A graphical LASSO analysis of global quality of life, sub scales of the EORTC QLQ-C30 instrument and depression in early breast cancer

We aimed to (a) investigate the interplay between depression, symptoms and level of functioning, and (b) understand the paths through which they influence health related quality of life (QOL) during the first year of rehabilitation period of early breast cancer. A network analysis method was used. The population consisted of 487 women aged 35-68 years, who had recently completed adjuvant chemotherapy or started endocrine therapy for early breast cancer. At baseline and at the first year from randomization QOL, symptomatology and functioning by the EORTC QLQ-C30 and BR-23 questionnaires, and depression by the Finnish version of Beck's 13-item depression scale, were collected. The multivariate interplay between the related scales was analysed via regularized partial correlation networks (graphical LASSO). The median global quality of life (gQoL) at baseline was 69.9 +/- 19.0 (16.7-100) and improved to 74.9 +/- 19.0 (0-100) after 1 year. Scales related to mental health (emotional functioning, cognitive functioning, depression, insomnia, body image, future perspective) were clustered together at both time points. Fatigue was mediated through a different route, having the strongest connection with physical functioning and no direct connection with depression. Multiple paths existed connecting symptoms and functioning types with gQoL. Factors with the strongest connections to gQoL included: social functioning, depression and fatigue at baseline; emotional functioning and fatigue at month 12. Overall, the most important nodes were depression, gQoL and fatigue. The graphical LASSO network analysis revealed that scales related to fatigue and emotional health had the strongest associations to the EORTC QLQ-C30 gQoL score. When we plan interventions for patients with impaired QOL it is important to consider both psychological support and interventions that improve fatigue and physical function like exercise.Peer reviewe

A Multidisciplinary Hyper-Modeling Scheme in Personalized In Silico Oncology: Coupling Cell Kinetics with Metabolism, Signaling Networks, and Biomechanics as Plug-In Component Models of a Cancer Digital Twin.

The massive amount of human biological, imaging, and clinical data produced by multiple and diverse sources necessitates integrative modeling approaches able to summarize all this information into answers to specific clinical questions. In this paper, we present a hypermodeling scheme able to combine models of diverse cancer aspects regardless of their underlying method or scale. Describing tissue-scale cancer cell proliferation, biomechanical tumor growth, nutrient transport, genomic-scale aberrant cancer cell metabolism, and cell-signaling pathways that regulate the cellular response to therapy, the hypermodel integrates mutation, miRNA expression, imaging, and clinical data. The constituting hypomodels, as well as their orchestration and links, are described. Two specific cancer types, Wilms tumor (nephroblastoma) and non-small cell lung cancer, are addressed as proof-of-concept study cases. Personalized simulations of the actual anatomy of a patient have been conducted. The hypermodel has also been applied to predict tumor control after radiotherapy and the relationship between tumor proliferative activity and response to neoadjuvant chemotherapy. Our innovative hypermodel holds promise as a digital twin-based clinical decision support system and as the core of future in silico trial platforms, although additional retrospective adaptation and validation are necessary

Trajectories of Quality of Life among an International Sample of Women during the First Year after the Diagnosis of Early Breast Cancer: A Latent Growth Curve Analysis

The current study aimed to track the trajectory of quality of life (QoL) among subgroups of women with breast cancer in the first 12 months post-diagnosis. We also aimed to assess the number and portion of women classified into each distinct trajectory and the sociodemographic, clinical, and psychosocial factors associated with these trajectories. The international sample included 699 participants who were recruited soon after being diagnosed with breast cancer as part of the BOUNCE Project. QoL was assessed at baseline and after 3, 6, 9, and 12 months, and we used Latent Class Growth Analysis to identify trajectory subgroups. Sociodemographic, clinical, and psychosocial factors at baseline were used to predict latent class membership. Four distinct QoL trajectories were identified in the first 12 months after a breast cancer diagnosis: medium and stable (26% of participants); medium and improving (47%); high and improving (18%); and low and stable (9%). Thus, most women experienced improvements in QoL during the first year post-diagnosis. However, approximately one-third of women experienced consistently low-to-medium QoL. Cancer stage was the only variable which was related to the QoL trajectory in the multivariate analysis. Early interventions which specifically target women who are at risk of ongoing low QoL are needed

