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503
II-SHO’BA. ONKOLOGIK
KASALLIKLARNING DIFFERENSIAL
DIAGNOSTIKASIDA
QO’LLANILAYOTGAN ZAMONAVIY
TEXNOLOGIYALAR
MODERN TECHNOLOGIES FOR DIFFERENTIAL DIAGNOSIS OF
LARGE B-CELL LYMPHOMAS
Burkhonova Mukhlisa Olim
Kizi
Student of Tashkent Medical
Academy 4-course student
Inogamova Munira
Bakhodirovna
Oncologist,
Hematologist
Scientific supervisor
Annotation
: Large B-cell lymphomas (LBCLs) are a heterogeneous group of
non-Hodgkin lymphomas that originate from B-lymphocytes and exhibit diverse
clinical, morphological, and molecular characteristics. Accurate differential
diagnosis of LBCL subtypes often exceeds the capabilities of conventional
histopathological methods alone. Therefore, modern diagnostic technologies —
including immunohistochemistry, flow cytometry, molecular and genetic profiling,
advanced imaging techniques (such as PET/CT), and artificial intelligence-based
algorithms are increasingly being utilized in clinical practice. This article reviews
the role, applications, and advantages of these contemporary approaches in
improving the precision of differential diagnosis in large B-cell lymphomas,
supported by the latest scientific literature.
Key
words:
Diffuse
large
B-cell
lymphoma
(DLBCL);
Immunohistochemistry (IHC); Molecular profiling; Flow cytometry; PET/CT
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imaging; Artificial intelligence; Differential diagnosis; WHO classification;
Biomarkers; Precision oncology.
Introduction.
Large B-cell lymphomas (LBCLs) are a group of malignant
lymphoproliferative disorders originating from B-lymphocytes, with diffuse large
B-cell lymphoma (DLBCL) being the most common subtype. These lymphomas
often present with aggressive clinical behavior, requiring prompt diagnosis and
timely
therapeutic
intervention.
However,
the
morphological
and
immunophenotypic similarities among various LBCL subtypes frequently pose
diagnostic challenges.
In recent years, the rapid advancement of medical technologies has
significantly enhanced the diagnostic capabilities in hematopathology. Beyond
conventional
histopathological
methods,
modern
techniques
such
as
immunohistochemistry, flow cytometry, molecular and genetic profiling, PET/CT
imaging, and artificial intelligence-based algorithms have emerged as pivotal tools
in achieving precise differential diagnosis [1].
This article aims to explore the significance of modern diagnostic
technologies in differentiating various subtypes of LBCL, highlighting their
practical applications, diagnostic value, and potential in improving patient
outcomes.
Objective:
To evaluate and analyze the role of advanced diagnostic tools in
the differential diagnosis of large B-cell lymphomas.
Overview of Large B-Cell Lymphomas.
Large B-cell lymphomas (LBCLs)
represent a heterogeneous group of mature B-cell neoplasms characterized by the
proliferation of large atypical lymphoid cells. Among these, Diffuse Large B-Cell
Lymphoma (DLBCL) accounts for approximately 30–40% of all non-Hodgkin
lymphomas (NHL) worldwide, making it the most prevalent and clinically
significant subtype [3].
According to the 2022 WHO Classification of Haematolymphoid Tumours,
LBCLs encompass multiple entities, including:
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DLBCL, not otherwise specified (NOS)
High-grade B-cell lymphomas (HGBCLs) with MYC and BCL2 and/or
BCL6 rearrangements (also known as double/triple-hit lymphomas)
Primary mediastinal large B-cell lymphoma (PMBCL)
T-cell/histiocyte-rich large B-cell lymphoma
Intravascular large B-cell lymphoma
EBV-positive DLBCL and others
Each subtype exhibits distinct clinical, morphological, immunophenotypic,
and genetic features, which are crucial for accurate classification and treatment
planning.
DLBCL, for instance, can be further subdivided based on gene expression
profiling (GEP) into:
Germinal Center B-cell–like (GCB)
Activated B-cell–like (ABC)
Unclassified subtypes
These molecular subgroups differ not only in pathogenesis but also in
response to therapy and prognosis, emphasizing the need for precise
subclassification during diagnostic workup [4].
Moreover, with the integration of next-generation sequencing (NGS) and
digital pathology, it is now possible to identify recurrent mutations (e.g., MYD88,
BCL2, TP53), chromosomal translocations, and epigenetic alterations that further
refine diagnosis and guide personalized therapy.
Modern Diagnostic Technologies in the Differential Diagnosis of LBCLs.
The complexity and heterogeneity of large B-cell lymphomas (LBCLs)
necessitate the use of advanced diagnostic technologies that go beyond traditional
histology. These modern tools not only improve diagnostic precision but also aid
in molecular subtyping, prognostication, and personalized treatment planning. The
key technologies currently used in clinical and research settings include:
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1. Immunohistochemistry (IHC).
