Table Info

Table 1.

Comparison between ICC 2022, WHO-HAEM5, and WHO revised 4th edition

The ICC of Mature Lymphoid Neoplasms: a report from the Clinical Advisory Committee (2022)	WHO Classification, 5th edition (2022), mature B-cell neoplasms	WHO Classification, revised 4th edition
-	Pre-neoplastic and neoplastic small lymphocytic proliferations	-
Monoclonal B-cell lymphocytosis (CLL type/non-CLL type)	Monoclonal B-cell lymphocytosis	Monoclonal B-cell lymphocytosis
CLL/SLL	CLL/SLL	CLL/SLL
B-cell prolymphocytic leukemia	Entity deleted	B-cell prolymphocytic leukemia
Splenic B-cell lymphomas (BCLs) and leukemias	Splenic BCLs and leukemias	-
Hairy cell leukemia	Hairy cell leukemia	Hairy cell leukemia
Splenic MZL	Splenic MZL	Splenic MZL
Splenic diffuse red pulp small BCL	Splenic diffuse red pulp small BCL	Splenic diffuse red pulp small BCL
Hairy cell leukemia-variant	Splenic BCL/leukemia with prominent nucleoli	Not previously included (encompassing hairy cell leukemia variant and some cases of B-cell prolymphocytic leukemia)
LPL	LPL	LPL
LPL/Waldenstrom macroglobulinemia	LPL	LPL
MZL	MZL	MZL
Extranodal MZL of MALT (MALT lymphoma)	Extranodal MZL of MALT	Extranodal MZL of MALT
Primary cutaneous marginal zone lymphoproliferative disorder*	Primary cutaneous MZL	Not previously included (originally included under extranodal MZL of MALT)
Nodal MZL	Nodal MZL	Nodal MZL
Pediatric nodal MZL	Pediatric MZL	Pediatric MZL
FL	FL	FL
In situ follicular neoplasia	In situ follicular B-cell neoplasm	In situ follicular neoplasia
FL	FL	FL
Pediatric-type FL	Pediatric-type FL	Pediatric-type FL
Duodenal-type FL	Duodenal-type FL	Duodenal-type FL
Testicular FL*	-	-
BCL2 apoptosis regulator (BCL2)-Rearrangement-negative, CD23-positive follicle center lymphoma	-	-
Cutaneous follicle center lymphoma	Cutaneous follicle center lymphoma	-
Primary cutaneous follicle center lymphoma	Primary cutaneous follicle center lymphoma	Primary cutaneous follicle center lymphoma
MCL	MCL	MCL
In situ mantle cell neoplasia	In situ mantle cell neoplasm	In situ mantle cell neoplasia
MCL	MCL	MCL
Leukemic non-nodal MCL	Leukemic non-nodal MCL	Leukemic non-nodal MCL
-	Transformations of indolent BCLs	-
-	Transformations of indolent BCLs	Not previously included
LBCLs	LBCLs	LBCLs
DLBCL, NOS/germinal center B (GCB)-cell subtype/activated B-cell (ABC) subtype	DLBCL, NOS	DLBCL, NOS
T cell/histiocyte-rich LBCL	T-cell/histiocyte-rich LBCL	T-cell/histiocyte-rich LBCL
HBCL, with MYC and BCL2 rearrangements/HBCL with MYC* and BCL6 transcription repressor (BCL6) rearrangements*	DLBCL/HBCL with MYC and BCL2 rearrangements	HBCL with MYC and BCL2 and/or BCL6 rearrangements
Anaplastic lymphoma kinase (ALK)-positive LBCL	ALK-positive LBCL	ALK-positive LBCL
LBCL with interferon regulatory factor 4 (IRF4) rearrangement*	LBCL with IRF4 rearrangement	LBCL with IRF4 rearrangement
LBCL with 11q aberration*	HBCL with 11q aberrations	Burkitt-like lymphoma with 11q aberration
Nodular lymphocyte-predominant BCL	-	-
Lymphomatoid granulomatosis	Lymphomatoid granulomatosis	Lymphomatoid granulomatosis
Epstein-Barr virus–positive polymorphic B-cell lymphoproliferative disorder, NOS*	-	-
EBV-positive DLBCL, NOS	EBV-positive DLBCL	EBV-positive DLBCL, NOS
Pediatric nodal MZL	Pediatric MZL	Pediatric MZL
DLBCL associated with chronic inflammation	DLBCL associated with chronic inflammation	DLBCL associated with chronic inflammation
Fibrin-associated DLBCL	Fibrin-associated LBCL	Not previously included (previously considered a subtype of DLBCL associated with chronic inflammation)
HHV-8 and EBV-negative primary effusion-based lymphoma*	Fluid overload-associated LBCL	Not previously included
Plasmablastic lymphoma	Plasmablastic lymphoma	Plasmablastic lymphoma
Primary DLBCL of the central nervous system/primary DLBCL of the testis*	Primary LBCL of immune-privileged sites	Not previously included, encompassing primary DLBCL of the clinical nurse specialist (CNS) in revised 4th edition (plus primary LBCL of the vitreoretina and primary LBCL of the testis)
