Marta Missiaggia
photo: Missiaggia

Assistant Professor of Radiation Physics, Microdosimetry and Biophysical Modeling

Ph.D., 2022 - Universita degli Studi di Trento

Louisiana State University
Department of Physics & Astronomy
459-A Nicholson Hall, Tower Dr.
Baton Rouge, LA 70803-4001
225-578-3006-Office
Marta.Missiaggia@lsu.edu

Research Overview

My research lies at the intersection of radiation physics, microdosimetry, and biophysical modeling to advance our understanding of how ionizing radiation interacts with biological tissues and how these interactions influence therapeutic outcomes. Central to this work is the concept of radiation quality: moving beyond traditional macroscopic descriptors to capture how the microscopic spatial distribution of energy deposition drives biological damage.

Focus Areas and Select Publications

Microdosimetry

I develop and apply microdosimetric techniques, both experimental and computational, to quantify the stochastic nature of energy deposition at cellular and subcellular scales.

  • Missiaggia, M. “On the radiation quality characterization in radiation therapy: from linear energy transfer to experimental microdosimetry.” European Physical Journal Plus (2024).
  • Missiaggia, M., et al. “Investigation of In-Field and Out-of-Field Radiation Quality With Microdosimetry and Its Impact on Relative Biological Effectiveness in Proton Therapy.” International Journal of Radiation Oncology, Biology, Physics (2023).
  • Missiaggia, M., et al. “A novel hybrid microdosimeter for radiation field characterization based on TEPC detector and LGADs tracker: a feasibility study.” Frontiers in Physics (2021).
  • Missiaggia, M., et al. “Microdosimetric measurements as a tool to assess potential in- and out-of-field toxicity regions in proton therapy.” Physics in Medicine & Biology (2020).

Biophysical Modeling

I design biophysical models that connect macro-, micro-, and nanoscale energy deposition to biological endpoints such as DNA damage, cell survival, and treatment response, with a focus on proton and heavy-ion therapy.

  • Battestini, M., Missiaggia, M., et al. “A multiscale radiation biophysical stochastic model describing the cell survival response at ultra-high dose rate over a broad LET, particle type and oxygenation range.” Radiotherapy & Oncology (2025).
  • Bordieri, G., Missiaggia, M.*, et al. “Validation of the Generalized Stochastic Microdosimetric Model (GSM²) over a broad range of LET and particle beam type: a unique model for accurate description of therapy-relevant radiation qualities.” Physics in Medicine & Biology (2024).
  • Missiaggia, M., et al. “Cell survival computation via the Generalized Stochastic Microdosimetric Model (GSM²) - Part II: numerical results.” Radiation Research (2024).
  • Cordoni, F., Missiaggia, M., et al. “Generalized Stochastic Microdosimetric Model: the main formulation.” Physical Review E (2021).

AI and Clinical Outcome Prediction

I develop and apply computational and AI-driven modeling frameworks that integrate physics-based simulations, microdosimetric data, and biological response models. These tools are designed to improve the prediction of radiation effects across scales and to support radiotherapy planning and personalization.

  • DeCunha, J.M., Missiaggia, M., et al. “A Library of Proton Lineal Energy Spectra Spanning the Full Range of Clinically Relevant Energies.” Medical Physics (2025).
  • Cartechini, G., Missiaggia, M., et al. “Integrating microdosimetric in vitro RBE models for particle therapy into TOPAS MC using the MicrOdosimetry-based modeling for RBE Assessment (MONAS) tool.” Physics in Medicine & Biology (2024).
  • Cordoni, F., Missiaggia, M., et al. “An Artificial Intelligence-based model for cell killing prediction: development, validation and explainability analysis of the ANAKIN model.” Physics in Medicine & Biology (2023).
  • Missiaggia, M., et al. “Machine learning techniques applied to therapeutic energies particle tracking with a novel Hybrid Detector for Microdosimetry (HDM).” Physics in Medicine & Biology (2022).

 

 

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