Vol. 17 No. 3 (2024), LYMPHOID TUMORS
Vol. 17 No. 3 (2024)
LYMPHOID TUMORS

Multiple Myeloma: Simulation of Therapy Scenarios Using the Monte Carlo Method

Published 01.07.2024
Aleksander Sergeevich Luchinin
Kirov Research Institute of Hematology and Transfusiology, 72 Krasnoarmeiskaya ul., Kirov, Russian Federation, 610027
https://orcid.org/0009-0006-0020-3168
A.A. Semenova
NN Blokhin National Medical Cancer Research Center, 24 Kashirskoye sh., Moscow, Russian Federation, 155522
https://orcid.org/0000-0003-4951-3053
S.V. Semochkin
NI Pirogov Russian National Research Medical University, 1 Ostrovityanova ul., Moscow, Russian Federation, 117997; PA Gertsen Moscow Oncology Research Institute, branch of the NMRC of Radiology, 3 2-i Botkinskii pr-d, Moscow, Russian Federation, 125284
https://orcid.org/0000-0002-8129-8114
2024-3
PDF_2024-17-3_246-255 (Russian)

Keywords

multiple myeloma
treatment
clinical decision support system
Monte Carlo method

How to Cite

1.
Luchinin A.S., Semenova A.A., Semochkin S.V. Multiple Myeloma: Simulation of Therapy Scenarios Using the Monte Carlo Method. Клиническая онкогематология. 2024;17(3):246-255. doi:10.21320/2500-2139-2024-17-3-246-255
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Abstract

AIM. To develop an information and retrieval system for hematologists which would enable effective decision making in multiple myeloma (MM) treatment through simulation and prediction of response to therapy considering a patient’s clinical profile-related characteristics and based on the analysis of data from public science sources.

MATERIALS & METHODS. The analysis included 145 therapeutic options and 56,217 MM patients enrolled in 311 clinical studies, the results of which were published in the medical literature from 2003 to 2024. To simulate therapy scenarios, the Monte Carlo method was used for calculating the probability of achieving very good and even better partial response in patients with different characteristics that define not only their clinical profile but also the chemotherapy variants.

RESULTS. This study introduces an interactive online application called М-BОТ (available at oncotriage.ru) enabling to predict response to therapy under certain specified conditions and to visualize the result as real-time ranking of therapeutic options via the user interface. Apart from a patient’s clinical profile-related characteristics underlying MM treatment decision making, it is possible to select trials by their types and numbers of patients enrolled.

CONCLUSION. The therapy recommendations resulted from simulation of different MM therapy scenarios with the use of the Monte Carlo method considerably extend the potential for rapid retrieval of reliable science information which would confirm the optimal choice of a therapeutic option in the given clinical setting. In future, this approach can be regarded as a basis for building up a support system in individual and consensus decision making. It will allow for predicting the efficacy of multi-stage MM treatment strategies with several therapy lines and their safety as well.

PDF_2024-17-3_246-255 (Russian)

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