Diagnostic performance of radiomics analysis for pulmonary cancer airway spread: a systematic review and meta-analysis.

Abstract

Purpose: Spread through air spaces (STAS) is a unique metastatic pattern of pulmonary cancer closely associated with patient prognosis. This study evaluates the application of radiomics in the diagnosis of pulmonary cancer STAS through meta-analysis and explores its clinical significance and potential limitations.

Methods: We systematically searched the PubMed, Embase, and Cochrane Central Register of Controlled Trials databases for relevant studies between inception and April 1, 2024. The main evaluation indicators included sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and area under the curve (AUC). A total of 18 studies, covering 6,642 lung cancer cases, were included in the systematic review.

Results: In the development cohort, the sensitivity of radiomics for diagnosing STAS was 0.80 [95% confidence interval (CI): 0.75-0.84; P < 0.001; I²: 72.8%], and the specificity was 0.79 (95% CI: 0.71-0.85; P < 0.001; I²: 93.4%). In the validation cohort, the sensitivity was 0.81 (95% CI: 0.75-0.86; P < 0.001; I²: 45.8%), and the specificity was 0.74 (95% CI: 0.68-0.80; P < 0.001; I²: 65.0%). The summary AUC for both cohorts was 0.85 (95% CI: 0.82-0.88). Deeks' funnel plot analysis showed no significant publication bias in either cohort (P values: 0.963 and 0.106, respectively).

Conclusion: Radiomics analysis demonstrates important clinical significance in the diagnosis of pulmonary cancer STAS, with promising sensitivity and specificity results in both development and validation cohorts.

Clinical significance: While radiomics analysis offers valuable diagnostic insights for STAS in pulmonary cancer, its limitations must be carefully considered. Future research should focus on addressing these limitations and further exploring the application prospects of radiomics in lung cancer diagnosis and treatment.