Studies in health technology and informatics · 2026
Building an RWD Oncology Data Mart for Diffuse Large B-Cell Lymphoma: From Data Integration to Clinical Insight.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
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История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Antenucci Laura, Pompei Edoardo, Masciocchi Carlotta, Patarnello Stefano, Maiolo Elena, Campana Fabrizia, Hohaus Stefan
Аннотация
INTRODUCTION: Diffuse Large B-Cell Lymphoma (DLBCL) is a heterogeneous and aggressive non-Hodgkin lymphoma, marked by wide variability in clinical presentation, biological profile, and disease course. Real World Data provide a unique opportunity to capture this complexity, offering insights into patient characteristics, disease progression, and outcomes in routine practice.
METHODS: This study describes the development of a DLBCL-specific Data Mart that integrates heterogeneous demographics, laboratory, histopathological, therapeutic and progression data into a standardized framework. The system supports automated identification of treatment lines and reconstruction of longitudinal patient histories, incorporating chemotherapy, radiotherapy, relapse and mortality data. An interactive dashboard displays individual disease trajectories over time.
RESULTS: The 1,235 patients Data Mart facilitates systematic analysis of therapies, patient responses, and disease progression, providing a reproducible, scalable platform for Real World Evidence generation.
CONCLUSION: The DLBCL Data Mart demonstrates the potential of Real World Data to support research, inform clinical decision-making, and serve as a model for other oncology domains.
METHODS: This study describes the development of a DLBCL-specific Data Mart that integrates heterogeneous demographics, laboratory, histopathological, therapeutic and progression data into a standardized framework. The system supports automated identification of treatment lines and reconstruction of longitudinal patient histories, incorporating chemotherapy, radiotherapy, relapse and mortality data. An interactive dashboard displays individual disease trajectories over time.
RESULTS: The 1,235 patients Data Mart facilitates systematic analysis of therapies, patient responses, and disease progression, providing a reproducible, scalable platform for Real World Evidence generation.
CONCLUSION: The DLBCL Data Mart demonstrates the potential of Real World Data to support research, inform clinical decision-making, and serve as a model for other oncology domains.