Predictive Factors and Nomogram for Malignant Pulmonary Nodules (≤ 1 cm)

Area of Science:

• Pulmonary medicine
• Radiology
• Oncology

Background:

• Accurate prediction of malignancy in small pulmonary nodules (≤10 mm) is crucial for patient management.
• Existing models often lack sufficient predictive power for these small lesions.

Purpose of the Study:

• To identify key factors predicting malignancy in pulmonary nodules ≤10 mm.
• To develop and evaluate a risk prediction model (nomogram) for these nodules.

Main Methods:

• Retrospective analysis of 298 patients with pulmonary nodules ≤1 cm.
• Inclusion of variables: sex, smoking, nodule position, density, enhancement, diameter, and calcification.
• Development of a nomogram using forward stepwise selection.

Main Results:

• The nomogram achieved an area under the curve (AUC) of 0.79.
• Independent predictors of malignancy included partial-solid/nonsolid density, larger diameter, and absence of calcification.
• Optimal threshold yielded 70% sensitivity, 79% specificity, and 77% accuracy.

Conclusions:

• Nodule density, diameter, and calcification are significant predictors of malignancy in small pulmonary nodules.
• The developed nomogram demonstrates good predictive performance.
• External validation is recommended, particularly concerning the model's sensitivity.