The goal is the development of the cervical cancer ONCOsimulator (CERONCO), able to simulate the response of cervical tumors to radiotherapy treatment (external beam radiotherapy followed by brachytherapy) in the patient-individualized context. The resulting simulator will be extensively analyzed and tested, and it will be integrated into the DR THERAPAT platform in order to enable an assessment of the modeling results based on actual clinical data.

Approach and expected outcome

CERONCO will make use of the available multi-scale (e.g. imaging, histological, clinical, treatment) data of the patient. Discrete mathematics will serve as the main mathematical tool, while the use of continuous mathematics for simulating specific aspects of cancer behavior will be explored. The modeling core developed within the EC funded projects ACGT and ContraCancrum will serve as the development basis of the model.

A “top-down” simulation approach is adopted, starting from the macroscopic imaging data of the tumor. More specifically, the tumor region (as defined based on the imaging data) will be represented in the model by a three-dimensional discretization mesh. The simulation will follow the behavior of all tumor cells residing in the mesh, which may be in various states depending on their mitotic and metabolic potential (e.g. stem, non-stem, proliferating, dormant, dead, differentiated). This will be accomplished by applying at each elementary cube of the mesh, and at each time-point, specifically formulated biological and biophysical “rules”, such as cycling through the various phases of the cell cycle, death according to the linear quadratic model, symmetric and asymmetric cell division, transition to dormancy due to hypoxia etc. The output of the Oncosimulator at any given instant will be the distribution of the tumor cells of the various categories throughout the tumor region. The modeling technique will permit the consideration of spatially-varying tumor and treatment characteristics.

The development of the simulator will be strictly driven by the available clinical data. CERONCO will be quantitatively adapted to clinical reality by exploiting the sets of real multi-scale biodata, including imaging, histological, clinical, and treatment data that will be available during the project. Extensive numerical sensitivity analyses will test the behavior of the Oncosimulator, in order for it to comply with biological and clinical reality. Both literature and clinically-based information will be used for this purpose. Modeling results will be also compared to corresponding TCP modeling predictions from WP4.

The workflow of the Oncosimulator under clinical adaptation would be as follows:

  1. Collect all available data sets (imaging, treatment, histopathological, clinical etc.) corresponding to a particular clinical case
  2. Preprocess the patient’s data in order for them to acquire the appropriate input form for the simulator. For example, the imaging data are segmented, interpolated, possibly fused, and finally reconstructed in an input “image”, representing the tumor region. In the input image, all structures or areas that it is desirable to differentiate are labeled by a pre-defined characteristic gray-level value. This is addressed by the tasks “Image processing” and “Quantitative imaging”.
  3. Adjust model parameters based on the available clinical data.
  4. Use literature-based and/or clinical experience-based information for tumor features/ or model parameters for which real clinical data are unavailable.
  5. Run the simulation and visualize the predictions.
  6. Compare simulation results with clinical reality.
  7. “Correct” model parameter values as necessary
  8. Repeat steps 5 to 7, until the simulation results are in agreement with all clinical data


A schematic representation of the procedure of clinical adaptation of CERONCO.

A schematic representation of the procedure of clinical adaptation of CERONCO.