Primary cutaneous DLBCL, leg type	Primary cutaneous DLBCL, leg type	Primary cutaneous DLBCL, leg type
Intravascular LBCL	Intravascular LBCL	Intravascular LBCL
Primary mediastinal LBC	Primary mediastinal LBCL	Primary mediastinal LBCL
Mediastinal gray-zone lymphoma*	Mediastinal grey-zone lymphoma	BCL, unclassifiable, with features intermediate between DLBCL and classic HL
HBCL, NOS	HBCL, NOS	HBCL, NOS
Nodular lymphocyte predominant BCL*	-	-
BL	BL	-
BL	BL	BL
HHV-8–associated lymphoproliferative disorders	Kaposi’s sarcoma-associated herpesvirus (KSHV)/HHV8-associated B-cell lymphoid proliferations and lymphomas	-
Primary effusion lymphoma	Primary effusion lymphoma	Primary effusion lymphoma
HHV-8-positive DLBCL, NOS	KSHV/HHV8-positive DLBCL	HHV8-positive DLBCL, NOS
HHV-8-positive germinotropic lymphoproliferative disorder	KSHV/HHV8-positive germinotropic lymphoproliferative disorder	HHV8-positive germinotropic lymphoproliferative disorder
Pediatric nodal MZL	Pediatric MZL	Pediatric MZL
-	Lymphoid proliferations and lymphomas associated with immune deficiency and dysregulation	-
-	Hyperplasias arising in immune deficiency/dysregulation	Not previously included, encompassing non-destructive post-transplant lymphoproliferative disorders, among others
-	Polymorphic lymphoproliferative disorders arising in immune deficiency/dysregulation	Not previously included, encompassing polymorphic posttransplant lymphoproliferative disorders, other iatrogenic immunodeficiency-associated lymphoproliferative disorders, among others
EBV-positive mucocutaneous ulcer*	EBV-positive mucocutaneous ulcer	EBV-positive mucocutaneous ulcer
-	Lymphomas arising in immune deficiency/dysregulation	Not previously included, encompassing monomorphic posttransplant lymphoproliferative disorders, classic HL posttransplant lymphoproliferative disorders, lymphomas associated with human immunodeficiency virus (HIV) infection, among others
-	Inborn error of immunity-associated lymphoid proliferations and lymphomas	Lymphoproliferative diseases associated with primary immune disorders
-	Plasma cell neoplasms and other diseases with paraproteins	-
-	Monoclonal gammopathies	-
Primary cold agglutinin disease*	Cold agglutinin disease	Not previously included
Immunoglobulin M (IgM) monoclonal gammopathy of undetermined significance (MGUS)/IgM MGUS, plasma cell type/IgM MGUS, NOS	IgM MGUS	IgM MGUS
Non-IgM MGUS	Non-IgM MGUS	Non-IgM MGUS
-	Monoclonal gammopathy of renal significance	Not previously included
Monoclonal Ig deposition diseases/Ig light chain amyloidosis (AL)/localized AL amyloidosis/light chain and heavy chain deposition disease	Diseases with monoclonal Ig deposition	-
-	Ig-related AL amyloidosis	Primary amyloidosis
Monoclonal Ig deposition diseases	Monoclonal Ig deposition disease	Light chain and heavy chain deposition disease
Heavy chain diseases	Heavy chain diseases	Heavy chain diseases
Mu heavy chain disease	Mu heavy chain disease	Mu heavy chain disease
Gamma heavy chain disease	Gamma heavy chain disease	Gamma heavy chain disease
Alpha heavy chain disease	Alpha heavy chain disease	Alpha heavy chain disease
Plasma cell neoplasms	Plasma cell neoplasms	Plasma cell neoplasms
Solitary plasmacytoma of bone/extraosseous plasmacytoma	Plasmacytoma	Plasmacytoma
Multiple myeloma (plasma cell myeloma)	Plasma cell myeloma	Plasma cell myeloma
Multiple myeloma with recurrent genetic abnormality [cyclin D (CCND) family translocation, musculoaponeurotic fibrosarcoma (MAF) family translocation, nuclear receptor binding SET domain protein 2 (NSD2) translocation, with hyperdiploidy]	-	-
-	Plasma cell neoplasms with associated paraneoplastic syndrome	-
-	Polyneuropathy, organomegaly, endocrinopathy, M-protein, skin changes (POEMS) syndrome	-
-	TEMPI syndrome	-
-	Adenopathy and an extensive skin patch overlying a plasmacytoma (AESOP) syndrome	(Same) Except AESOP syndrome not previously included

