What question did this study set out to answer?

The study aims to create a nomogram that combines clinical variables and CT features to predict malignancy in small pulmonary nodules.

June 10, 2026Open Access

Development and internal validation of a clinical nomogram incorporating quantitative CT features for predicting malignancy in pulmonary nodules ≤ 3 cm

Key Points

The study aims to create a nomogram that combines clinical variables and CT features to predict malignancy in small pulmonary nodules.
Retrospective single-center study of 1,419 patients with pulmonary nodules ≤ 3 cm undergoing resection.
Cohort divided into a training set (n = 994) and a validation set (n = 425) for developing and validating the nomogram.
Multivariable logistic regression used to identify malignancy-associated variables and construct the model.
81.0% of nodules (1,150) were malignant; 19.0% were benign (269).
Final nomogram included seven variables associated with malignancy, including suspicious radiologic features, nodule size > 2 cm, and minimum CT attenuation.
The model demonstrated good discrimination (AUC 0.809 in training, 0.782 in validation) and reliability in risk prediction.

Abstract

Accurate differentiation between benign and malignant pulmonary nodules ≤ 3 cm remains a clinical challenge. This study aimed to develop and internally validate a clinically interpretable nomogram integrating clinical variables and quantitative computed tomography (CT) features for predicting malignancy in pulmonary nodules. This retrospective single-center study included 1,419 patients with pulmonary nodules ≤ 3 cm who underwent surgical resection between January 2012 and July 2025 with pathologic confirmation. The cohort was randomly divided into a training set ( n = 994) for model development and a validation set ( n = 425) for internal validation. Clinical data, conventional imaging findings, serum biomarkers, and quantitative CT measurements from preoperative thin-section CT were collected. Multivariable logistic regression was used to identify variables associated with malignancy and construct the nomogram. Among the 1,419 nodules, 1,150 (81.0%) were malignant and 269 (19.0%) were benign. The final nomogram incorporated seven variables: suspicious radiologic features, nodule size, sex, symptoms at detection, consolidation-to-tumor ratio, minimum CT attenuation, and age. Age was retained in the final model on clinical grounds despite lacking statistical significance in multivariable analysis. Suspicious radiologic features (adjusted odds ratio aOR = 6.61, 95% confidence interval CI: 4.51–9.84; P 2 cm (aOR = 4.07, 95% CI: 2.16–7.62; P 0.50 (aOR = 0.20, 95% CI: 0.06–0.61; P = 0.005), and minimum CT attenuation per 100-HU increase (aOR = 0.82, 95% CI: 0.74–0.92; P < 0.001) were independently associated with malignancy. The nomogram showed good discrimination, with area under the receiver operating characteristic curve values of 0.809 in the training set and 0.782 in the validation set. Calibration analysis showed agreement between predicted and observed risks, and decision curve analysis supported usefulness. We developed and internally validated a clinical nomogram incorporating quantitative CT features for malignancy risk estimation in surgically resected pulmonary nodules ≤ 3 cm. The model showed good discrimination, calibration, and potential utility in a malignancy-enriched preoperative cohort. External validation in broader, less selected, screening-detected, incidental, and multicenter populations is warranted before routine clinical application.

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