What does this research mean for the field?

An artificial intelligence model trained on H&E-stained slides to identify MYC rearrangements can independently predict overall and progression-free survival in large B-cell lymphomas by detecting morphological 'MYCness'. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

Determine whether a deep learning model trained on standard hematoxylin and eosin whole-slide images can detect MYC rearrangements and stratify high-risk diffuse large B-cell lymphoma cases.

September 2, 2026HemaSphereOpen Access

AI for prognostic assessment of diffuse large B‐cell lymphoma using H&E whole‐slide images

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Authors

JLJonas Lippl SRSarah Reinke SSStefan Schrod

Discussion

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Overview

Cohort study reveals AI-based histologic assessment predicts survival in diffuse large B-cell lymphoma, indicating automated pathology improves risk stratification.

Key Points

Determine whether a deep learning model trained on standard hematoxylin and eosin whole-slide images can detect MYC rearrangements and stratify high-risk diffuse large B-cell lymphoma cases.
Trained a multiple-instance deep learning model with a tumor tissue classifier using 1,035 digitized hematoxylin and eosin slides annotated for MYC, BCL2, and BCL6 translocations from an overall cohort of 2,018 cases.
Evaluated model predictions against overall survival and progression-free survival in an external test cohort (N = 499) and a clinical validation cohort without translocation labels (N = 484).
AI model scores demonstrated significant associations with progression-free survival and overall survival across both the external test cohort (N = 499) and the unlabelled clinical cohort (N = 484).
Model outputs correlated with MYC and BCL2 gene expression, high-grade signatures, and tumor microenvironment patterns, maintaining prognostic significance on multivariate analysis after adjusting for International Prognostic Index factors.

Cite This Study

Lippl et al. (2026) studied this question.

synapsesocial.com/papers/6a97e2eac562ede874ec75d1 https://doi.org/10.1002/hem3.70463

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September 2, 2026HemaSphereOpen Access

AI for prognostic assessment of diffuse large B‐cell lymphoma using H&E whole‐slide images

View Full Paper

Ask AI

Bookmark

Authors

JLJonas Lippl SRSarah Reinke SSStefan Schrod

Discussion

Member takes

Overview

Cohort study reveals AI-based histologic assessment predicts survival in diffuse large B-cell lymphoma, indicating automated pathology improves risk stratification.

Key Points

Determine whether a deep learning model trained on standard hematoxylin and eosin whole-slide images can detect MYC rearrangements and stratify high-risk diffuse large B-cell lymphoma cases.
Trained a multiple-instance deep learning model with a tumor tissue classifier using 1,035 digitized hematoxylin and eosin slides annotated for MYC, BCL2, and BCL6 translocations from an overall cohort of 2,018 cases.
Evaluated model predictions against overall survival and progression-free survival in an external test cohort (N = 499) and a clinical validation cohort without translocation labels (N = 484).
AI model scores demonstrated significant associations with progression-free survival and overall survival across both the external test cohort (N = 499) and the unlabelled clinical cohort (N = 484).
Model outputs correlated with MYC and BCL2 gene expression, high-grade signatures, and tumor microenvironment patterns, maintaining prognostic significance on multivariate analysis after adjusting for International Prognostic Index factors.

Cite This Study

Lippl et al. (2026) studied this question.

synapsesocial.com/papers/6a97e2eac562ede874ec75d1 https://doi.org/10.1002/hem3.70463

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