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A stochastic model for tumor geometry evolution during radiation therapy in cervical cancer
Author(s) -
Liu Yifang,
Chan Timothy C. Y.,
Lee ChiGuhn,
Cho YoungBin,
Islam Mohammad K.
Publication year - 2014
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 - 10.1118/1.4859355
Subject(s) - voxel , markov model , markov chain , sensitivity (control systems) , markov process , mathematics , shrinkage , constant (computer programming) , statistical model , stochastic modelling , computer science , statistics , algorithm , artificial intelligence , electronic engineering , engineering , programming language
Purpose: To develop mathematical models to predict the evolution of tumor geometry in cervical cancer undergoing radiation therapy.Methods: The authors develop two mathematical models to estimate tumor geometry change: a Markov model and an isomorphic shrinkage model. The Markov model describes tumor evolution by investigating the change in state (either tumor or nontumor) of voxels on the tumor surface. It assumes that the evolution follows a Markov process. Transition probabilities are obtained using maximum likelihood estimation and depend on the states of neighboring voxels. The isomorphic shrinkage model describes tumor shrinkage or growth in terms of layers of voxels on the tumor surface, instead of modeling individual voxels. The two proposed models were applied to data from 29 cervical cancer patients treated at Princess Margaret Cancer Centre and then compared to a constant volume approach. Model performance was measured using sensitivity and specificity.Results: The Markov model outperformed both the isomorphic shrinkage and constant volume models in terms of the trade‐off between sensitivity (target coverage) and specificity (normal tissue sparing). Generally, the Markov model achieved a few percentage points in improvement in either sensitivity or specificity compared to the other models. The isomorphic shrinkage model was comparable to the Markov approach under certain parameter settings. Convex tumor shapes were easier to predict.Conclusions: By modeling tumor geometry change at the voxel level using a probabilistic model, improvements in target coverage and normal tissue sparing are possible. Our Markov model is flexible and has tunable parameters to adjust model performance to meet a range of criteria. Such a model may support the development of an adaptive paradigm for radiation therapy of cervical cancer.