Refining the risk stratification in advanced-stage classical Hodgkin lymphoma: A critical analysis of clinical prediction models
British Journal of Haematology, cilt.207, sa.5, ss.1744-1754, 2025 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Derleme
- Cilt numarası: 207 Sayı: 5
- Basım Tarihi: 2025
- Doi Numarası: 10.1111/bjh.70115
- Dergi Adı: British Journal of Haematology
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, BIOSIS, EMBASE
- Sayfa Sayıları: ss.1744-1754
- Anahtar Kelimeler: A-HIPI, Hodgkin lymphoma, IPS, prediction model, prognosis
- İstanbul Üniversitesi-Cerrahpaşa Adresli: Evet
Özet
Classical Hodgkin lymphoma (cHL) is a haematological malignancy with high curability; however, prognosis varies significantly based on clinical and biological factors. To enhance risk stratification, several clinical prediction models have been developed over time, particularly for advanced-stage cHL. The International Prognostic Score (IPS), introduced in 1998, was the first widely adopted model, later refined in 2012 (updated IPS) and further simplified in 2015 (IPS-3). Despite their prognostic utility, these models have demonstrated declining predictive performance due to advancements in cHL treatment. In response, the HoLISTIC consortium recently introduced the Advanced-Stage Hodgkin Lymphoma International Prognostic Index (A-HIPI) in 2023. Unlike previous models, A-HIPI incorporates continuous variables, aiming to provide a more precise risk assessment. However, its applicability to older patients remains uncertain, necessitating further validation studies. Additionally, none of the existing models incorporate dynamic treatment response markers such as interim positron emission tomography/computed tomography (PET/CT), which have shown strong prognostic value. This review comprehensively discusses the evolution, strengths and limitations of these prediction models, their clinical implications and the necessity for future refinements integrating dynamic biomarkers and treatment response indicators. The integration of machine learning and multi-omics approaches could further enhance risk stratification, improve treatment personalization and optimize patient outcomes in cHL.