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Inflammatory and Nutritional Biomarker in Different Subtypes of Early Stage Diffuse Large B-Cell Lymphoma: Towards a Diagnostic Model
Inflammatory and Nutritional Biomarker in Different Subtypes of Early Stage Diffuse Large B-Cell Lymphoma: Towards a Diagnostic Model
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Inflammatory and Nutritional Biomarker in Different Subtypes of Early Stage Diffuse Large B-Cell Lymphoma: Towards a Diagnostic Model
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Inflammatory and Nutritional Biomarker in Different Subtypes of Early Stage Diffuse Large B-Cell Lymphoma: Towards a Diagnostic Model
Inflammatory and Nutritional Biomarker in Different Subtypes of Early Stage Diffuse Large B-Cell Lymphoma: Towards a Diagnostic Model

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Inflammatory and Nutritional Biomarker in Different Subtypes of Early Stage Diffuse Large B-Cell Lymphoma: Towards a Diagnostic Model
Inflammatory and Nutritional Biomarker in Different Subtypes of Early Stage Diffuse Large B-Cell Lymphoma: Towards a Diagnostic Model
Journal Article

Inflammatory and Nutritional Biomarker in Different Subtypes of Early Stage Diffuse Large B-Cell Lymphoma: Towards a Diagnostic Model

2025
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Overview
Diffuse Large B-Cell Lymphoma (DLBCL) is a heterogeneous malignancy with distinct Germinal Center B-Cell Like (GCB) and non-GCB subtypes. Accurate subtyping is crucial due to differences in prognosis and treatment response. While gene expression profiling is the gold standard, it is often unavailable in low-resource settings. Inflammatory and nutritional biomarkers such as Systemic Immune-Inflammation Index (SII), Prognostic Nutritional Index (PNI), and Advanced Lung Cancer Inflammation Index (ALI) offer a practical alternative. This study aims to evaluate their diagnostic potential in early-stage DLBCL subtypes. A cross-sectional analysis was conducted on 60 early stage DLBCL patients (30 GCB, 30 non-GCB) at Dr Hasan Sadikin General Hospital Bandung. Clinical characteristics, hematological parameters, and inflammation-based indices (SII, PNI, and ALI) were evaluated. Differences between subtypes were analyzed using the Mann-Whitney -test, and Receiver Operating Characteristic (ROC) analysis was used to determine diagnostic performance. The median SII was significantly higher in non-GCB compared with GCB (982,575; IQR: 609,112-2,239,917 vs 575,598; IQR: 454,578-886,426, p = 0.014). Conversely, PNI was higher in GCB compared to non-GCB group (49.18; IQR: 46.38-56.38 vs 45.96; IQR 40.05-52.28, p = 0.011). ALI values were also higher in the GCB than non-GCB (44.14; IQR: 27.69-67.18 vs 24.51; IQR: 14.34-42.47,p=0.003). ROC analysis revealed that ALI had the highest diagnostic accuracy (AUC = 0.724; 95% CI: 0.593-0.831), followed by SII (AUC = 0.685, 95% CI: 0.552-0.799) and PNI (AUC = 0.691 95% CI: 0.558-0.804). Optimal cut-off values were ≤1,234,133 for SII, >43.27 for PNI, and >27.41 for ALI. ALI demonstrated the best balance between sensitivity (76.7%) and specificity (63.3%), making it the most reliable marker for distinguishing DLBCL subtypes. SII, PNI, and ALI differ significantly between DLBCL subtypes. These findings suggest that integrating these indices into a diagnostic model could enhance risk stratification and guide therapeutic decision-making in DLBCL. Further studies with larger cohorts are warranted to validate these findings.