Changes from the 2016 WHO classification in the ICC classification, based on the ICC 2022, WHO-HAEM5, and WHO revised 4th edition [1–10]. In the ICC, the changes from the 2016 WHO Classification are highlighted with an asterisk. Bold font indicates the principal lymphoma types. TEMPI: telangiectasia-erythrocytosis-monoclonal gammopathy-perinephric-fluid collections-intrapulmonary shunting syndrome; -: no data

Note. Adapted from “A comparison of the International Consensus and 5th World Health Organization classifications of mature B-cell lymphomas,” by Falini B, Martino G, Lazzi S. Leukemia. 2023;37:18–34 (https://www.nature.com/articles/s41375-022-01764-1). CC BY; “The 5th edition of the World Health Organization Classification of Haematolymphoid Tumours: lymphoid neoplasms,” by Alaggio R, Amador C, Anagnostopoulos I, Attygalle AD, Araujo IBO, Berti E, et al. Leukemia. 2022;36:1720–48 (https://www.nature.com/articles/s41375-022-01620-2). CC BY; “WHO classification of tumours [Internet],” Lyons: International Agency for Research on Cancer; c1965–2024 [cited 2023 Jul 7]. Available from: https://whobluebooks.iarc.who.int/structures/haematolymphoid/

Declarations

Author contributions

JC: Conceptualization, Investigation, Writing—original draft, Writing—review & editing. RH: Investigation. NN: Investigation, Supervision. All authors have read and approved the submitted version.

Conflicts of interest

The authors declare that they have no conflicts of interest.

Ethical approval

Not applicable.

Consent to participate

Not applicable.

Consent to publication

Not applicable.

Availability of data and materials

The datasets for this study are available upon request to Joaquim Carreras (joaquim.carreras@tokai.ac.jp).

Funding

Joaquim Carreras was funded by the Ministry of Education, Culture, Sports, Science and Technology (MEXT); and the Japan Society for the Promotion of Science (JSPS) [KAKEN 23K06454, 15K19061, 18K15100, 24590430]; and the Tokai University School of Medicine research incentive assistant plan [2021-B04]. This study was funded by the Cancer Research UK [47550]; UFS project PKAG/309 [C92/A8329]. Rifat Hamoudi is funded by ASPIRE, the technology program management pillar of Abu Dhabi’s Advanced Technology Research Council (ATRC), via the ASPIRE Precision Medicine Research Institute Abu Dhabi (ASPIREPMRIAD) [VRI-20-10]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Copyright

References

Harris NL, Jaffe ES, Stein H, Banks PM, Chan JK, Cleary ML, et al. A revised European-American classification of lymphoid neoplasms: a proposal from the International Lymphoma Study Group. Blood. 1994;84:1361–92. [DOI] [PubMed]

The Non-Hodgkin’s Lymphoma Classification Project. A clinical evaluation of the International Lymphoma Study Group classification of non-Hodgkin’s lymphoma. Blood. 1997;89:3909–18. [DOI] [PubMed]

Jaffe ES, Harris NL, Stein H, Vardiman J, editors. Pathology and genetics of tumours of haematopoietic and lymphoid tissues. 3rd ed. Lyon: International Agency for Research on Cancer; 2001.

Campo E, Swerdlow SH, Harris NL, Pileri S, Stein H, Jaffe ES. The 2008 WHO classification of lymphoid neoplasms and beyond: evolving concepts and practical applications. Blood. 2011;117:5019–32. [DOI] [PubMed] [PMC]

Swerdlow SH, Campo E, Pileri SA, Harris NL, Stein H, Siebert R, et al. The 2016 revision of the World Health Organization classification of lymphoid neoplasms. Blood. 2016;127:2375–90. [DOI] [PubMed] [PMC]

Campo E, Jaffe ES, Cook JR, Quintanilla-Martinez L, Swerdlow SH, Anderson KC, et al. The International Consensus Classification of mature lymphoid neoplasms: a report from the Clinical Advisory Committee. Blood. 2022;140:1229–53. Erratum in: Blood. 2023;141:437. [DOI] [PubMed] [PMC]

