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Chuanbao Zhang

Publications and source records attributed to Chuanbao Zhang.

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Standardization Challenges in Glycated Albumin Measurement: Methodological Heterogeneity and Quantification Discrepancies.

BACKGROUND: Glycated albumin (GA) is a valuable biomarker for monitoring glycemic status. However, measurement standardization is challenged by methodological heterogeneity, where different analytical principles and target measurands cause quantification discrepancies. This study systematically compared prevailing methodologies to identify a robust reference measurement procedure for widespread standardization. METHODS: We compared a targeted bottom-up proteomics method (ID-LC-MS/MS) with an enzymatic assay and the Japan Society of Clinical Chemistry (JSCC) reference method. A cohort of 129 donor serum specimens and certified reference materials (JCCRM-611) were analyzed to assess methodological comparability. Furthermore, GA concentration-dependent glycation kinetics at the Lys-525 site of albumin was examined. RESULTS: The optimized targeted bottom-up proteomics method showed a strong correlation (r = 0.986) with both the enzymatic assay and JSCC reference method. However, a progressively increasing negative systematic bias was observed at higher GA levels, confirming that Lys-525 underestimates GA at higher levels. In addition, with increasing overall GA concentration, the glycation ratio at the Lys-525 site consistently declined compared to the total glycated lysine residues. CONCLUSIONS: The standardization of GA measurements requires a precise, universally accepted definition to address analytical discrepancies. The present results indicate that quantification targeting all glycated lysine residues (as in the JSCC method) aligns more closely with biologically relevant GA values than site-specific measurement at Lys-525, which shows greater bias at higher concentrations. Therefore, further GA standardization would focus on adopting total glycated lysine residues on albumin as the preferred measurand definition, to improve detection accuracy and clinical comparability.

Serum Albumin

Leveraging Interradiomic Feature Relationships for Enhanced Prediction of Distant Metastasis and Characterization of Heterogeneity in Head and Neck Cancer.

PURPOSE: Distant metastasis remains a major cause of treatment failure in head and neck (HN) cancer, highlighting the need for more accurate early risk stratification. This study developed and validated a deep radiomics framework to characterize tumor heterogeneity from pretreatment computed tomography (CT) images and improve prediction of distant metastasis-free survival (DMFS). METHODS AND MATERIALS: This multicenter study included 3421 patients with HN cancer from 4 cohorts across 12 institutions. Radiomics features were extracted from primary tumors and transformed into OmicsMaps, a structured representation that spatially organizes interfeature relationships to facilitate learning of complex prognostic patterns. A convolutional neural network was trained to derive prognostic signatures, which were integrated with key clinical variables to construct an OmicsMap-clinical fusion model for patient risk stratification. Model performance was assessed using the concordance index (C-index) and time-dependent area under the receiver operating characteristic curve (AUC) in the CT Images from Large Head and Neck Cohort (RADCURE), HEAD-NECK-RADIOMICS-HN1 (HN1), and Head-Neck-Positron Emission Tomography-Computed Tomography (HN-PET-CT) cohorts. Radiogenomic analyses using RNA-seq data were conducted in the Cancer Genome Atlas Head-Neck Squamous Cell Carcinoma (TCGA-HNSC) cohort to investigate biological characteristics associated with the imaging-defined risk groups. RESULTS: The OmicsMap achieved C-index values of 0.742, 0.768, and 0.671 in the RADCURE, HN1, and HN-PET-CT cohorts, outperforming the conventional radiomics approach by 5.40%-6.37%. Incorporating clinical variables further improved generalizability, yielding a C-index of 0.864 (HN1) and 0.730 (HN-PET-CT), with time-dependent AUC of 0.727-0.895. The fusion model consistently stratified patients into distinct high- and low-risk groups for both DMFS and overall survival across cohorts (P <.01). Radiogenomic analyses revealed enrichment of immune-related pathways in the low-risk group, whereas the high-risk group exhibited a more aggressive phenotype enriched for proliferation, hypoxia, and epithelial-mesenchymal transition pathways, along with a fibrosis-prone tumor microenvironment characterized by extracellular matrix remodeling. CONCLUSIONS: Modeling interradiomic feature relationships using the OmicsMap representation substantially improves CT-based prediction of DMFS and characterization of tumor heterogeneity in HN cancer, supporting precision risk stratification in clinical oncology.

Journal Article