Adaptive Mathematical Model of Tumor Response to Radiotherapy Based on CBCT Data

A. Belfatto, M. Riboldi, Delia Ciardo, Agnese Cecconi, Roberta Lazzari, Barbara Alicja Jereczek, Roberto Orecchia, G. Baroni, P. Cerveri

Research output: Contribution to journalArticlepeer-review


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 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 study, 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 group-specific 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 group-specific approach. This suggests that an online parameter tuning can overcome the limitations of a suboptimal patient stratification, which appeared otherwise a critical issue.

Original languageEnglish
Article number7153523
Pages (from-to)802-809
Number of pages8
JournalIEEE Journal of Biomedical and Health Informatics
Issue number3
Publication statusPublished - May 1 2016


  • Image-guided radiotherapy (IGRT)
  • mathematical model
  • parameter adaptation
  • radiation therapy
  • tumor growth

ASJC Scopus subject areas

  • Biotechnology
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Health Information Management


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