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<p>To evaluate the diagnostic performance, methodological quality, and clinical feasibility of ¹⁸F-FDG PET/CT-based radiomics machine learning models for predicting PD-L1 expression in non-small cell lung cancer (NSCLC). Systematic searches of PubMed, Scopus, Web of Science, Embase, and IEEE Xplore were conducted up to July 2025. Eligible studies developed radiomics-only models from ¹⁸F-FDG PET/CT for pre-biopsy or pre-operative PD-L1 prediction, with immunohistochemistry (IHC) as the reference standard (tumor proportion score ≥ 1%). Study quality was assessed using QUADAS-2 and METRICS. Pooled area under the curve (AUC), sensitivity, and specificity, with 95% confidence intervals (CI), were measured via a bivariate random-effects model. Eleven studies met the inclusion criteria; eight were included in the meta-analysis (<i>n</i> = 1,053). The pooled AUC was 0.83 (95% CI: 0.79–0.86), sensitivity 0.75 (95% CI: 0.64–0.84), and specificity 0.77 (95% CI: 0.64–0.87). Subgroup analyses revealed higher accuracy with semi-automatic segmentation, larger training cohorts, and biopsy-only specimens. QUADAS-2 identified high bias risk in the index test domain, mainly owing to the absence of segmentation validation and unclear blinding. METRICS scores averaged 58.04% (range: 41–64.7%), indicating moderate methodological quality. ¹⁸F-FDG PET/CT-based radiomics models show promise for non-invasive PD-L1 prediction in NSCLC, but their clinical translation is limited by methodological heterogeneity, absence of multi-center design, lack of external validation, and variable segmentation practices. Future work should focus on multi-center datasets, standardized workflows, and rigorous validation to enable reliable real-world applications.</p>

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Diagnostic accuracy of ¹⁸F-FDG PET/CT radiomics for non-invasive prediction of PD-L1 expression in non-small cell lung cancer: A systematic review and meta-analysis

  • 期刊论文

  • 发表日期：

    2025年12月29日

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  • Mohsen Salimi,
  • Pouria Vadipour,
  • Adnan Khosravi,
  • Babak Salimi,
  • Maryam Mabani,
  • Parsa Rostami,
  • Sharareh Seifi

摘要

To evaluate the diagnostic performance, methodological quality, and clinical feasibility of ¹⁸F-FDG PET/CT-based radiomics machine learning models for predicting PD-L1 expression in non-small cell lung cancer (NSCLC). Systematic searches of PubMed, Scopus, Web of Science, Embase, and IEEE Xplore were conducted up to July 2025. Eligible studies developed radiomics-only models from ¹⁸F-FDG PET/CT for pre-biopsy or pre-operative PD-L1 prediction, with immunohistochemistry (IHC) as the reference standard (tumor proportion score ≥ 1%). Study quality was assessed using QUADAS-2 and METRICS. Pooled area under the curve (AUC), sensitivity, and specificity, with 95% confidence intervals (CI), were measured via a bivariate random-effects model. Eleven studies met the inclusion criteria; eight were included in the meta-analysis (n = 1,053). The pooled AUC was 0.83 (95% CI: 0.79–0.86), sensitivity 0.75 (95% CI: 0.64–0.84), and specificity 0.77 (95% CI: 0.64–0.87). Subgroup analyses revealed higher accuracy with semi-automatic segmentation, larger training cohorts, and biopsy-only specimens. QUADAS-2 identified high bias risk in the index test domain, mainly owing to the absence of segmentation validation and unclear blinding. METRICS scores averaged 58.04% (range: 41–64.7%), indicating moderate methodological quality. ¹⁸F-FDG PET/CT-based radiomics models show promise for non-invasive PD-L1 prediction in NSCLC, but their clinical translation is limited by methodological heterogeneity, absence of multi-center design, lack of external validation, and variable segmentation practices. Future work should focus on multi-center datasets, standardized workflows, and rigorous validation to enable reliable real-world applications.