Quantifiably differentiating clinical tumours with distinct regression profiles brings the new domain a step closer to its clinical translation
A recent paper by the In Silico Oncology and In Silico Medicine Group, Institute of Communication and Computer Systems, School of Electrical and Computer Engineering, National Technical University of Athens, published in Scientific Reports, Nature Publishing Group, reveals the great potential that in silico oncology possesses in quantifiably differentiating tumours with distinct regression profiles.
This is a crucial step towards the clinical translation of this emergent and highly promising domain.The work started within the framework of the EU funded DR THERAPAT (Digital radiation therapy patient) project. The clinical data utilized along with special radiobiological and imaging advice was provided by the Aarhus University, Department of Clinical Medicine, Denmark, whereas support with imaging data processing was provided by Philips GmbH, Hamburg, Germany, the overall Dr Therapat project coordinator. Details on the researchers involved in these important supportive actions are provided in the acknowledgments section of the paper.
The title, the authors and the abstract of the paper are as follows.
Title: “Studying the regression profiles of cervical tumours during radiotherapy treatment using a patient-specific multiscale model”
Authors: Christos A. Kyroudis, Dimitra D. Dionysiou, Eleni A. Kolokotroni and Georgios S. Stamatakos.
Abstract: Apart from offering insight into the biomechanisms involved in cancer, many recent mathematical modeling efforts aspire to the ultimate goal of clinical translation, wherein models are designed to be used in the future as clinical decision support systems in the patient-individualized context. Most significant challenges are the integration of multiscale biodata and the patient-specific model parameterization. A central aim of this study was the design of a clinically-relevant parameterization methodology for a patient-specific computational model of cervical cancer response to radiotherapy treatment with concomitant cisplatin chemotherapy, built around a tumour features-based search of the parameter space. Additionally, a methodological framework for the predictive use of the model was designed, including a scoring method to quantitatively refect the similarity and bilateral predictive ability of any two tumours in terms of their regression profile. The methodology was applied to the datasets of eight patients. Tumour scenarios in accordance with the available longitudinal data have been determined. Predictive investigations identifed three patient cases, anyone of which can be used to predict the volumetric evolution throughout therapy of the tumours of the other two with very good results. Our observations show that the presented approach is promising in quantifiably differentiating tumours with distinct regression profiles.
For more information, the full paper is available under the Open Access scheme at https://doi.org/10.1038/s41598-018-37155-9