This study is expected to develop a comprehensive predictive model based on plasma mass spectrometry data and clinical information, significantly improving the accuracy of early cGVHD prediction. By conducting in-depth analysis of plasma samples from cGVHD patients using mass spectrometry (MS) technology and integrating clinical data, the goal is to identify a set of biomarkers that can accurately diagnose, predict disease progression, and assess prognosis. This approach aims to develop a clinically actionable tool for cGVHD diagnosis and prediction, enabling early detection and precise intervention. The model will provide clinicians with more precise diagnostic tools and treatment decision support, facilitating early detection and targeted treatment of cGVHD. Additionally, the findings of this study will offer new data to support further biological research on cGVHD mechanisms and provide a theoretical basis for future personalized medicine approaches.

This study is expected to develop a comprehensive predictive model based on plasma mass spectrometry data and clinical information, significantly improving the accuracy of early cGVHD prediction. By conducting in-depth analysis of plasma samples from cGVHD patients using mass spectrometry (MS) technology and integrating clinical data, the goal is to identify a set of biomarkers that can accurately diagnose, predict disease progression, and assess prognosis. This approach aims to develop a clinically actionable tool for cGVHD diagnosis and prediction, enabling early detection and precise intervention. The model will provide clinicians with more precise diagnostic tools and treatment decision support, facilitating early detection and targeted treatment of cGVHD. Additionally, the findings of this study will offer new data to support further biological research on cGVHD mechanisms and provide a theoretical basis for future personalized medicine approaches.

By analyzing plasma samples from cGVHD patients using mass spectrometry (MS) technology, the aim is to identify biomarkers associated with cGVHD. Additionally, the model will integrate clinical information (such as transplant type, HLA matching, aGVHD history, etc.) to develop a comprehensive predictive model that can accurately diagnose cGVHD and predict disease -severity. This model will help improve early-stage recognition of cGVHD, especially in cases where symptoms are non-specific or difficult to detect and provide clinical decision support.

Through mass spectrometry analysis, this study will explore the relationship between biomarkers in plasma samples and the treatment response, and severity of cGVHD patients. The goal is to identify biomarkers closely linked to cGVHD prognosis, providing more accurate risk assessment and long-term prognostic prediction tools for patients.