This multicenter prospective diagnostic accuracy study will compare the performance of three artificial intelligence (AI) models (MVCS, LungDoc, and a United Imaging AI model) for predicting the malignancy risk of pulmonary nodules on chest CT. All enrolled patients will have pulmonary nodules ≤3 cm on CT and a definitive postoperative or biopsy pathological diagnosis. The AI models will generate continuous malignancy probability scores based only on CT images. Pathology will serve as the gold standard.
The primary objective is to compare the area under the receiver operating characteristic curve (AUC) for malignancy prediction among the three AI models. Secondary objectives include comparison of sensitivity, specificity, positive and negative predictive values, accuracy, F1 score, and calibration. Exploratory analyses will evaluate the MVCS model for predicting pathological invasion degree (pre-invasive, minimally invasive, and invasive adenocarcinoma) and an extended MVCSN model that incorporates clinical and imaging features in a data-complete subset.
Age ≥ 18 years, any sex.
At least one pulmonary nodule detected on chest CT, with initial nodule diameter ≤ 3 cm.
The nodule undergoes surgical resection or biopsy with a definitive benign or malignant pathological diagnosis.
Time interval between CT examination and pathological examination ≤ 6 months.
Pathological results are unclear, inconclusive, or disputed; nodule nature or grade cannot be reliably determined.
The patient receives treatments between CT and pathology that may significantly alter nodule appearance (e.g., chemotherapy, radiotherapy, targeted therapy).
CT imaging data are incomplete (missing essential series) or have severe motion, metal, or other artifacts preventing accurate AI analysis.
Required metadata for any AI model are missing and cannot be imputed. History of other malignant tumors (malignancies other than the index non-small cell lung cancer).