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SIS epidemiological model for adaptive RT: Forecasting the parotid glands shrinkage during tomotherapy treatment
Author(s) -
Maffei Nicola,
Guidi Gabriele,
Vecchi Claudio,
Ciarmatori Alberto,
Gottardi Giovanni,
Meduri Bruno,
D'Angelo Elisa,
Bruni Alessio,
Mazzeo Ercole,
Pratissoli Silvia,
Giacobazzi Patrizia,
Baldazzi Giuseppe,
Lohr Frank,
Costi Tiziana
Publication year - 2016
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4954004
Subject(s) - tomotherapy , image warping , image registration , imaging phantom , artificial intelligence , nuclear medicine , mathematics , computer science , medicine , radiation therapy , radiology , image (mathematics)
Purpose: A susceptible‐infected‐susceptible (SIS) epidemic model was applied to radiation therapy (RT) treatments to predict morphological variations in head and neck (H&N) anatomy. Methods: 360 daily MVCT images of 12 H&N patients treated by tomotherapy were analyzed in this retrospective study. Deformable image registration (DIR) algorithms, mesh grids, and structure recontouring, implemented in the RayStation treatment planning system (TPS), were applied to assess the daily organ warping. The parotid's warping was evaluated using the epidemiological approach considering each vertex as a single subject and its deformed vector field (DVF) as an infection. Dedicated IronPython scripts were developed to export daily coordinates and displacements of the region of interest (ROI) from the TPS. matlab tools were implemented to simulate the SIS modeling. Finally, the fully trained model was applied to a new patient. Results: A QUASAR phantom was used to validate the model. The patients’ validation was obtained setting 0.4 cm of vertex displacement as threshold and splitting susceptible ( S ) and infectious ( I ) cases. The correlation between the epidemiological model and the parotids’ trend for further optimization of alpha and beta was carried out by Euclidean and dynamic time warping (DTW) distances. The best fit with experimental conditions across all patients (Euclidean distance of 4.09 ± 1.12 and DTW distance of 2.39 ± 0.66) was obtained setting the contact rate at 7.55 ± 0.69 and the recovery rate at 2.45 ± 0.26; birth rate was disregarded in this constant population. Conclusions: Combining an epidemiological model with adaptive RT (ART), the authors’ novel approach could support image‐guided radiation therapy (IGRT) to validate daily setup and to forecast anatomical variations. The SIS‐ART model developed could support clinical decisions in order to optimize timing of replanning achieving personalized treatments.