Arber DA, Orazi A, Hasserjian RP, Borowitz MJ, Calvo KR, Kvasnicka HM, et al. International Consensus Classification of myeloid neoplasms and acute leukemias: integrating morphologic, clinical, and genomic data. Blood. 2022;140:1200–28. [DOI] [PubMed] [PMC]

Cazzola M, Sehn LH. Developing a classification of hematologic neoplasms in the era of precision medicine. Blood. 2022;140:1193–99. [DOI] [PubMed]

de Leval L, Alizadeh AA, Bergsagel PL, Campo E, Davies A, Dogan A, et al. Genomic profiling for clinical decision making in lymphoid neoplasms. Blood. 2022;140:2193–27. [DOI] [PubMed] [PMC]

10.

Alaggio R, Amador C, Anagnostopoulos I, Attygalle AD, Araujo IBO, Berti E, et al. The 5th edition of the World Health Organization Classification of haematolymphoid tumours: lymphoid neoplasms. Leukemia. 2022;36:1720–48. Erratum in: Leukemia. 2023;37:1944–51. [DOI] [PubMed] [PMC]

11.

Khoury JD, Solary E, Abla O, Akkari Y, Alaggio R, Apperley JF, et al. The 5th edition of the World Health Organization Classification of haematolymphoid tumours: myeloid and histiocytic/dendritic neoplasms. Leukemia. 2022;36:1703–19. [DOI] [PubMed] [PMC]

12.

Swerdlow SH, Campo E, Arber DA, Cazzola M, Cook JR, Döhner H, et al. Response to “The WHO classification of haematolymphoid tumours” (Editorial). Leukemia. 2022;36:2748–9. [DOI] [PubMed]

13.

de Leval L, Jaffe ES. Lymphoma Classification. Cancer J. 2020;26:176–85. [DOI] [PubMed]

14.

Carreras J, Roncador G, Hamoudi R. Artificial intelligence predicted overall survival and classified mature B-cell neoplasms based on immuno-oncology and immune checkpoint panels. Cancers. 2022;14:5318. [DOI] [PubMed] [PMC]

15.

Carreras J, Hamoudi R. Artificial neural network analysis of gene expression data predicted non-Hodgkin lymphoma subtypes with high accuracy. Mach Learn Knowl Extr. 2021;3:720–39. [DOI]

16.

Chiorazzi N, Rai KR, Ferrarini M. Chronic lymphocytic leukemia. N Engl J Med. 2005;352:804–15. [DOI] [PubMed]

17.

Navarro A, Beà S, Jares P, Campo E. Molecular pathogenesis of mantle cell lymphoma. Hematol Oncol Clin North Am. 2020;34:795–807. [DOI] [PubMed] [PMC]

18.

Zucca E, Bertoni F. The spectrum of MALT lymphoma at different sites: biological and therapeutic relevance. Blood. 2016;127:2082–92. [DOI] [PubMed]

19.

Bertoni F, Coiffier B, Salles G, Stathis A, Traverse-Glehen A, Thieblemont C, et al. MALT lymphomas: pathogenesis can drive treatment. Oncology (Williston Park). 2011;25:1134–42, 1147. [PubMed]

20.

Chapuy B, Stewart C, Dunford AJ, Kim J, Kamburov A, Redd RA, et al. Molecular subtypes of diffuse large B cell lymphoma are associated with distinct pathogenic mechanisms and outcomes. Nat Med. 2018;24:679–90. Erratum in: Nat Med. 2018;24:1292. Erratum in: Nat Med. 2018;24:1290–1. [DOI] [PubMed] [PMC]

21.

Wright GW, Huang DW, Phelan JD, Coulibaly ZA, Roulland S, Young RM, et al. A probabilistic classification tool for genetic subtypes of diffuse large B cell lymphoma with therapeutic implications. Cancer Cell. 2020;37:551–68.e14. [DOI] [PubMed] [PMC]

22.

Lacy SE, Barrans SL, Beer PA, Painter D, Smith AG, Roman E, et al. Targeted sequencing in DLBCL, molecular subtypes, and outcomes: a haematological malignancy research network report. Blood. 2020;135:1759–71. [DOI] [PubMed] [PMC]

23.

Runge HFP, Lacy S, Barrans S, Beer PA, Painter D, Smith A, et al. Application of the LymphGen classification tool to 928 clinically and genetically-characterised cases of diffuse large B cell lymphoma (DLBCL). Br J Haematol. 2021;192:216–20. [DOI] [PubMed]

24.

