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Updated: Jan 17, 2026

Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
International Prognostic Models as Tools for Selection of Higher-risk Trial-eligible Patients With Diffuse Large
Jelena Jelicic1, Karen Juul-Jensen1, Zoran Bukumiric2
1Department of Hematology, Odense University Hospital, Odense, Denmark.
Background:
The International prognostic index (IPI) is a widely used model for identifying trial-eligible patients with diffuse large B-cell lymphoma (DLBCL). However, the applicability of prognostic models in identifying trial-eligible high-intermediate (HI) and high-risk (H), particularly younger patients, has not been extensively studied.
Methods:
Patients with newly diagnosed DLBCL were identified in the Danish Lymphoma Registry (LYFO). To evaluate the impact of IPI and age-adjusted IPI (aaIPI) on identifying higher-risk (HI and H-risk) trial-eligible patients, we retrieved the eligibility criteria for the frontMIND trial (NCT04824092).
Results:
Of 6252 patients with DLBCL registered in the LYFO, 3725 (59.6%) were trial-eligible. The dataset included all IPI/aaIPI groups. However, 46% of 3725 patients would meet trial eligibility if IPI and aaIPI higher-risk patients were selected. The 5-year progression-free (PFS) and overall survival (OS) were 61.7% and 70.5%, respectively. Among patients aged ≤ 60 years (35.5%; 1321/3725), 29.5% were frontMIND-eligible based on aaIPI, with 5-year PFS and OS of 72.3% and 82.8%, respectively. Combining IPI and aaIPI did not improve the identification of patients who did not respond to standard treatment, and utilizing this strategy for trial selection was not superior to using IPI or NCCN-IPI alone.
Conclusions:
Prognostic models can help in selecting trial-eligible HI and H-risk patients, thereby increasing the chances of identifying those who do not respond to standard treatment. However, the currently used prognostic indices fail to accurately recognize some high-risk patients, particularly young patients. Therefore, additional risk factors beyond prognostic models are needed to improve patient selection for trial participation.

