Objectives To provide an introduction to in silico oncology and more generally in silico medicine through the CHIC project; to outline the clinical drive, the clinical orientation and the envisaged ...clinical translation of this emergent and promising interdisciplinary domain; to introduce cancer multi-modeller multiscale hypermodels including the Oncosimulator; to outline pertinent repositories, technologies and clinically relevant scenarios aiming at treatment individualization and in silico clinical trials; to provide clinical adaptation and partial clinical validation outcomes...
The aim of this study was to examine the longitudinal impact of self-efficacy to cope with cancer on the cancer-related coping reactions of breast cancer patients and vice versa.
Data from the BOUNCE ...Project (
https://www.bounce-project.eu/
) were used to address the hypotheses. Participants (N = 403) were enrolled in the study a few weeks after surgery or biopsy. Coping self-efficacy was assessed at baseline and six months later (M6). Cancer-related coping was assessed three (M3) and nine months (M9) after baseline. The analyses were performed using structural equation modeling with Mplus 8.6.
Baseline coping self-efficacy predicted all M3 coping reactions, while M6 coping self-efficacy also predicted changes in all but one M9 coping reaction. Moreover, one of the M3 coping reactions, that is, hopelessness/helplessness, predicted the changes in M6 coping self-efficacy. The relation between coping self-efficacy and one coping reaction (i.e. cognitive avoidance) was rather weak. Stability paths from M3 to M9 coping reactions were moderate to high.
The relationship between self-efficacy to cope with cancer and cancer-related coping is complex. New theoretical models are needed to more accurately describe the diverse aspects of this association.
This study aimed to examine whether self-efficacy to cope with cancer changes over time in patients with breast cancer and whether these potential changes are similar across patients. It also aimed ...to examine whether these trajectories are related to patient psychological well-being and overall quality of life.
Participants (N = 404) from four countries (i.e. Finland, Israel, Italy, and Portugal) were enrolled in the study few weeks after breast surgery or biopsy. Self-efficacy to cope with cancer was assessed at baseline, six and 12 months later. Well-being indices were assessed at baseline, 12 and 18 months later.
Using Latent Class Growth Analysis, two groups of patients were identified. The majority of patients reported high levels of self-efficacy to cope, which increased over time. For almost 15% of the patients, however, self-efficacy declined over time. Diminishing levels of self-efficacy to cope predicted worse levels of well-being. The pattern of self-efficacy changes and their relationships to well-being was consistent across countries.
Monitoring self-efficacy to cope with cancer is probably important in order to detect alarming changes in its levels, as a declining self-efficacy to cope may serve as a signal of the need for intervention to prevent adaptation difficulties.
The role of self-efficacy to cope with breast cancer as a mediator and/or moderator in the relationship of trait resilience to quality of life and psychological symptoms was examined in this study. ...Data from the BOUNCE Project (
https://www.bounce-project.eu/
) were used. Women diagnosed with and in treatment for breast cancer (
N
= 484), from four countries, participated in the study. Trait resilience and coping self-efficacy were assessed at baseline (soon after the beginning of systemic treatment), and outcomes (quality of life, psychological symptoms) 3 months later. Hierarchical regression, mediation, moderation, and conditional (moderated) mediation and moderation analyses were performed to examine the study hypotheses. Coping self-efficacy mediated the impact of trait resilience. In addition, higher levels of resilience in combination with higher levels of coping self-efficacy were associated with better outcomes. Country of origin had no impact on these results. Overall, it seems that coping self-efficacy is a key factor that should be taken into account for research and intervention efforts in cancer.
In this paper an advanced, clinically oriented multiscale cancer model of breast tumor response to chemotherapy is presented. The paradigm of early breast cancer treated by epirubicin according to a ...branch of an actual clinical trial (the Trial of Principle, TOP trial) has been addressed. The model, stemming from previous work of the In Silico Oncology Group, National Technical University of Athens, is characterized by several crucial new features, such as the explicit distinction of proliferating cells into stem cells of infinite mitotic potential and cells of limited proliferative capacity, an advanced generic cytokinetic model and an improved tumor constitution initialization technique. A sensitivity analysis regarding critical parameters of the model has revealed their effect on the behavior of the biological system. The favorable outcome of an initial step towards the clinical adaptation and validation of the simulation model, based on the use of anonymized data from the TOP clinical trial, is presented and discussed. Two real clinical cases from the TOP trial with variable molecular profile have been simulated. A realistic time course of the tumor diameter and a reduction in tumor size in agreement with the clinical data has been achieved for both cases by selection of reasonable model parameter values, thus demonstrating a possible adaptation process of the model to real clinical trial data. Available imaging, histological, molecular and treatment data are exploited by the model in order to strengthen patient individualization modeling. The expected use of the model following thorough clinical adaptation, optimization and validation is to simulate either several candidate treatment schemes for a particular patient and support the selection of the optimal one or to simulate the expected extent of tumor shrinkage for a given time instant and decide on the adequacy or not of the simulated scheme.
