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Updated: Feb 12, 2026

Multifractal Spectrum Analysis for Assessing Pulmonary Nodule Malignancy
Published on: January 10, 2025
Predicting malignancy of pulmonary ground-glass nodules and their invasiveness by random forest
Xueyan Mei1, Rui Wang2, Wenjia Yang3
1Department of Applied Biomedical Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA.
Background:
The purpose of this study was to develop a predictive model that could accurately predict the malignancy of the pulmonary ground-glass nodules (GGNs) and the invasiveness of the malignant GGNs.
Methods:
The authors built two binary classification models that could predict the malignancy of the pulmonary GGNs and the invasiveness of the malignant GGNs.
Results:
Results of our developed model showed random forest could achieve 95.1% accuracy to predict the malignancy of GGNs and 83.0% accuracy to predict the invasiveness of the malignant GGNs.
Conclusions:
The malignancy and invasiveness of pulmonary GGNs could be predicted by random forest.
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