McCarthy, J. What is artificial intelligence? [Internet]. Stanford: Stanford University; 2007 [cited 2022 Dec 6]. Available from: http://jmc.stanford.edu/articles/whatisai.html

25.

What is artificial intelligence? [Internet]. Armonk: IBM Corporation; c1994–2023 [cited 2022 Dec 6 ]. What is artificial intelligence (AI)? [about 2 screens]. Available from: https://www.ibm.com/topics/artificial-intelligence

26.

Turing AM. Computing machinery and intelligence. Mind. 1950;59:433–60. [DOI]

27.

What is strong AI? [Internet]. Armonk: IBM Corporation; c1994–2023 [Cited 2022 Dec 8 ]. What is strong AI?; [about 1 screen]. Available from: https://www.ibm.com/cloud/learn/strong-ai#toc-strong-ai--YaLcx8oG

28.

Carreras J. Artificial intelligence analysis of celiac disease using an autoimmune discovery transcriptomic panel highlighted pathogenic genes including BTLA. Healthcare (Basel). 2022;10:1550. [DOI] [PubMed] [PMC]

29.

Carreras J. Artificial intelligence analysis of ulcerative colitis using an autoimmune discovery transcriptomic panel. Healthcare (Basel). 2022;10:1476. [DOI] [PubMed] [PMC]

30.

Yamamoto Y, Carreras J, Shimizu T, Kakizaki M, Kikuti YY, Roncador G, et al. Anti-HBV drug entecavir ameliorates DSS-induced colitis through PD-L1 induction. Pharmacol Res. 2022;179:105918. [DOI] [PubMed]

31.

Carreras J, Kikuti YY, Miyaoka M, Hiraiwa S, Tomita S, Ikoma H, et al. The use of the random number generator and artificial intelligence analysis for dimensionality reduction of follicular lymphoma transcriptomic data. BioMedInformatics. 2022;2:268–80. [DOI]

32.

Carreras J, Nakamura N, Hamoudi R. Artificial intelligence analysis of gene expression predicted the overall survival of mantle cell lymphoma and a large pan-cancer series. Healthcare (Basel). 2022;10:155. [DOI] [PubMed] [PMC]

33.

Carreras J, Hiraiwa S, Kikuti YY, Miyaoka M, Tomita S, Ikoma H, et al. Artificial neural networks predicted the overall survival and molecular subtypes of diffuse large B-cell lymphoma using a pancancer immune-oncology panel. Cancers (Basel). 2021;13:6384. [DOI] [PubMed] [PMC]

34.

Carreras J, Kikuti YY, Miyaoka M, Roncador G, Garcia JF, Hiraiwa S, et al. Integrative statistics, machine learning and artificial intelligence neural network analysis correlated CSF1R with the prognosis of diffuse large B-cell lymphoma. Hemato. 2021;2:182–206. [DOI]

35.

Carreras J, Kikuti YY, Roncador G, Miyaoka M, Hiraiwa S, Tomita S, et al. High expression of caspase-8 associated with improved survival in diffuse large B-cell lymphoma: machine learning and artificial neural networks analyses. BioMedInformatics. 2021;1:18–46. [DOI]

36.

Carreras J, Kikuti YY, Miyaoka M, Hiraiwa S, Tomita S, Ikoma H, et al. A combination of multilayer perceptron, radial basis function artificial neural networks and machine learning image segmentation for the dimension reduction and the prognosis assessment of diffuse large B-cell lymphoma. AI. 2021;2:106–34. [DOI]

37.

Carreras J, Hamoudi R, Nakamura N. Artificial intelligence analysis of gene expression data predicted the prognosis of patients with diffuse large B-cell lymphoma. Tokai J Exp Clin Med. 2020;45:37–48. [PubMed]

38.

Ma MCJ, Tadros S, Bouska A, Heavican T, Yang H, Deng Q, et al. Subtype-specific and co-occurring genetic alterations in B-cell non-Hodgkin lymphoma. Haematologica. 2022;107:690–701. [DOI] [PubMed] [PMC]

39.

Bishop CM. Neural networks for pattern recognition, 3rd ed. Oxford: Oxford University Press; 1995.

40.

Fine TL. Feedforward neural network methodology, 3rd ed. New York: Springer; 1999.

41.

Haykin S. Neural networks: a comprehensive foundation, 2nd ed. Upper Saddle River: Prentice Hall; 1998.

42.

Ripley BD. Pattern recognition and neural networks. Cambridge: Cambridge University Press; 1996.