Kinetic Models for Predicting Cervical Cancer Response to Radiation Therapy on Individual Basis Using Tumor Regression Measured In Vivo With Volumetric Imaging. (February 2016)

• Record Type: Journal Article
• Authors: Belfatto, Antonella; Riboldi, Marco; Ciardo, Delia; Cattani, Federica; Cecconi, Agnese; Lazzari, Roberta; Jereczek-Fossa, Barbara Alicja; Orecchia, Roberto; Baroni, Guido; Cerveri, Pietro
• Is Part Of: Technology in cancer research & treatment. Volume 15:Number 1(2016:Feb.)
• Page Start: 146
• Page End: 158
• Publication Date: 2016-02
• Subjects: tumor mathematical modeling -- uterine cervical cancer -- radiosensitivity -- linear-quadratic model -- image-guided radiotherapy; Oncology -- Periodicals; Cancer -- Diagnosis -- Periodicals; Cancer -- Treatment -- Technological innovations -- Periodicals; 616.994
• DOI: 10.1177/1533034615573796
• Languages: English
• ISSNs: 1533-0346

Abstract

This article describes a macroscopic mathematical modeling approach to capture the interplay between solid tumor evolution and cell damage during radiotherapy. Volume regression profiles of 15 patients with uterine cervical cancer were reconstructed from serial cone-beam computed tomography data sets, acquired for image-guided radiotherapy, and used for model parameter learning by means of a genetic-based optimization. Patients, diagnosed with either squamous cell carcinoma or adenocarcinoma, underwent different treatment modalities (image-guided radiotherapy and image-guided chemo-radiotherapy). The mean volume at the beginning of radiotherapy and the end of radiotherapy was on average 23.7 cm 3 (range: 12.7-44.4 cm 3 ) and 8.6 cm 3 (range: 3.6-17.1 cm 3 ), respectively. Two different tumor dynamics were taken into account in the model: the viable (active) and the necrotic cancer cells. However, according to the results of a preliminary volume regression analysis, we assumed a short dead cell resolving time and the model was simplified to the active tumor volume. Model learning was performed both on the complete patient cohort (cohort-based model learning) and on each single patient (patient-specific model learning). The fitting results (mean error: ∼16% and ∼6% for the cohort-based model and patient-specific model, respectively) highlighted the model ability to quantitatively reproduce tumor regression. Volume prediction errors of about 18% on average were obtained using cohort-based model computed on all but 1 patient at a time (leave-one-out technique). Finally, a sensitivity analysis was performed and the data uncertainty effects evaluated by simulating an average volume perturbation of about 1.5 cm 3 obtaining an error increase within 0.2%. In conclusion, we showed that simple time-continuous models can represent tumor regression curves both on a patient cohort and patient-specific basis; this discloses the opportunity in the future to exploit such models to predict how changes in the treatment schedule (number of fractions, doses, intervals among fractions) might affect the tumor regression on an individual basis.