Preoperative CT semantic features predict lymph node metastasis of part-solid nodules in non-small cell lung cancer: a multicenter retrospective study

Abstract Background Lymph node metastasis (LNM) is the most common way of metastasis of lung cancer, and it is an independent risk factor for long-term survival and recurrence of non-small cell lung cancer (NSCLC) patients. The purpose of this study was to explore the value of preoperative computed tomography (CT) semantic features in differential diagnosis of LNM in part-solid nodules of NSCLC. Methods A total of 955 NSCLC patients confirmed by postoperative pathology were retrospectively enrolled from January 2019 to March 2023. The clinical, pathological data and preoperative CT images of these patients were investigated and statistically analyzed in order to explore the risk factors of LNM. Multivariate logistic regression was used to select independent risk factors and establish different prediction models. 10-fold cross-validation was used for model training and validation. The area under the curve (AUC) of receiver operating characteristic curve (ROC) was calculated and the Delong test was performed to compare the predictive performance between models. Results LNM occurred in 68 of 955 patients. After univariate analysis and adjustment for confounding factors, smoking history, pulmonary disease, solid component proportion, pleural contact type, and mean diameter were screened as independent risk factors for differential LNM. The image predictors model established by four independent factors of CT semantic features except smoking history showed a good diagnostic efficiency for LNM. Its AUC in the validation group was 0.857, and the sensitivity, specificity and accuracy of the model were all 77.6%. Conclusions Preoperative CT semantic features have good diagnostic value for LNM of NSCLC. The image predictors model based on pulmonary disease, solid component proportion, pleural contact type and mean diameter has excellent diagnostic efficacy, and can provide non-invasive evaluation for clinical practice.