非血液リキッドバイオプシー検体のメタアナリシス：がん検出における診断性能

Non-blood liquid biopsy specimens represent an emerging frontier in cancer diagnostics, offering anatomically relevant alternatives to traditional blood-based analyses. These specimens provide unique advantages including proximity to tumor sites and potentially higher concentrations of tumor-derived biomarkers.

To systematically evaluate the diagnostic performance of various non-blood liquid biopsy specimens across multiple cancer types through comprehensive meta-analysis.

A systematic literature search was conducted across PubMed databases for studies published between 2013 and 2024. Studies evaluating non-blood liquid biopsy for cancer detection were included. Data extraction focused on diagnostic accuracy metrics including sensitivity, specificity, positive and negative likelihood ratios, and diagnostic odds ratios. Random-effects meta-analysis was performed using hierarchical modeling to account for heterogeneity.

Twenty studies encompassing 13,486 participants were analyzed across nine specimen types. Overall pooled diagnostic performance demonstrated sensitivity of 76.2% (95% CI: 71.2%-81.2%) and specificity of 77.8% (95% CI: 72.8%-82.8%), with a diagnostic odds ratio of 11.2. Aqueous humor achieved the highest individual performance with 96% sensitivity and 100% specificity for retinoblastoma detection. Cerebrospinal fluid (CSF) demonstrated excellent performance across brain tumor types with 88.9% sensitivity and 76.1% specificity. Heterogeneity was observed across specimen types and cancer contexts. The significant variability observed among different specimen types, especially in saliva where the I² value for specificity is 78.9%.

Non-blood liquid biopsy specimens demonstrate clinically meaningful diagnostic performance across diverse cancer types, with specimen-specific advantages for anatomically relevant malignancies. The identification of optimal specimen-cancer pairings provides immediate clinical utility for precision cancer diagnostics. Future research should focus on standardization of collection protocols and larger validation studies.