CT-based radiomics models for predicting spread through air space in lung cancer: A systematic review and meta-analysis

Abstract

Rationale and objectives

Numerous studies have developed and validated models to predict spread through air space (STAS) in lung cancer using preoperative computed tomography (CT), yielding inconsistent results. We aimed to estimate the diagnostic accuracy of CT-based radiomics for predicting spread through air space (STAS) for preoperative prediction of lung cancer.

Materials and methods

Original studies published prior to January 2024 were searched in various databases. Only studies that used CT-based radiomics to preoperatively predict STAS in lung cancer patients were included. Two researchers independently extracted data and assessed the methodological quality of the included studies. We estimated summary sensitivity (SEN), specificity (SPE), and the areas under the receiver operating characteristic curve (AUC) of CT-based radiomics for predicting STAS. A head-to-head comparison was performed to evaluate the efficacy of clinical and radiomics models.

Results

A total of 17 studies with 6254 participants were included, and the methodological quality was found to be moderate. The meta-analysis comprised 26 datasets and achieved a pooled SEN of 0.82 (95 % CI: 0.78, 0.86), SPE of 0.76 (95 % CI: 0.72, 0.80), and AUC of 0.86 (95 % CI: 0.83, 0.89). In 11 pairwise comparison datasets, the radiomics model outperformed the clinical model with a higher AUC of 0.86 (95 % CI: 0.83, 0.89) compared to 0.80 (95 % CI: 0.76, 0.85), p < 0.001.

Conclusions

Due to its moderate diagnostic accuracy, widespread use, and low cost, CT-based radiomics can be used to predict STAS in lung cancer preoperatively. However, further research is required in a large, multicentre, and prospective design.