Mathematical modeling of tumor response to radiotherapy has the potential of enhancing the quality of the treatment plan, which can be even tailored on an individual basis. Lack of extensive in vivo validation has prevented, however, reliable clinical translation of modeling outcomes. Image guided radiotherapy (IGRT) is a consolidated treatment modality based on computed tomographic (CT) imaging for tumor delineation and volumetric cone beam CT data for periodic checks during treatment. In this work, a macroscopic model of tumor growth and radiation response is proposed, being able to adapt along the treatment course as volumetric tumor data become available. Model parameter learning was based on cone beam CT images in 13 uterine cervical cancer patients, subdivided into three groups (G1, G2, G3) according to tumor type and treatment. Three groupspecific parameter sets (PS1, PS2 and PS3) on one general parameter set (PSa) were applied. The corresponding average model fitting errors were 14, 18, 13 and 21%, respectively. The model adaptation testing was performed using volume data of three patients, other than the ones involved in the parameter learning. The extrapolation performance of the general model was improved, while comparable prediction errors were found for the groupspecific approach. This suggests that an on-line parameter tuning can overcome the limitations of a suboptimal patient stratification, which appeared otherwise a critical issue.

Adaptive mathematical model of tumor response to radiotherapy based on CBCT data / A. Belfatto, M. Riboldi, D. Ciardo, A. Cecconi, R. Lazzari, B. Jereczek-Fossa, R. Orecchia, G. Baroni, P. Cerveri. - In: IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS. - ISSN 2168-2194. - 20:3(2016 May), pp. 802-809. [10.1109/JBHI.2015.2453437]

Adaptive mathematical model of tumor response to radiotherapy based on CBCT data

B. Jereczek-Fossa;R. Orecchia;
2016-05

Abstract

Mathematical modeling of tumor response to radiotherapy has the potential of enhancing the quality of the treatment plan, which can be even tailored on an individual basis. Lack of extensive in vivo validation has prevented, however, reliable clinical translation of modeling outcomes. Image guided radiotherapy (IGRT) is a consolidated treatment modality based on computed tomographic (CT) imaging for tumor delineation and volumetric cone beam CT data for periodic checks during treatment. In this work, a macroscopic model of tumor growth and radiation response is proposed, being able to adapt along the treatment course as volumetric tumor data become available. Model parameter learning was based on cone beam CT images in 13 uterine cervical cancer patients, subdivided into three groups (G1, G2, G3) according to tumor type and treatment. Three groupspecific parameter sets (PS1, PS2 and PS3) on one general parameter set (PSa) were applied. The corresponding average model fitting errors were 14, 18, 13 and 21%, respectively. The model adaptation testing was performed using volume data of three patients, other than the ones involved in the parameter learning. The extrapolation performance of the general model was improved, while comparable prediction errors were found for the groupspecific approach. This suggests that an on-line parameter tuning can overcome the limitations of a suboptimal patient stratification, which appeared otherwise a critical issue.
mathematical model; tumor growth; parameter adaptation; radiation therapy; IGRT
Settore MED/36 - Diagnostica per Immagini e Radioterapia
9-lug-2015
Article (author)
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2434/352404
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