CT-Based Radiomics for Predicting PD-L1 Expression in Non-small Cell Lung Cancer: A Systematic Review and Meta-analysis

• Published in: Academic radiology
• Main Authors: Salimi, Mohsen, Vadipour, Pouria, Khosravi, Adnan, Salimi, Babak, Mabani, Maryam, Rostami, Parsa, Seifi, Sharareh
• Format: Journal Article
• Language: English
• Published: United States 29.05.2025
• Subjects: Programmed death ligand-1; Artificial intelligence; Lung cancer; Machine learning; Radiomics
• ISSN: 1878-4046
• DOI: 10.1016/j.acra.2025.05.024

The efficacy of immunotherapy in non-small cell lung cancer (NSCLC) is intricately associated with baseline PD-L1 expression rates. The standard method for measuring PD-L1 is immunohistochemistry, which is invasive and may not capture tumor heterogeneity. The primary aim of the current study is to assess whether CT-based radiomics models can accurately predict PD-L1 expression status in NSCLC and evaluate their quality and potential gaps in their design. Scopus, PubMed, Web of Science, Embase, and IEEE databases were systematically searched up until February 14, 2025, to retrieve relevant studies. Data from validation cohorts of models that classified patients by tumor proportion score (TPS) of 1% (TPS1) and 50% (TPS50) were extracted and analyzed separately. Quality assessment was performed through METRICS and QUADAS-2 tools. Diagnostic test accuracy meta-analysis was conducted using a bivariate random-effects approach to pool values of performance metrics. The qualitative synthesis included twenty-two studies, and the meta-analysis analyzed 11 studies with 997 individual subjects. The pooled AUC, sensitivity, and specificity of TPS1 models were 0.85, 0.76, and 0.79, respectively. The pooled AUC, sensitivity, and specificity of TPS50 models were 0.88, 0.72, and 0.86, accordingly. The QUADAS-2 tool identified a substantial risk of bias regarding the flow and timing and index test domains. Certain methodological limitations were highlighted by the METRICS score, which averaged 58.1% and ranged from 24% to 83.4%. CT-based radiomics demonstrates strong potential as a non-invasive method for predicting PD-L1 expression in NSCLC. While promising, significant methodological gaps must be addressed to achieve the generalizability and reliability required for clinical application.