IHC remains a cornerstone in the initial
evaluation of LBCLs. It is essential for identifying B-cell lineage (e.g., CD20,
CD79a, PAX5), proliferation index (Ki-67), and for subclassification using
algorithms such as Hans classifier, which differentiates between GCB and non-
GCB (ABC) subtypes. [3].
2. Flow Cytometry.
This technique enables rapid and quantitative analysis
of surface and intracellular markers in fresh or frozen tissue samples. It is
particularly useful in differentiating LBCLs from other lymphoproliferative
disorders like Burkitt lymphoma or follicular lymphoma. [5].
3. Molecular and Genetic Profiling.
Techniques such as fluorescence in situ
hybridization (FISH), PCR, and next-generation sequencing (NGS) allow for the
detection of chromosomal rearrangements (e.g., MYC, BCL2, BCL6), gene
mutations (e.g., MYD88, EZH2), and clonality. Identification of “double-hit” or
“triple-hit” lymphomas using these methods significantly affects prognosis and
treatment strategy. [4].
4. PET/CT Imaging.
18F-FDG PET/CT is the imaging modality of choice
for staging, response assessment, and identifying extranodal involvement.
Dissemination features on PET, such as total metabolic tumor volume (TMTV),
are now recognized as strong predictors of treatment outcome. [2].
5. Artificial Intelligence (AI) and Digital Pathology.
AI-driven algorithms
and digital image analysis are increasingly used to automate morphological
assessment, predict molecular subtypes from H&E slides, and integrate multi-
omics data. These technologies promise higher accuracy and reproducibility,
especially in resource-limited settings. [3].
Discussion.
The differential diagnosis of large B-cell lymphomas (LBCLs)
remains a clinical challenge due to their heterogeneity in morphological,
immunophenotypic, and genetic features. The integration of modern diagnostic
technologies has significantly improved the precision and depth of lymphoma
diagnostics, allowing clinicians to move beyond basic histological interpretation.
The use of immunohistochemistry (IHC) provides the foundation for initial
diagnosis and is indispensable in resource-limited settings. However, IHC alone is
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often insufficient to distinguish between LBCL subtypes with overlapping
features. Incorporation of molecular classification (e.g., GCB vs. ABC) has
significant prognostic implications and influences treatment decisions, such as the
use of targeted agents like BTK inhibitors in ABC subtypes. [3].
Molecular profiling, particularly next-generation sequencing (NGS), allows
for the identification of mutations such as MYD88, EZH2, and TP53, which have
diagnostic, prognostic, and therapeutic implications. The detection of double- or
triple-hit rearrangements using FISH or PCR is crucial for identifying high-grade
B-cell lymphomas that require more intensive chemotherapy regimens.
Advanced PET/CT imaging not only aids in initial staging but also plays a
pivotal role in response assessment. Recent research demonstrates that imaging
features like total metabolic tumor volume (TMTV) and dissemination scores can
independently predict patient outcomes, further guiding risk-adapted therapy. [2].
While AI and digital pathology are still emerging tools, their integration is
accelerating, particularly in academic centers. AI-based classification systems
have shown promising results in predicting molecular subtypes from histological
slides with high accuracy. These tools also hold potential in reducing inter-
observer variability and enhancing reproducibility of diagnoses.
Despite these advancements, several challenges remain. Molecular
diagnostics and AI technologies often require expensive infrastructure, trained
personnel, and standardized protocols — limiting their widespread use in low-
resource settings. Additionally, variability in interpretation, lack of universally
accepted diagnostic algorithms, and the complexity of integrating multimodal data
still pose barriers to routine clinical implementation.
Nevertheless, the continued evolution of diagnostic platforms and
collaborative research will likely overcome many of these limitations, paving the
way for more personalized and effective management of LBCLs.
Conclusion.
The diagnostic landscape of large B-cell lymphomas (LBCLs)
has transformed remarkably with the advent of modern technologies. While
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traditional histopathology and immunohistochemistry remain essential, they are
now complemented by molecular profiling, advanced imaging, and artificial
intelligence tools that enable more accurate and individualized diagnoses. These
advancements have significantly improved our ability to differentiate between
LBCL subtypes, predict prognosis, and guide targeted therapeutic strategies.
The incorporation of next-generation sequencing, PET/CT imaging, and AI-
assisted digital pathology offers a comprehensive diagnostic approach that aligns
with the principles of precision medicine. However, successful implementation of
these technologies in routine clinical practice requires addressing challenges such
as accessibility, cost, and standardization.
Overall, embracing a multidisciplinary diagnostic framework that integrates
both conventional and cutting-edge tools is critical for optimizing patient
outcomes in LBCLs. Continued research, technological development, and global
collaboration will further enhance the accuracy and equity of lymphoma diagnosis
in the future.
REFERENCE
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https://www.verywellhealth.com/types-of-b-cell-lymphoma-4688476
(murojaat
qilingan sana: 13.04.2025).
2. Cottereau A-S, Nioche C, Dirand A-S, Clerc J, Morschhauser F,
Casasnovas O, Meignan M, Buvat I. 18F-FDG-PET dissemination features in
diffuse large B cell lymphoma are predictive of outcome. arxiv. 2020.
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