Tumours behave as complex, self-organizing, opportunistic dynamic systems. In an attempt to better understand and describe the highly complicated tumour behaviour, a novel four-dimensional simulation ...model of in vivo tumour growth and response to radiotherapy has been developed. This paper presents the latest improvements to the model as well as a parametric validation of it. Improvements include an advanced algorithm leading to conformal tumour shrinkage, a quantitative consideration of the influence of oxygenation on radiosensitivity and a more realistic, imaging based description of the neovasculature distribution. The tumours selected for the validation of the model are a wild type and a mutated p53 gene glioblastomas multiforme. According to the model predictions, a whole tumour with larger cell cycle duration tends to repopulate more slowly. A lower oxygen enhancement ratio value leads to a more radiosensitive whole tumour. Higher clonogenic cell density (CCD) produces a higher number of proliferating tumour cells and, therefore, a more difficult tumour to treat. Simulation predictions agree at least semi-quantitatively with clinical experience, and particularly with the outcome of the Radiation Therapy Oncology Group (RTOG) Study 83-02. It is stressed that the model allows a quantitative study of the interrelationship between the competing influences in a complex, dynamic tumour environment. Therefore, the model can already be useful as an educational tool with which to study, understand and demonstrate the role of various parameters in tumour growth and response to irradiation. A long term quantitative clinical adaptation and validation of the model aiming at its integration into the treatment planning procedure is in progress.
Prior to an eventual clinical adaptation and validation of any clinically oriented model, a thorough study of its dynamic behavior is a sine qua non. Such a study can also elucidate aspects of the ...interplay of the involved biological mechanisms. Toward this goal, the paper focuses on an in-depth investigation of the free growth behavior of a macroscopically homogeneous malignant tumor system, using a discrete model of tumor growth. We demonstrate that when a clinical tumor grows exponentially, the following preconditions must be fulfilled: (a) time- and space-independent tumor dynamics, in terms of the transition rates among the considered cell categories and the duration of the cell cycle phases, and (b) a tumor system in a state of population equilibrium. Moreover, constant tumor dynamics during the simulation are assumed. In order to create a growing tumor, a condition that the model parameters must fulfill has been derived based on an analytical treatment of the model’s assumptions. A detailed parametric analysis of the model has been performed, in order to determine the impact and the interdependences of its parameters with focus on the free growth rate and the composition of cell population. Constraining tumor cell kinetics, toward limiting the number of possible solutions (i.e., sets of parameters) to the problem of adaptation to the real macroscopic features of a tumor, is also discussed. After completing all parametric studies and after adapting and validating the model on clinical data, it is envisaged to end up with a reliable tool for supporting clinicians in selecting the most appropriate pattern, extracted from several candidate therapeutic schemes, by exploiting tumor- and patient-specific imaging, molecular and histological data.
Image-based modeling of tumor growth combines methods from cancer simulation and medical imaging. In this context, we present a novel approach to adapt a healthy brain atlas to MR images of tumor ...patients. In order to establish correspondence between a healthy atlas and a pathologic patient image, tumor growth modeling in combination with registration algorithms is employed. In a first step, the tumor is grown in the atlas based on a new multiscale, multiphysics model including growth simulation from the cellular level up to the biomechanical level, accounting for cell proliferation and tissue deformations. Large-scale deformations are handled with an Eulerian approach for finite element computations, which can operate directly on the image voxel mesh. Subsequently, dense correspondence between the modified atlas and patient image is established using nonrigid registration. The method offers opportunities in atlas-based segmentation of tumor-bearing brain images as well as for improved patient-specific simulation and prognosis of tumor progression.
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