A comprehensive nomogram combining CT-based radiomics with clinical features for differentiation of benign and malignant lung subcentimeter solid nodules.

Objective: To establish a nomogram based on non-enhanced computed tomography(CT) imaging radiomics and clinical features for use in predicting the malignancy of sub-centimeter solid nodules (SCSNs). Materials and methods: Retrospective analysis was performed of records for 198 patients with SCSNs that were surgically resected and examined pathologically at two medical institutions between January 2020 and June 2021. Patients from Center 1 were included in the training cohort (n = 147), and patients from Center 2 were included in the external validation cohort (n = 52). Radiomic features were extracted from chest CT images. The least absolute shrinkage and selection operator (LASSO) regression model was used for radiomic feature extraction and computation of radiomic scores. Clinical features, subjective CT findings, and radiomic scores were used to build multiple predictive models. Model performance was examined by evaluating the area under the receiver operating characteristic curve (AUC). The best model was selected for efficacy evaluation in a validation cohort, and column line plots were created. Results: Pulmonary malignant nodules were significantly associated with vascular alterations in both the training (p < 0.001) and external validation (p < 0.001) cohorts. Eleven radiomic features were selected after a dimensionality reduction to calculate the radiomic scores. Based on these findings, three prediction models were constructed: subjective model (Model 1), radiomic score model (Model 2), and comprehensive model (Model 3), with AUCs of 0.672, 0.888, and 0.930, respectively. The optimal model with an AUC of 0.905 was applied to the validation cohort, and decision curve analysis indicated that the comprehensive model column line plot was clinically useful. Conclusion: Predictive models constructed based on CT-based radiomics with clinical features can help clinicians diagnose pulmonary nodules and guide clinical decision making.

Low Expression of ASK1-Interacting Protein-1 Is Significantly Correlated with Tumor Angiogenesis and Poor Survival in Patients with Early Stage Non-Small Cell Lung Cancer.

OBJECTIVE: To investigate the expression of tumor suppressor protein ASK1-interacting protein-1 (AIP1) in cancer tissues of patients with early-stage non-small cell lung cancer (NSCLC) and its correlation with tumor progression, tumor angiogenesis and prognosis. METHODS: A total of 136 patients with stage I NSCLC who underwent radical resection of lung cancer in Qianfoshan Hospital of Shandong Province from January 2011 to December 2011 were enrolled. Immunohistochemistry was used to detect AIP1 protein in tumor tissues. Vascular endothelial CD34 immunohistochemical staining was used to count intratumoral microvessel density (MVD). SPSS 19.0 software was used to analyze the relationship between AIP1 protein expression and clinicopathological features, tumor angiogenesis and prognosis. RESULTS: Low expression of AIP1 was more common in tumor tissues with high MVD, and patients with low expression of AIP1 were more likely to have tumor recurrence. Multivariate analysis showed that low expression of AIP1 had predictive value for overall survival, disease-free survival, and disease-specific survival. CONCLUSION: Downregulation of AIP1 protein expression is associated with lung cancer progression, tumor angiogenesis and poor prognosis. Consequently, AIP1 may prove to be an important predictor of recovery from lung cancer and could become a new therapeutic target for lung cancer treatment.