A graphical LASSO analysis of global quality of life, sub scales of the EORTC QLQ-C30 instrument and depression in early breast cancer

We aimed to (a) investigate the interplay between depression, symptoms and level of functioning, and (b) understand the paths through which they influence health related quality of life (QOL) during the first year of rehabilitation period of early breast cancer. A network analysis method was used. The population consisted of 487 women aged 35-68 years, who had recently completed adjuvant chemotherapy or started endocrine therapy for early breast cancer. At baseline and at the first year from randomization QOL, symptomatology and functioning by the EORTC QLQ-C30 and BR-23 questionnaires, and depression by the Finnish version of Beck's 13-item depression scale, were collected. The multivariate interplay between the related scales was analysed via regularized partial correlation networks (graphical LASSO). The median global quality of life (gQoL) at baseline was 69.9 +/- 19.0 (16.7-100) and improved to 74.9 +/- 19.0 (0-100) after 1 year. Scales related to mental health (emotional functioning, cognitive functioning, depression, insomnia, body image, future perspective) were clustered together at both time points. Fatigue was mediated through a different route, having the strongest connection with physical functioning and no direct connection with depression. Multiple paths existed connecting symptoms and functioning types with gQoL. Factors with the strongest connections to gQoL included: social functioning, depression and fatigue at baseline; emotional functioning and fatigue at month 12. Overall, the most important nodes were depression, gQoL and fatigue. The graphical LASSO network analysis revealed that scales related to fatigue and emotional health had the strongest associations to the EORTC QLQ-C30 gQoL score. When we plan interventions for patients with impaired QOL it is important to consider both psychological support and interventions that improve fatigue and physical function like exercise

Associations between Physical Exercise, Quality of Life, Psychological Symptoms and Treatment Side Effects in Early Breast Cancer

Peer reviewe

The Development of an Empathy Curriculum (Empathy in Health) for Healthcare Students Using VR Technology

Empathy in Health is an Erasmus + funded project, which aims to design a curriculum for empathetic skill development in healthcare practitioners and home carers based on up-to-date evidence and cutting-edge technology tools. A literature review was carried out that focused on empathy in health care using VR technology. The results of the literature review helped develop a focus group guide for the purposes of the qualitative part of the need assessment exercise. The data from the focus groups were transcribed and analysed using the methodology of content analysis. The themes that emerged from the analysis of the focus groups’ data lent themselves to three major working areas. These informed the development of the qualification framework, which in turn informed the development of the detailed curriculum. The Empathy in Health curriculum involves 21-hour classroom teaching, 3-hour Asynchronous Electronic Learning and 6-hour Directed Self Learning for graduate students or final year undergraduate students or Health Care Professionals. The curriculum covers understanding empathy and competencies necessary for empathy, understanding empathy in relationships and information exchanges in different health care contexts/environments, showing empathy in diverse environments and overcoming barriers/challenges to empathy

A multidisciplinary hyper-modeling scheme in personalized in silico oncology : coupling cell kinetics with metabolism, signaling networks, and biomechanics as plug-in component models of a cancer digital twin

The massive amount of human biological, imaging, and clinical data produced by multiple and diverse sources necessitates integrative modeling approaches able to summarize all this information into answers to specific clinical questions. In this paper, we present a hypermodeling scheme able to combine models of diverse cancer aspects regardless of their underlying method or scale. Describing tissue-scale cancer cell proliferation, biomechanical tumor growth, nutrient transport, genomic-scale aberrant cancer cell metabolism, and cell-signaling pathways that regulate the cellular response to therapy, the hypermodel integrates mutation, miRNA expression, imaging, and clinical data. The constituting hypomodels, as well as their orchestration and links, are described. Two specific cancer types, Wilms tumor (nephroblastoma) and non-small cell lung cancer, are addressed as proof-of-concept study cases. Personalized simulations of the actual anatomy of a patient have been conducted. The hypermodel has also been applied to predict tumor control after radiotherapy and the relationship between tumor proliferative activity and response to neoadjuvant chemotherapy. Our innovative hypermodel holds promise as a digital twin-based clinical decision support system and as the core of future in silico trial platforms, although additional retrospective adaptation and validation are necessary

