Machine Learning Methods for Predicting Tumor Volume in Rats after Termination of Complex Treatment with a Varying Dose of Cyclophosphamide
Abstract
Konstantin Sarin, Marina Bardamova, Mikhail Svetlakov, Ilya Hodashinsky, Evgeny Kostyuchenko, Denis Pakhmurin, Artem Slezkin, Victoria Pakhmurina, Gennadiy Mikhalchenko, Ivan Sidorov
Predictive determination of tumor volume is a significant task in cancer treatment. The importance of predictive definition is due to its relevance in research and clinical practice. The use of predictive models makes it possible to evaluate the effectiveness of the treatment used in the early stages and decide on its correction in case of unsatisfactory prognosis. The paper investigates models for predictive determination of tumor volume after applying a complex treatment that combines chemotherapy and hyperthermia. Considered machine learning models are ensembles of decision trees, regression based on Gaussian processes, ridge regression, and Bayesian regression. The raw data for machine learning comes from an experiment on laboratory rats. As part of the experiment, all animals received an injection of the fast-growing Walker-256 carcinoma into the left thigh. After the injection, the animals were subjected to complex treatment with cyclophosphamide and hyperthermia. During the study, the rats were divided into three groups. The division into groups depended on the dose of cyclophosphamide received. The determination of informative features is based on the application of greedy and genetic algorithms. The analysis of the selected features with their use in various types of models. The projections of remission and the depth of remission were calculated based on the tumor volume's predicted values. The applicability of several investigated models for predictive assessment of the therapeutic effect of combined treatment has been shown using Student’s and Friedman's statistical tests.
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| Pubmed Style Sarin K, Bardamova M, Svetlakov M, Hodashinsky I, Kostyuchenko E, Pakhmurin D, Slezkin A, Pakhmurina V, Mikhalchenko G, Sidorov I. Machine Learning Methods for Predicting Tumor Volume in Rats after Termination of Complex Treatment with a Varying Dose of Cyclophosphamide. SRP. 2021; 12(1): 993-1005. doi:10.31838/srp.2021.1.139 Web Style Sarin K, Bardamova M, Svetlakov M, Hodashinsky I, Kostyuchenko E, Pakhmurin D, Slezkin A, Pakhmurina V, Mikhalchenko G, Sidorov I. Machine Learning Methods for Predicting Tumor Volume in Rats after Termination of Complex Treatment with a Varying Dose of Cyclophosphamide. http://www.sysrevpharm.org/?mno=45860 [Access: March 29, 2021]. doi:10.31838/srp.2021.1.139 AMA (American Medical Association) Style Sarin K, Bardamova M, Svetlakov M, Hodashinsky I, Kostyuchenko E, Pakhmurin D, Slezkin A, Pakhmurina V, Mikhalchenko G, Sidorov I. Machine Learning Methods for Predicting Tumor Volume in Rats after Termination of Complex Treatment with a Varying Dose of Cyclophosphamide. SRP. 2021; 12(1): 993-1005. doi:10.31838/srp.2021.1.139 Vancouver/ICMJE Style Sarin K, Bardamova M, Svetlakov M, Hodashinsky I, Kostyuchenko E, Pakhmurin D, Slezkin A, Pakhmurina V, Mikhalchenko G, Sidorov I. Machine Learning Methods for Predicting Tumor Volume in Rats after Termination of Complex Treatment with a Varying Dose of Cyclophosphamide. SRP. (2021), [cited March 29, 2021]; 12(1): 993-1005. doi:10.31838/srp.2021.1.139 Harvard Style Sarin, K., Bardamova, . M., Svetlakov, . M., Hodashinsky, . I., Kostyuchenko, . E., Pakhmurin, . D., Slezkin, . A., Pakhmurina, . V., Mikhalchenko, . G. & Sidorov, . I. (2021) Machine Learning Methods for Predicting Tumor Volume in Rats after Termination of Complex Treatment with a Varying Dose of Cyclophosphamide. SRP, 12 (1), 993-1005. doi:10.31838/srp.2021.1.139 Turabian Style Sarin, Konstantin, Marina Bardamova, Mikhail Svetlakov, Ilya Hodashinsky, Evgeny Kostyuchenko, Denis Pakhmurin, Artem Slezkin, Victoria Pakhmurina, Gennadiy Mikhalchenko, and Ivan Sidorov. 2021. Machine Learning Methods for Predicting Tumor Volume in Rats after Termination of Complex Treatment with a Varying Dose of Cyclophosphamide. Systematic Reviews in Pharmacy, 12 (1), 993-1005. doi:10.31838/srp.2021.1.139 Chicago Style Sarin, Konstantin, Marina Bardamova, Mikhail Svetlakov, Ilya Hodashinsky, Evgeny Kostyuchenko, Denis Pakhmurin, Artem Slezkin, Victoria Pakhmurina, Gennadiy Mikhalchenko, and Ivan Sidorov. "Machine Learning Methods for Predicting Tumor Volume in Rats after Termination of Complex Treatment with a Varying Dose of Cyclophosphamide." Systematic Reviews in Pharmacy 12 (2021), 993-1005. doi:10.31838/srp.2021.1.139 MLA (The Modern Language Association) Style Sarin, Konstantin, Marina Bardamova, Mikhail Svetlakov, Ilya Hodashinsky, Evgeny Kostyuchenko, Denis Pakhmurin, Artem Slezkin, Victoria Pakhmurina, Gennadiy Mikhalchenko, and Ivan Sidorov. "Machine Learning Methods for Predicting Tumor Volume in Rats after Termination of Complex Treatment with a Varying Dose of Cyclophosphamide." Systematic Reviews in Pharmacy 12.1 (2021), 993-1005. Print. doi:10.31838/srp.2021.1.139 APA (American Psychological Association) Style Sarin, K., Bardamova, . M., Svetlakov, . M., Hodashinsky, . I., Kostyuchenko, . E., Pakhmurin, . D., Slezkin, . A., Pakhmurina, . V., Mikhalchenko, . G. & Sidorov, . I. (2021) Machine Learning Methods for Predicting Tumor Volume in Rats after Termination of Complex Treatment with a Varying Dose of Cyclophosphamide. Systematic Reviews in Pharmacy, 12 (1), 993-1005. doi:10.31838/srp.2021.1.139 |
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