ArticleClinical and experimental medicine2025
Predicting the risk of ibrutinib in combination with R-ICE in patients with relapsed or refractory DLBCL using explainable machine learning algorithms.
Article in Clinical and experimental medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence for Risk Stratification in Diffuse Large B-Cell Lymphoma: A Systematic Review of Classification Models and Predictive Performances.Medical sciences (Basel, Switzerland) · 2025Pooled it
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
Relapsed or refractory diffuse large B-cell lymphoma (DLBCL) poses significant therapeutic challenges due to heterogeneous patient outcomes. This study aimed to evaluate the efficacy of the ibrutinib plus R-ICE regimen and to leverage explainable machine learning models (ML) for predicting treatment risks and outcomes. Retrospective data from 28 patients treated between March 2019 and July 2022 were analyzed. Machine learning models, including CoxBoost + StepCox, were developed and validated using bootstrap methods. Synthetic minority over-sampling combined with propensity score matching (SMOTE-PSM) addressed class imbalances. Prognostic performance was compared against the Cox proportional hazards model using decision curve and calibration analysis, as well as time-dependent ROC curves. The CoxBoost + StepCox model achieved an average C-index of 0.955 for overall survival (OS) and progression-free survival (PFS). Key prognostic indicators included elevated lactate dehydrogenase (LDH), initial treatment response, time to relapse > 12 months, and CD5 + expression. Calibration curves showed a C-index of 0.932 for OS and 0.972 for PFS in the training set. CD5 + was most predictive for OS and LDH for PFS. Machine learning models demonstrated high accuracy and clinical utility, indicating potential for data-driven treatment decisions in DLBCL.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.