Academic Journal
pyMKM: An Open-Source Python Package for Microdosimetric Kinetic Model Calculation in Research and Clinical Applications.
| Τίτλος: | pyMKM: An Open-Source Python Package for Microdosimetric Kinetic Model Calculation in Research and Clinical Applications. |
|---|---|
| Συγγραφείς: | Magro, Giuseppe, Pavanello, Vittoria, Jia, Yihan, Grevillot, Loïc, Glimelius, Lars, Mairani, Andrea |
| Πηγή: | Computation; Nov2025, Vol. 13 Issue 11, p264, 21p |
| Θεματικοί όροι: | Microdosimetry, Radiation dosimetry, Computer simulation, Python programming language, Open source software, Physiological effects of radiation |
| Περίληψη: | 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. [ABSTRACT FROM AUTHOR] |
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| Βάση Δεδομένων: | Biomedical Index |
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| Items | – Name: Title Label: Title Group: Ti Data: pyMKM: An Open-Source Python Package for Microdosimetric Kinetic Model Calculation in Research and Clinical Applications. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Magro%2C+Giuseppe%22">Magro, Giuseppe</searchLink><br /><searchLink fieldCode="AR" term="%22Pavanello%2C+Vittoria%22">Pavanello, Vittoria</searchLink><br /><searchLink fieldCode="AR" term="%22Jia%2C+Yihan%22">Jia, Yihan</searchLink><br /><searchLink fieldCode="AR" term="%22Grevillot%2C+Loïc%22">Grevillot, Loïc</searchLink><br /><searchLink fieldCode="AR" term="%22Glimelius%2C+Lars%22">Glimelius, Lars</searchLink><br /><searchLink fieldCode="AR" term="%22Mairani%2C+Andrea%22">Mairani, Andrea</searchLink> – Name: TitleSource Label: Source Group: Src Data: Computation; Nov2025, Vol. 13 Issue 11, p264, 21p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Microdosimetry%22">Microdosimetry</searchLink><br /><searchLink fieldCode="DE" term="%22Radiation+dosimetry%22">Radiation dosimetry</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+simulation%22">Computer simulation</searchLink><br /><searchLink fieldCode="DE" term="%22Python+programming+language%22">Python programming language</searchLink><br /><searchLink fieldCode="DE" term="%22Open+source+software%22">Open source software</searchLink><br /><searchLink fieldCode="DE" term="%22Physiological+effects+of+radiation%22">Physiological effects of radiation</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Computation is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/computation13110264 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 21 StartPage: 264 Subjects: – SubjectFull: Microdosimetry Type: general – SubjectFull: Radiation dosimetry Type: general – SubjectFull: Computer simulation Type: general – SubjectFull: Python programming language Type: general – SubjectFull: Open source software Type: general – SubjectFull: Physiological effects of radiation Type: general Titles: – TitleFull: pyMKM: An Open-Source Python Package for Microdosimetric Kinetic Model Calculation in Research and Clinical Applications. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Magro, Giuseppe – PersonEntity: Name: NameFull: Pavanello, Vittoria – PersonEntity: Name: NameFull: Jia, Yihan – PersonEntity: Name: NameFull: Grevillot, Loïc – PersonEntity: Name: NameFull: Glimelius, Lars – PersonEntity: Name: NameFull: Mairani, Andrea IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 20793197 Numbering: – Type: volume Value: 13 – Type: issue Value: 11 Titles: – TitleFull: Computation Type: main |
| ResultId | 1 |