated superior predictive performance.
Model 3 achieved an AUC of 0.959, accuracy of 0.881, sensitivity of 0.902, and specificity of 0.856.
The radiomics model outperformed experienced radiologists in predicting GGN progression.

Conclusions:

A radiomics-based machine learning model effectively predicts GGN progression on initial CT.
This validated model shows potential for enhancing GGN follow-up management.
The findings support the use of AI in diagnosing and managing pulmonary nodules.