Comparison between model‐predicted tumor oxygenation dynamics and vascular‐/flow‐related Doppler indices

Abstract

The results suggest that the modeled relation between tumor evolution and oxygen dynamics was reasonable enough to provide realistic oxygenation curves in five cases (correlation greater than 50%) out of seven. In case of nonresponsive tumors, the model failed in predicting the oxygenation trend while succeeded in reproducing the average oxygenation value according to the mean vascularization-flow index. Despite the need for deeper investigations, the outcomes of the present work support the hypothesis that a simple macroscale model of tumor response to radiotherapy is able to predict the tumor oxygenation. The possibility of an objective and quantitative validation on imaging data discloses the possibility to translate them as decision support tools in clinical practice and to move a step forward in the treatment personalization.

Model-Supported Radiotherapy Personalization: In silico Test of Hyper- and Hypo-Fractionation Effects

“…First of all, we noticed that the same treatment can behave very differently depending on the patient characteristics, even if it was expected, given that the data used for the training itself showed large variability even in the final regression (20–90%) ( Belfatto et al, 2017 ). For example, the hyperfractionation ( d = 1) leads to a reduction of less than 30% for patient A and more than 99% for patient D (see Table 2 ).…”

“…Free parameters underwent patient-specific optimization by means of a custom Montecarlo algorithm implemented in MATLAB ® (The MathWorks, Inc., Natick, MA, United States). The growth rate was searched in the interval 0.01 < ρ < 0.2, the initial carrying capacity was allowed to span between 1 and 3 times the initial tumor volume (100 < < 300), while the dead cells clearance time ( T 1/2 ) was bounded between 2 and 30 days ( Belfatto et al, 2017 ). Finally, γ, determining SF v , was allowed to range in the [0 .. 1] interval.…”

“…This was set to limit the lethal effects of irradiation to the vasculature according to the rationale of radiotherapy that healthy cells are less sensitive than the tumor ones. The model was optimized according to the methodology described in Belfatto et al (2017) using the tumor volume evolution ( V t ) of seven woman (age range: 41–81 years), listed from A to G, affected by uterine cervical cancer. Details of the estimated parameters are depicted in Table 1 .…”

“…In previous studies, we presented different macroscale models of tumor growth and response to radiotherapy, trained and tested on both animal and clinical data ( Belfatto et al, 2015 , 2016a , b , 2017 ). In this work, we aimed at simulating the effects of hyper- and hypo-fractionation schemes together with the conventional schedule using the most complete of the proposed model formulations ( Belfatto et al, 2017 ). The adopted model, including active and necrotic tumor cells and oxygenation dynamics, had already been trained on the volume data of seven patients, diagnosed with cervical cancer, who were treated using the standard 1.8G/28-fraction EBRT regimen and monitored using 3D-Doppler images.…”

Modelling the Tumour Microenvironment, but What Exactly Do We Mean by “Model”?

“…The scale, or point of view, of a model, provides different resolutions at which the model operates: at an organ scale, they are considered macroscale models [105] and, at the cell level, they are considered microscale models [106]. Intermediate scales are sometimes referred as mesoscale models [107] and are related to mesoscale imaging [108], which aims to link the information of cells, organs and tissues.…”