A nomogram based on clinical and CT features to differentiate benign from malignant solid pulmonary nodules ≤ 2 cm: a two-center study
Year: 2026
Type: article
Abstract: Objective To develop a predictive nomogram incorporating computed tomography-derived imaging characteristics and clinical indicators to differentiate between malignant and benign solid pulmonary nodules ≤ 2 cm, aiding clinical judgment. Materials and methods A retrospective analysis was conducted with 1337 participants from two medical centers. The training set included patients with solid pulmonary nodules ≤ 2 cm who underwent surgery from January 2019 to January 2023, and the testing set included cases from February 2023 to June 2025 at Yunnan Cancer Hospital. An independent external validation set was formed using patients from the First Affiliated Hospital of Kunming Medical University from January 2020 to January 2024. For each participant, clinical parameters and computed tomography imaging findings were comprehensively documented. Logistic regression analyses identified predictors significantly associated with malignancy. Results Multivariate logistic regression identified six independent predictors of malignancy in solid pulmonary nodules ≤ 2 cm: lobulation, spiculation, carcinoembryonic antigen levels, maximum diameter, and mean computed tomography value as risk factors, and calcification as a protective factor. The nomogram showed strong discrimination, achieving area under the curve values of 0.869 (training), 0.823 (testing), and 0.902 (validation). Calibration curves and decision curve analyses further confirmed good agreement between predicted and observed outcomes, highlighting substantial clinical applicability. Conclusion This study successfully formulated a nomogram combining clinical factors with imaging features from computed tomography scans, exhibiting excellent accuracy in predicting malignancy for solid pulmonary nodules measuring ≤ 2 cm. This model holds significant potential to enhance clinical decision-making and patient management.
Source: BMC Medical Imaging
Language: English
Subfield: Pulmonary and Respiratory Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,090
OpenAlex ID: W7225618060
FWCI:
Cites:
Cited by:
Related to: