Large Cleaved and Immunoblastic Lymphoma May Represent Two Distinct Clinicopathologic Entities Within the Group of Diffuse Large B-Cell Lymphomas

Paepe, Pascale De; Achten, Ruth; Verhoef, Gregor; Włodarska, Iwona; Stul, Michel; Vanhentenrijk, Vera; Praet, Marleen; Wolf‐Peeters, Chris De

doi:10.1200/jco.2005.15.503

Cited by 77 publications

(73 citation statements)

References 28 publications

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“…6 According to several studies, the patients with immunohistochemically defined GC phenotype have a superior outcome in response to standard CHOP-like CT. 6,7,9 However, not all studies have been able to confirm the prognostic significance of the phenotypes. 18,19 Thus far, the data on the impact of cell of origin for survival in response to HDT and auto-SCT are also inconsistent. While van Imhoff et al 13 showed that primary high-risk patients with GC phenotype have a better outcome than the non-GC group, we and others 14 could not observe such a difference.…”

Section: Discussionmentioning

confidence: 99%

Impact of germinal center and non-germinal center phenotypes on overall and failure-free survival after high-dose chemotherapy and auto-SCT in primary diffuse large B-cell lymphoma

Nyman

Jantunen

Juvonen

et al. 2008

Bone Marrow Transplant

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Non-germinal center (non-GC) phenotype is an adverse prognostic factor in chemotherapy (CT)-treated diffuse large B-cell lymphoma (DLBCL) patients. To determine how high-dose therapy (HDT) supported with auto-SCT as first line therapy influences GC-associated outcome in young high-risk DLBCL patients GC and non-GC phenotypes were determined immunohistochemically from 63 patients. Of these, 29 primary high-risk DLBCL patients were treated with auto-SCT, whereas 34 CT-treated patients served as a control group. Consistent with previous studies, non-GC phenotype was associated with adverse outcome in CT-treated high-risk patients. In contrast, immunohistochemical classification by cell of origin did not associate with survival after auto-SCT. When the impact of treatment on the predictive value of cell of origin was analyzed, the non-GC patients, who received HDT, had a better failure-free survival (FFS) and overall survival (OS) than the patients treated with CT alone. In multivariate analyses, both age-adjusted International Prognostic Index (aaIPI) and treatment were independent prognostic factors for FFS and OS. For the patients with GC phenotype, the influence of auto-SCT on survival was not significant. The data imply that auto-SCT can overcome the adverse prognostic impact of the non-GC phenotype in patients with high-risk DLBCL and warrant additional prospective studies.

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Section: Discussionmentioning

confidence: 99%

Impact of germinal center and non-germinal center phenotypes on overall and failure-free survival after high-dose chemotherapy and auto-SCT in primary diffuse large B-cell lymphoma

Nyman

Jantunen

Juvonen

et al. 2008

Bone Marrow Transplant

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“…Of interest, immunoblastic morphology is a significant adverse predictor of DFS in univariate and multivariate analyses. 28 …”

Section: Immunoblastic Dlbclmentioning

confidence: 99%

“…No differences in terms of DFS and overall survival (OS) from the other DLBCL have been demonstrated. 28 Spindle cell DLBCL This is another entity recognized within the GCB-like subgroup. In contrast to the large cleaved DLBCL, these lymphomas are consistently CD10 and MUM1 negative but bcl-6 positive.…”

Section: Large Cleaved Dlbclmentioning

confidence: 99%

“…Although several studies demonstrated that immunohistochemistry performed on formalin-fixed and paraffin-embedded tissue allows reliable identification of these subgroups using only three markers (CD10, bcl-6 and MUM-1) 20,21 applying the algorithm proposed by Hans et al 20 (see Figure 1a), other studies failed to demonstrate the prognostic significance of subgroups identified by this algorithm. 26,28 Bcl-2 protein, an antiapoptotic protein, is expressed in up to 80% of DLBCL. Although there are no clear differences in bcl-2 expression in ABC-like and GCB-like DLBCL, the mechanisms of bcl-2 expression in both subgroups are distinct.…”

Section: Gcb-dlbcl Abc-dlbclmentioning

confidence: 99%

“…Several studies demonstrated that a high bcl-2 expression represents a bad prognostic factor 30,31 in the ABC-like 32 and GCB-like DLBCL. 28 Of interest, by adding the expression of bcl-2 to the GCB/ABC related proteins CD10 and MUM1 (see Figure 1b), a new algorithm to identify prognostic subgroups in DLBCL was proposed. 33 It was demonstrated to have a stronger prognostic power than the algorithm of Hans et al 20 Can we recognize ABC-like and GCB-like DLBCL by morphology?…”

Section: Gcb-dlbcl Abc-dlbclmentioning

confidence: 99%

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Diffuse large B-cell lymphoma: a heterogeneous group of non-Hodgkin lymphomas comprising several distinct clinicopathological entities

Paepe

Wolf-Peeters

2006

Leukemia

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Diffuse large B-cell lymphoma (DLBCL) as defined by the World Health Organization (WHO) classification is clinically, morphologically and genetically a heterogeneous group of malignant proliferations of large lymphoid B cells. Over the last 6 years, several studies have been published improving our understanding of these lymphomas. These studies analyzed DLBCL by their gene expression profile, provided further information on some of the variants of DLBCL listed in the WHO classification and stressed the impact of the site of origin of these tumors. This review summarizes these recent data and explores their impact on the recognition of new clinicopathological lymphoma entities.

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Prediction of immunochemotherapy response for diffuse large B‐cell lymphoma using artificial intelligence digital pathology

Lee,

Song,

Lee

et al. 2024

The Journal of Pathology CR

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Diffuse large B‐cell lymphoma (DLBCL) is a heterogeneous and prevalent subtype of aggressive non‐Hodgkin lymphoma that poses diagnostic and prognostic challenges, particularly in predicting drug responsiveness. In this study, we used digital pathology and deep learning to predict responses to immunochemotherapy in patients with DLBCL. We retrospectively collected 251 slide images from 216 DLBCL patients treated with rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R‐CHOP), with their immunochemotherapy response labels. The digital pathology images were processed using contrastive learning for feature extraction. A multi‐modal prediction model was developed by integrating clinical data and pathology image features. Knowledge distillation was employed to mitigate overfitting on gigapixel histopathology images to create a model that predicts responses based solely on pathology images. Based on the importance derived from the attention mechanism of the model, we extracted histological features that were considered key textures associated with drug responsiveness. The multi‐modal prediction model achieved an impressive area under the ROC curve of 0.856, demonstrating significant associations with clinical variables such as Ann Arbor stage, International Prognostic Index, and bulky disease. Survival analyses indicated their effectiveness in predicting relapse‐free survival. External validation using TCGA datasets supported the model's ability to predict survival differences. Additionally, pathology‐based predictions show promise as independent prognostic indicators. Histopathological analysis identified centroblastic and immunoblastic features to be associated with treatment response, aligning with previous morphological classifications and highlighting the objectivity and reproducibility of artificial intelligence‐based diagnosis. This study introduces a novel approach that combines digital pathology and clinical data to predict the response to immunochemotherapy in patients with DLBCL. This model shows great promise as a diagnostic and prognostic tool for clinical management of DLBCL. Further research and genomic data integration hold the potential to enhance its impact on clinical practice, ultimately improving patient outcomes.

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Large Cleaved and Immunoblastic Lymphoma May Represent Two Distinct Clinicopathologic Entities Within the Group of Diffuse Large B-Cell Lymphomas

Cited by 77 publications

References 28 publications

Impact of germinal center and non-germinal center phenotypes on overall and failure-free survival after high-dose chemotherapy and auto-SCT in primary diffuse large B-cell lymphoma

Impact of germinal center and non-germinal center phenotypes on overall and failure-free survival after high-dose chemotherapy and auto-SCT in primary diffuse large B-cell lymphoma

Diffuse large B-cell lymphoma: a heterogeneous group of non-Hodgkin lymphomas comprising several distinct clinicopathological entities

Prediction of immunochemotherapy response for diffuse large B‐cell lymphoma using artificial intelligence digital pathology

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