Predicting Effective Adaptation to Breast Cancer to Help Women BOUNCE Back : Protocol for a Multicenter Clinical Pilot Study

Publisher Copyright: © 2022 Greta Pettini.Background: Despite the continued progress of medicine, dealing with breast cancer is becoming a major socioeconomic challenge, particularly due to its increasing incidence. The ability to better manage and adapt to the entire care process depends not only on the type of cancer but also on the patient's sociodemographic and psychological characteristics as well as on the social environment in which a person lives and interacts. Therefore, it is important to understand which factors may contribute to successful adaptation to breast cancer. To our knowledge, no studies have been performed on the combination effect of multiple psychological, biological, and functional variables in predicting the patient's ability to bounce back from a stressful life event,such as a breast cancer diagnosis. Here we describe the study protocol of a multicenter clinical study entitled "Predicting Effective Adaptation to Breast Cancer to Help Women to BOUNCE Back"or, in short, BOUNCE. Objective: The aim of the study is to build a quantitative mathematical model of factors associated with the capacity for optimal adjustment to cancer and to study resilience through the cancer continuum in a population of patients with breast cancer. Methods: A total of 660 women with breast cancer will be recruited from five European cancer centers in Italy, Finland, Israel, and Portugal. Biomedical and psychosocial variables will be collected using the Noona Healthcare platform. Psychosocial, sociodemographic, lifestyle, and clinical variables will be measured every 3 months, starting from presurgery assessment (ie, baseline) to 18 months after surgery. Temporal data mining, time-series prediction, sequence classification methods, clustering time-series data, and temporal association rules will be used to develop the predictive model. Results: The recruitment process stared in January 2019 and ended in November 2021. Preliminary results have been published in a scientific journal and are available for consultation on the BOUNCE project website. Data analysis and dissemination of the study results will be performed in 2022. Conclusions: This study will develop a predictive model that is able to describe individual resilience and identify different resilience trajectories along the care process. The results will allow the implementation of tailored interventions according to patients' needs, supported by eHealth technologies.Peer reviewe

Exploiting Clinical Trial Data Drastically Narrows the Window of Possible Solutions to the Problem of Clinical Adaptation of a Multiscale Cancer Model

The development of computational models for simulating tumor growth and response to treatment has gained significant momentum during the last few decades. At the dawn of the era of personalized medicine, providing insight into complex mechanisms involved in cancer and contributing to patient-specific therapy optimization constitute particularly inspiring pursuits. The in silico oncology community is facing the great challenge of effectively translating simulation models into clinical practice, which presupposes a thorough sensitivity analysis, adaptation and validation process based on real clinical data. In this paper, the behavior of a clinically-oriented, multiscale model of solid tumor response to chemotherapy is investigated, using the paradigm of nephroblastoma response to preoperative chemotherapy in the context of the SIOP/GPOH clinical trial. A sorting of the model's parameters according to the magnitude of their effect on the output has unveiled the relative importance of the corresponding biological mechanisms; major impact on the result of therapy is credited to the oxygenation and nutrient availability status of the tumor and the balance between the symmetric and asymmetric modes of stem cell division. The effect of a number of parameter combinations on the extent of chemotherapy-induced tumor shrinkage and on the tumor's growth rate are discussed. A real clinical case of nephroblastoma has served as a proof of principle study case, demonstrating the basics of an ongoing clinical adaptation and validation process. By using clinical data in conjunction with plausible values of model parameters, an excellent fit of the model to the available medical data of the selected nephroblastoma case has been achieved, in terms of both volume reduction and histological constitution of the tumor. In this context, the exploitation of multiscale clinical data drastically narrows the window of possible solutions to the clinical adaptation problem

