Construction of novel radiomics nomogram model based on preoperative CT to predict lymphovascular tumor embolus and recurrence-free survival in early T1-2a stage lung adenocarcinomas

TL;DR

This study creates a radiomics model using preoperative CT scans to predict lymphovascular tumor embolus and recurrence-free survival in early-stage lung adenocarcinoma patients.

Contribution

A novel radiomics nomogram model is developed to predict LTE and recurrence-free survival in early-stage lung adenocarcinoma using preoperative CT data.

Findings

A radiomics model integrating CT features and smoking status showed improved prediction of LTE compared to radiomics-only models.

The model demonstrated significant differences in recurrence-free survival between low- and high-score groups.

Calibration curves confirmed the model's accuracy in predicting LTE status.

Abstract

To construct a radiomics nomogram model predicting the status of lymphovascular tumor embolus (LTE) in patients with lung invasive adenocarcinoma (LAC).

This retrospective analysis enrolled 195 patients with pathologically-confirmed LAC, treated at Weifang People’s Hospital between January 2018 and April 2021, including 152 and 43 cases in the LTE and non-LTE groups, respectively. Regions of interest were manually delineated on preoperative CT images using 3D slicer. Subsequently, 850 radiomics features were extracted and subjected to feature reduction through least absolute shrinkage and selection operator regression. The effectiveness of the predictive model was evaluated using receiver operating characteristic curves, calibration, and decision curve analysis. The log-rank test was applied to data split into low-score and high-score groups to analyze early recurrence-free survival based on the optimal cutoff value established in the mixed model.

Five identified feature parameters were applied to establish a rad-score. Hybrid prediction model integrating smoking status and radiomics signature demonstrated better predictive efficacy than the radiomics models in the training cohort (area under the curve [AUC], 0.9210 vs. 0.8781) and validation cohort (AUC, 0.8807 vs. 0.8770), although without reaching statistical significance. The calibration curves of the nomogram illustrated the goodness-of-fit to predict LTE status in both cohorts. Kaplan-Meier survival curve analysis demonstrated a significant difference in recurrence-free survival rate between the low-score and high-score groups, as predicted based on the optimal cutoff value of the mixed model.

CT radiomics-based model, which could serve as a potential biomarker, demonstrated strong predictive value for LTE status in LAC.

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