International Research Team Develops Personalized Prediction Model for Early-stage Hodgkin Lymphoma
2025-11-06 14:39
Source:Rutgers Cancer Institute
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A research team from RWJBarnabas Health, Rutgers Cancer Institute, in collaboration with Tufts Medical Center and the University of Manchester, has recently developed the first personalized risk prediction model for early-stage classic Hodgkin lymphoma. This research result, published in the journal NEJM Evidence, provides a new quantitative tool for clinical treatment decisions.

The model, named the Early-stage Hodgkin International Prognostic Index (E-HIPI), was established based on data from over 3,000 patients in four international phase III clinical trials and validated in more than 2,300 real-world cases. Andrew M. Evens, MD, chief scientist of the research team and from Rutgers Cancer Institute, stated: "This model utilizes routine clinical indicators to accurately predict patients' two-year progression-free survival."

The E-HIPI model integrates patient gender, tumor volume, and laboratory indicators such as hemoglobin, with predictive performance superior to the currently widely used EORTC classification system. Susan K. Parsons, MD, from Tufts Medical Center, noted: "This personalized assessment tool aids in shared decision-making between doctors and patients, optimizing treatment plan selection." The research team has simultaneously developed a free online calculator for practical clinical use.

The study was completed through the HoLISTIC International Collaborative Consortium, integrating multinational clinical trial and cancer registry data. Angie Mae Rodday, PhD, the lead statistician, emphasized: "We strictly followed prediction model development guidelines to ensure the reliability and practicality of the results." Model validation shows high consistency between its predictions and patients' actual prognoses.

This breakthrough research provides new pathways for improving long-term prognosis in lymphoma patients. The researchers stated that the next step will be to promote global clinical application validation of the model.

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