Role of surgery in a novel multimodal therapeutic approach to complete cure of advanced lung cancer: current and future perspectives

Predicting Microwave Ablation Early Efficacy in Pulmonary Malignancies via Δ Radiomics Models

OBJECTIVE

This study aimed to explore the value of preoperative and postoperative computed tomography (CT)-based radiomic signatures and Δ radiomic signatures for evaluating the early efficacy of microwave ablation (MWA) for pulmonary malignancies.

METHODS

In total, 115 patients with pulmonary malignancies who underwent MWA treatment were categorized into response and nonresponse groups according to relevant guidelines and consensus. Quantitative image features of the largest pulmonary malignancies were extracted from CT noncontrast scan images preoperatively (time point 0, TP0) and immediately postoperatively (time point 1, TP1). Critical features were selected from TP0 and TP1 and as Δ radiomics signatures for building radiomics models. In addition, a combined radiomics model (C-RO) was developed by integrating radiomics parameters with clinical risk factors. Prediction performance was assessed using the area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA).

RESULTS

The radiomics model using Δ features outperformed the radiomics model using TP0 and TP1 features, with training and validation AUCs of 0.892, 0.808, and 0.787, and 0.705, 0.825, and 0.778, respectively. By combining the TP0, TP1, and Δ features, the logistic regression model exhibited the best performance, with training and validation AUCs of 0.945 and 0.744, respectively. The DCA confirmed the clinical utility of the Δ radiomics model.

CONCLUSIONS

A combined prediction model, including TP0, TP1, and Δ radiometric features, can be used to evaluate the early efficacy of MWA in pulmonary malignancies.

Clinicopathological and prognostic significance of SMAD4 in non-small cell lung cancer: A meta-analysis and database validation

BACKGROUND

The relationship between SMAD family member 4 (SMAD4) and the clinicopathological and prognostic significance of non-small cell lung cancer (NSCLC) patients is unclear. Our aim was to investigate the association between SMAD4 expression and clinicopathological parameters and NSCLC prognosis.

METHODS

We searched articles in databases from inception to July 2022 to retrieve literature related to SMAD4 expression and the clinicopathological and/or prognostic significance of NSCLC patients. Odds ratios (ORs), hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated. We evaluated the expression of SMAD4 and overall survival (OS) in NSCLC using the Kaplan-Meier plotter database.

RESULTS

Eight articles with 1461 NSCLC patients were included. SMAD4 expression was related to tumor differentiation (OR = 0.359, 95% CI: 0.238-0.543, P = .000), lymph node metastasis (OR = 0.469, 95% CI: 0.04-0.725, P = .001), tumor node metastasis stage (OR = 0.238, 95% CI: 0.156-0.362, P = .000) and good OS (HR = 0.592, 95% CI: 0.332-0.853, P = .000) in NSCLC. There was no significant association between SMAD4 expression and age (OR = 0.822, 95% CI: 0.515-1.312, P = .411) or sex (OR = 1.056, 95% CI: 0.675-1.653, P = .811). Furthermore, SMAD4 expression was lower in NSCLC, and a good prognosis in NSCLC (HR = 0.6, 95% CI = 0.51-0.72, P = 4.2 e-9) was shown to correlate with higher SMAD4 expression using the Kaplan-Meier Plotter database.

CONCLUSION

SMAD4 expression is lower in NSCLC and correlated with lymph node metastasis, tumor differentiation, tumor node metastasis stage and good OS for NSCLC patients.