In silico simulations of diagnostic and therapeutic techniques concerning normal and pathological cell systems

In the present thesis, a clinically oriented, multiscale, discrete simulation modelof cancer free growth and response to chemotherapy and/or radiotherapy is presentedand investigated. Two versions of the model have been implemented: the spatial andthe non spatial approach. The spatial model concerns the spatiotemporal evolution ofsolid tumours, whereas the non spatial model can be applied in the case of non solidcancers, as well as solid tumours, when no emphasis is put on the spatial features of atumour evolution. The research work has been focused on the paradigms of earlybreast cancer treated with the single agent epirubicin, primary lung cancer treated withvarious combinations of cisplatin, gemcitabine, vinorelbin and docetaxel andglioblastoma multiforme treated with combined modality treatment using radiationand chemotherapy with temozolomide. The goal is to end up with a reliablesimulation system able to assist clinicians in selecting the most appropriatetherapeutic pattern, extracted from several candidate therapeutic schemes in thecontext of patient individualized treatment optimization.The model incorporates the biological mechanisms of cell cycling, quiescence,recruitment (reentry into the cell cycle), differentiation and death. It is based on thewell documented assumption that tumour sustenance is due to the existence of cancerstem cells, i.e. cells which have the ability to preserve their own population, as well asgive birth to cells that follow the path towards terminal differentiation. Furthermore,the mechanism of action, pharmacokinetics and pharmacodynamics of all consideredagents have been bibliographically studied and incorporated into the model. Finally,the model has been developed to support and incorporate individualized clinical datasuch as imaging data (e.g. CT, MRI, PET slices, possibly fused), including thedefinition of the tumour contour and internal tumour regions (proliferating, necrotic),histopathologic (e.g., type of tumour) and genetic data (e.g., gene expression).An exhaustive and in-depth examination of the model behaviour with respect tothe variation of its input parameters has been performed, in order to determine theimpact of its parameters, guarantee a biologically relevant virtual tumour behaviourand enlighten aspects of the interplay and possible interdependencies of the biologicalmechanisms modeled. Finally, the model has been quantitativily validated and adaptated in the framework of the ACGT (Advancing Clinicogenomic Trials onCancer, FP6-2005-IST-026996), ContraCancrum (Clinically Oriented CancerMultilevel Modelling, FP7-ICT-2007-2-223979) and P-medicine (From data sharingand integration via VPH models to Personalized medicine, FP7-ICT-2009-6-270089)European Commission-funded projects by exploiting real clinical data. In the presentthesis, the clinical adaptation of the model focuses on breast cancer, lung cancer andglioblastoma multiforme clinical cases. Moreover, various versions of the model havebeen uploaded to the EU cancer model repository developed by the TUMOR(Transatlantic Tumour Model Repositories, FP7-ICT-2009-5-247754) EuropeanCommission-funded project. The model has been developed in the C++ programminglanguage.