machine learning

Artificial Intelligence in Hematology

AUTOIMMUNE THROMBOCYTOPENIA

“Artificial intelligence will not replace physicians, however, those physicians who use artificial

intelligence will replace those who don’t.”

Dr. Bertalan Mesko, the medical futurist

AS Luchinin

Kirov Research Institute of Hematology and Transfusiology, 72 Krasnoarmeiskaya str., Kirov, Russian Federation, 610027

For correspondence: Aleksandr Sergeevich Luchinin, MD, PhD, 72 Krasnoarmeiskaya str., Kirov, Russian Federation, 610027; Tel.: +7(919)506-87-86; e-mail: glivec@mail.ru

For citation: Luchinin AS. Artificial Intelligence in Hematology. Clinical oncohematology. 2022;15(1):16–27. (In Russ).

DOI: 10.21320/2500-2139-2022-15-1-16-27

ABSTRACT

‘Artificial Intelligence’ is a general term to designate computer technologies for solving the problems that require implementation of human intelligence, for example, human voice or image recognition. Most artificial intelligence products with application in healthcare are associated with machine learning, i.e., a field of informatics and statistics dealing with the generation of predictive or descriptive models through data-based learning, rather than programming of strict rules. Machine learning has been widely used in pathomorphology, radiology, genomics, and electronic medical record data analysis. In line with the current trend, artificial intelligence technologies will most likely become increasingly integrated into health research and practice, including hematology. Thus, artificial intelligence and machine learning call for attention and understanding on the part of researchers and clinical physicians. The present review covers important terms and basic concepts of these technologies, as well as offers examples of their actual use in hematological research and practice.

Keywords: artificial intelligence, machine learning, neural network.

Received: September 23, 2021

Accepted: December 15, 2021

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Статистика Plumx английский

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Immunohistochemical Subtype and Parameters of International Prognostic Index in the New Prognostic Model of Diffuse Large B-Cell Lymphoma

LYMPHOID TUMORS , , , ,

SV Samarina1, AS Luchinin1, NV Minaeva1, IV Paramonov1, DA D’yakonov1, EV Vaneeva1, VA Rosin1, SV Gritsaev2

1 Kirov Research Institute of Hematology and Transfusiology, 72 Krasnoarmeiskaya str., Kirov, Russian Federation, 610027

2 Russian Research Institute of Hematology and Transfusiology, 16 2-ya Sovetskaya str., Saint Petersburg, Russian Federation, 191024

For correspondence: Svetlana Valer’evna Samarina, 72 Krasnoarmeiskaya str., Kirov, Russian Federation, 610027; Tel.: +7(912)732-47-56; e-mail: samarinasv2010@mail.ru

For citation: Samarina SV, Luchinin AS, Minaeva NV, et al. Immunohistochemical Subtype and Parameters of International Prognostic Index in the New Prognostic Model of Diffuse Large B-Cell Lymphoma. Clinical oncohematology. 2019;12(4):385–90 (In Russ).

DOI: 10.21320/2500-2139-2019-12-4-385-390

ABSTRACT

Aim. To develop an integrated prognostic model of diffuse large B-cell lymphoma (DLBCL) on the basis of immunohistochemical tumor subtype and parameters of International Prognostic Index (IPI).

Materials & Methods. Out of 104 DLBCL patients in the data base 81 (77.9 %) met the eligibility criteria. Median age was 58 years (range 23–83). All patients were treated with R-СНОР. The creation of overall survival (OS) prognostic model for DLBCL patients was based on machine learning with classification and regression trees. OS was analyzed using Kaplan-Meier method. Survival curves were compared by means of log rank test and hazard ratio (HR). Any test was considered significant if two-sided level of p < 0.05 was reached.

Results. Following the developed model three groups of patients were identified: the 1st group of low risk (the combination of low, intermediate-low, and intermediate-high risks according to IPI and GCB subtype); the 2nd group of intermediate risk (the combination of low, intermediate-low, and intermediate-high risks according to IPI and non-GCB subtype); the 3d group of high risk (irrespective of subtype). In the group of low risk (n = 26) 2-year OS during the monitoring period was 100 %. In the group of intermediate risk (n = 34) median OS was not reached, 2-year OS was 74 %, and expected 5-year OS was 68 %. In the group of high risk (n = 21) median OS was 25 months, 2-year OS was 46 %, and expected 5-year OS was 37 % (log rank p < 0.0001). HR calculated for the high-risk group compared with the low- and intermediate-risk groups was 5.1 (95% CI 2.1–12.1; p = 0.0003).

Conclusion. A new integrated system of DLBCL prognosis is suggested which includes IPI risk parameters and immunohistochemical subtype based on Hans algorithm. This prognostic system can be used in clinical practice for DLBCL patient stratification and risk-adapted therapy.

Keywords: diffuse large B-cell lymphoma, overall survival, prognosis, International Prognostic Index, machine learning.

Received: March 18, 2019

Accepted: August 27, 2019

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