pyMKM: An Open-Source Python Package for Microdosimetric Kinetic Model Calculation in Research and Clinical Applications
Among existing radiobiological models, the MKM and its extensions (SMK and OSMK) have demonstrated strong predictive capabilities but remain computationally demanding. To address this, we present pyMKM v0.1.0, an open-source Python package for the generation of microdosimetric tables and radiobiolog...
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| Vydáno v: | Computation Ročník 13; číslo 11; s. 264 |
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01.11.2025
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| Abstract | Among existing radiobiological models, the MKM and its extensions (SMK and OSMK) have demonstrated strong predictive capabilities but remain computationally demanding. To address this, we present pyMKM v0.1.0, an open-source Python package for the generation of microdosimetric tables and radiobiological quantities based on these models. The package includes modules for track structure integration, saturation and stochastic corrections, oxygen modulation, and survival fraction computation. Validation was conducted against multiple published datasets across various ion species, LET values, and cell lines under both normoxic and hypoxic conditions. Quantitative comparisons showed high agreement with reference data, with average log errors typically below 0.06 and symmetric mean absolute percentage errors under 2%. The software achieved full unit test coverage and successful execution across multiple Python versions through continuous integration workflows. These results confirm the numerical accuracy, structural robustness, and reproducibility of pyMKM. The package provides a transparent, modular, and extensible tool for microdosimetric modeling in support of radiobiological studies, Monte Carlo-based dose calculation, and biologically guided treatment planning. |
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| AbstractList | Among existing radiobiological models, the MKM and its extensions (SMK and OSMK) have demonstrated strong predictive capabilities but remain computationally demanding. To address this, we present pyMKM v0.1.0, an open-source Python package for the generation of microdosimetric tables and radiobiological quantities based on these models. The package includes modules for track structure integration, saturation and stochastic corrections, oxygen modulation, and survival fraction computation. Validation was conducted against multiple published datasets across various ion species, LET values, and cell lines under both normoxic and hypoxic conditions. Quantitative comparisons showed high agreement with reference data, with average log errors typically below 0.06 and symmetric mean absolute percentage errors under 2%. The software achieved full unit test coverage and successful execution across multiple Python versions through continuous integration workflows. These results confirm the numerical accuracy, structural robustness, and reproducibility of pyMKM. The package provides a transparent, modular, and extensible tool for microdosimetric modeling in support of radiobiological studies, Monte Carlo-based dose calculation, and biologically guided treatment planning. |
| Audience | Academic |
| Author | Magro, Giuseppe Pavanello, Vittoria Jia, Yihan Grevillot, Loïc Mairani, Andrea Glimelius, Lars |
| Author_xml | – sequence: 1 givenname: Giuseppe orcidid: 0000-0001-7140-8642 surname: Magro fullname: Magro, Giuseppe – sequence: 2 givenname: Vittoria orcidid: 0009-0005-2010-8291 surname: Pavanello fullname: Pavanello, Vittoria – sequence: 3 givenname: Yihan orcidid: 0000-0001-8411-0036 surname: Jia fullname: Jia, Yihan – sequence: 4 givenname: Loïc orcidid: 0000-0003-3433-9582 surname: Grevillot fullname: Grevillot, Loïc – sequence: 5 givenname: Lars orcidid: 0000-0002-9112-891X surname: Glimelius fullname: Glimelius, Lars – sequence: 6 givenname: Andrea surname: Mairani fullname: Mairani, Andrea |
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| Cites_doi | 10.1038/s41598-017-18871-0 10.1667/RR2842.1 10.1136/bmjopen-2024-098151 10.1088/1361-6560/aa9102 10.1088/0031-9155/31/11/002 10.1093/jrr/rrad049 10.3389/fphy.2020.578492 10.1088/1361-6560/aa6be9 10.1088/1361-6560/ab6eba 10.1080/09553002.2021.1956003 10.1016/j.nima.2016.06.125 10.1016/0273-1177(95)00784-C 10.1088/1361-6560/adc5d6 10.1088/1361-6560/aa642b 10.1016/j.ijrobp.2008.02.037 10.1667/0033-7587(2000)154[0485:IOAAHC]2.0.CO;2 10.1667/RR0536.1 10.1088/0031-9155/55/22/008 10.1088/0031-9155/53/1/003 10.1051/epjn/2024015 10.1088/0031-9155/58/19/6827 10.1016/j.ijrobp.2020.05.041 10.1088/1361-6560/aa9a68 10.1038/srep17016 10.1088/1361-6560/aabede 10.1080/00223131.2023.2275736 10.1088/1361-6560/ac29cc |
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| SubjectTerms | Accuracy Energy Errors hadrontherapy Hypoxia linear energy transfer microdosimetric kinetic model Open source software open-source python package Radiation therapy relative biological effectiveness |
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| Title | pyMKM: An Open-Source Python Package for Microdosimetric Kinetic Model Calculation in Research and Clinical Applications |
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