Lesions of lymphoid and hematopoietic tissue are the leading cause of mortality in young population among all malignant entities. To support the patients it should provide timely a reliable verification, which is the task of pathology service. Along with increasing of morbidity and mortality from malignant entities also the burden on pathologists is increasing. High work-flow on physicians combined with lack staffing remain urgent problems in healthcare. Currently, the application of artificial intelligence (AI) to laboratory practice plays a crucial role in medicine. The application of software with generative pre-trained model can support a pathologist in the interpretation of histological pattern and diagnosis of lymphatic system tumor in both surgical and biopsy samples. Diagnostic decision support improves a quality and accuracy of diagnosis and optimize the work process. Abstract review provides an analysis of recent scientific studies investigating the potential use of AI in the diagnosis of malignant entities of lymphoid and hematopoietic tissue.
Nikolai P. Zverev – resident, State Budgetary Institution 'A.S. Loginov Moscow Clinical Research and Practice Center', Department of Health Care of Moscow, Moscow, Russia. E-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
Ilya V. Tsvetnov – pathologist, State Budgetary Institution 'A. S. Loginov Moscow Clinical Research and Practice Center', Department of Health Care of Moscow, Moscow, Russia. E-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
Mark V. Voloshin – pathologist, State Budgetary Institution 'A.S. Loginov Moscow Clinical Research and Practice Center', Department of Health Care of Moscow, Moscow, Russia. E-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
Nikolay S. Karnaukhov – MD, PhD, State Budgetary Institution 'A. S. Loginov Moscow Clinical Research and Practice Center', Department of Health Care of Moscow, Moscow, Russia. E-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
1. Bray F, Laversanne M, Weiderpass E, Soerjomataram I. The ever-increasing importance of cancer as a leading cause of premature death worldwide. Cancer. 2021;127(16):3029-30. DOI: 10.1002/cncr.33587
2. Omran AR. The epidemiologic transition. A theory of the epidemiology of population change. Milbank Mem Fund Q. 1971;49(4):509-38.
3. Kaprin AD, Starinskij VV, SHahzadova AO, editors. Zlokachestvennye novoobrazovaniya v Rossii v 2021 godu (zabolevaemost' i smertnost'). Moscow: MNIOI im. P. A. Gercena; 2022. (In Russ).
4. Pantanowitz L, Valenstein PN, Evans AJ, et al. Review of the current state of whole slide imaging in pathology. J Pathol Inform. 2011;2:36. DOI: 10.4103/2153-3539.83746
5. Remez AI, Zhuravlev AS, Fattakhov AO, et al. Digital pathology in Russia: experience and perspectives. RMJ. Medical Review. 2018;6:19-21.
6. Cutler DM. What artificial intelligence means for health care. JAMA Health Forum. 2023;4(7):e232652. DOI: 10.1001/jamahealthforum.2023.2652
7. Steinbuss G, Kriegsmann M, Zgorzelski C, et al. Deep learning for the classification of non-hodgkin lymphoma on histopathological images. Cancers (Basel). 2021;13(10):2419. DOI: 10.3390/cancers13102419
8. Tsakiroglou AM, Bacon CM, Shingleton D, et al. Lymphoma triage from H&E using AI for improved clinical management. J Clin Pathol. 2023. DOI: 10.1136/jcp-2023-209186
9. Syrykh C, Abreu A, Amara N, et al. Accurate diagnosis of lymphoma on whole-slide histopathology images using deep learning. NPJ Digit Med. 2020;3:63. DOI: 10.1038/s41746-020-0272-0
10. Swiderska-Chadaj Z, Hebeda KM, van den Brand M, et al. Artificial intelligence to detect MYC translocation in slides of diffuse large B-cell lymphoma. Virchows Arch. 2021;479(3):617-21. DOI: 10.1007/s00428-020-02931-4
11. Perry C, Greenberg O, Haberman S, et al. Image-based deep learning detection of high-grade B-cell lymphomas directly from hematoxylin and eosin images. Cancers (Basel). 2023;15(21):5205. DOI: 10.3390/cancers15215205
12. Yu WH, Li CH, Wang RC, et al. Machine learning based on morphological features enables classification of primary intestinal T-cell lymphomas. Cancers (Basel). 2021;21(13):54-63. DOI: 10.3390/cancers13215463
13. Irshaid L, Bleiberg J, Weinberger E, et al. Histopathologic and machine deep learning criteria to predict lymphoma transformation in bone marrow biopsies. Arch Pathol Lab Med. 2022;146(2):182-93. DOI: 10.5858/arpa.2020-0510-OA
14. Doeleman T, Hondelink LM, Vermeer MH, et al. Artificial intelligence in digital pathology of cutaneous lymphomas: A review of the current state and future perspectives. Semin Cancer Biol. 2023;94:81-8. DOI: 10.1016/j.semcancer.2023.06.004
15. Cristian M, A?chie M, Deacu M, et al. Comparison of Ki67 proliferation index in gastrointestinal non-Hodgkin large B-cell lymphomas: the conventional method of evaluation or AI evaluation? Diagnostics (Basel). 2023;13(17):2775. DOI: 10.3390/diagnostics13172775
16. Ehteshami Bejnordi B, Veta M, Johannes van Diest P, et al. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA. 2017;318(22):2199-210. DOI: 10.1001/jama.2017.14585
17. Caldonazzi N, Rizzo PC, Eccher A, et al. Value of artificial intelligence in evaluating lymph node metastases. Cancers (Basel). 2023;15(9):2491. DOI: 10.3390/cancers15092491
18. Borbat AM. Nejronnye seti v morfologicheskoj diagnostike. Klinicheskaya i eksperimental'naya morfologiya. 2020;9(2):11-15. (In Russ).
19. Niazi MKK, Parwani AV, Gurcan MN. Digital pathology and artificial intelligence. Lancet Oncol. 2019;20(5):e253-e261. DOI: 10.1016/S1470-2045(19)30154-8
20. El Achi H, Khoury JD. Artificial Intelligence and Digital Microscopy Applications in Diagnostic Hematopathology. Cancers (Basel). 2020;12(4):797. DOI: 10.3390/cancers12040797
21. Moran-Sanchez J, Santisteban-Espejo A, Martin-Piedra MA, et al. Translational applications of artificial intelligence and machine learning for diagnostic pathology in lymphoid neoplasms: a comprehensive and evolutive analysis. Biomolecules. 2021;11(6):793. DOI: 10.3390/biom11060793
22. Shelekhova KV. Vozmozhnosti i perspektivy iskusstvennogo intellekta v patomorfologicheskoj diagnostike raka. Prakticheskaya onkologiya. 2022:23(4):234-8. (In Russ).
23. Hu Y, Luo Y, Tang G, et al. Artificial intelligence and its applications in digital hematopathology. Blood Sci. 2022;4(3):136-42. DOI: 10.1097/BS9.0000000000000130
24. Lushnikov EF. Celi i metody raboty patologoanatoma v sovremennoj onkologii. Onkologiya. Zhurnal im. PA Gercena. 2017;6(2):67-72. (In Russ).
25. Frank GA, Starodubova VI, editors. Sostoyanie i osnovnye zadachi razvitiya patologo-anatomicheskoj sluzhby Rossijskoj Federacii: Otraslevoe statisticheskoe issledovanie za 2022 god. Moscow: Minzdrav Rossii; 2023.(In Russ).
26. Shafi S, Parwani AV. Artificial intelligence in diagnostic pathology. Diagn Pathol. 2023;18(1):109. DOI: 10.1186/s13000-023-01375-z
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