Η διατριβή αφορά την ανάπτυξη και υλοποίηση ενός τετραδιάστατου, διακριτούμοντέλου προσομοίωσης της συμπεριφοράς καρκινικών κυτταρικών συστημάτων σεελεύθερη ανάπτυξη και της απόκρισής τους σε χημειοθεραπευτική ή καιακτινοθεραπευτική αγωγή. Υλοποιήθηκαν δύο εκδοχές του μοντέλου: η χωρική και ημη χωρική προσέγγιση. Η χωρική προσέγγιση αναφέρεται στην τετραδιάστατηπροσομοίωση συμπαγών όγκων. Η μη χωρική προσέγγιση βρίσκει εφαρμογή στηνπερίπτωση μη συμπαγών όγκων, καθώς και συμπαγών όγκων, όταν δεν δίνεταιέμφαση στη χωρική εξέλιξή τους. Η ερευνητική εργασία έχει επικεντρωθεί σε τρειςτύπους καρκινικών όγκων: καρκίνος του μαστού, καρκίνος του πνεύμονα καιπολύμορφο γλοιοβλάστωμα και σε θεραπευτικά σχήματα χορήγησης τωνσκευασμάτων: επιρουβικίνη (epirubicin), τεμοζολομίδη (temozolomide), σισπλατίνη(cisplatin), γεμσιταμπίνη (gemcitabine), βινορελμπίνη (vinorelbine) και δοσεταξέλη(docetaxel). Σκοπός της εργασίας είναι η ανάπτυξη ενός εργαλείου για την αξιόπιστηυποστήριξη ιατρών στη λήψη αποφάσεων σχετικά με την επιλογή θεραπευτικώνσχημάτων και την εξατομικευμένη βελτιστοποίηση της θεραπευτικής αγωγής.Η αφετηρία είναι η μοντελοποίηση του κυτταρικού κύκλου και των πιθανώνμεταβάσεων μεταξύ των καταστάσεων που μπορεί να βρεθεί ένα κύτταρο. Τομοντέλο βασίζεται στην υπόθεση ότι ο καρκινικός όγκος διατηρείται από μιασυγκεκριμένη κατηγορία κυττάρων, τα καρκινικά βλαστικά κύτταρα (cancer stemcells), και έχει επεκταθεί ώστε να περιλαμβάνει σε μεγαλύτερη λεπτομέρειαδιάφορους βιολογικούς μηχανισμούς σε μοριακό (πχ. εκφράσεις γονιδίων) καικυτταρικό επίπεδο. Ο μηχανισμός δράσης, η φαρμακοκινητική και ηφαρμακοδυναμική των θεωρούμενων σκευασμάτων έχουν μελετηθεί βιβλιογραφικάκαι έχουν ενσωματωθεί στο μοντέλο. Επίσης, το μοντέλο έχει αναπτυχθεί ώστε ναλαμβάνει υπόψη του την κλινική εικόνα του ασθενούς με χρήση εξατομικευμένωνκλινικών δεδομένων, όπως απεικονιστικά δεδομένα (π.χ. CT, MRI, PET),ιστοπαθολογικά δεδομένα (π.χ. τύπος όγκου, βαθμός διαφοροποίησης) και μοριακάδεδομένα (π.χ. έκφραση γονιδίων).Στα πλαίσια της διατριβής πραγματοποιούνται έλεγχοι αξιοπιστίας και εκτενείςπαραμετρικές μελέτες για την αποσαφήνιση της ευαισθησίας του μοντέλου στη διακύμανση των παραμέτρων του τόσο κατά την προσομοίωση της ελεύθερηςανάπτυξης όσο και κατά την εφαρμογή της χημειοθεραπευτικής αγωγής. Η ποσοτικήαξιολόγηση, προσαρμογή και βελτιστοποίηση του μοντέλου πραγματοποιείται σταπλαίσια των ευρωπαϊκών ερευνητικών προγραμμάτων ACGT (AdvancingClinicogenomic Trials on Cancer, FP6-2005-IST-026996), ContraCancrum(Clinically Oriented Cancer Multilevel Modelling, FP7-ICT-2007-2-223979) και Pmedicine(From data sharing and integration via VPH models to Personalizedmedicine, FP7-ICT-2009-6-270089) μέσω της αξιοποίησης πραγματικών κλινικώνδεδομένων. Στην παρούσα διατριβή παρουσιάζονται τα αποτελέσματα τηςπροσαρμογής του μοντέλου σε κλινικά δεδομένα του καρκίνου του μαστού, τουκαρκίνου του πνεύμονα και του πολύμορφου γλοιοβλαστώματος. Επιπλέον, διάφορεςεκδόσεις του μοντέλου έχουν αξιοποιηθεί για ‘την επάνδρωση’ μιας ευρωπαϊκήςβάσης μοντέλων για τον καρκίνο, που υλοποιείται στα πλαίσια του ευρωπαϊκούερευνητικού προγράμματος TUMOR (Transatlantic Tumour Model Repositories,FP7-ICT-2009-5-247754). Το μοντέλο υλοποιείται σε γλώσσα προγραμματισμούC++