MI-Common Data Model: Extending Observational Medical Outcomes Partnership-Common Data Model (OMOP-CDM) for Registering Medical Imaging Metadata and Subsequent Curation Processes.

PURPOSE: The explosion of big data and artificial intelligence has rapidly increased the need for integrated, homogenized, and harmonized health data. Many common data models (CDMs) and standard vocabularies have appeared in an attempt to offer harmonized access to the available information, with Observational Medical Outcomes Partnership (OMOP)-CDM being one of the most prominent ones, allowing the standardization and harmonization of health care information. However, despite its flexibility, still capturing imaging metadata along with the corresponding clinical data continues to pose a challenge. This challenge arises from the absence of a comprehensive standard representation for image-related information and subsequent image curation processes and their interlinkage with the respective clinical information. Successful resolution of this challenge holds the potential to enable imaging and clinical data to become harmonized, quality-checked, annotated, and ready to be used in conjunction, in the development of artificial intelligence models and other data-dependent use cases. METHODS: To address this challenge, we introduce medical imaging (MI)-CDM-an extension of the OMOP-CDM specifically designed for registering medical imaging data and curation-related processes. Our modeling choices were the result of iterative numerous discussions among clinical and AI experts to enable the integration of imaging and clinical data in the context of the ProCAncer-I project, for answering a set of clinical questions across the prostate cancer's continuum. RESULTS: Our MI-CDM extension has been successfully implemented for the use case of prostate cancer for integrating imaging and curation metadata along with clinical information by using the OMOP-CDM and its oncology extension. CONCLUSION: By using our proposed terminologies and standardized attributes, we demonstrate how diverse imaging modalities can be seamlessly integrated in the future.

Data infrastructures for AI in medical imaging: a report on the experiences of five EU projects.

Artificial intelligence (AI) is transforming the field of medical imaging and has the potential to bring medicine from the era of 'sick-care' to the era of healthcare and prevention. The development of AI requires access to large, complete, and harmonized real-world datasets, representative of the population, and disease diversity. However, to date, efforts are fragmented, based on single-institution, size-limited, and annotation-limited datasets. Available public datasets (e.g., The Cancer Imaging Archive, TCIA, USA) are limited in scope, making model generalizability really difficult. In this direction, five European Union projects are currently working on the development of big data infrastructures that will enable European, ethically and General Data Protection Regulation-compliant, quality-controlled, cancer-related, medical imaging platforms, in which both large-scale data and AI algorithms will coexist. The vision is to create sustainable AI cloud-based platforms for the development, implementation, verification, and validation of trustable, usable, and reliable AI models for addressing specific unmet needs regarding cancer care provision. In this paper, we present an overview of the development efforts highlighting challenges and approaches selected providing valuable feedback to future attempts in the area.Key points• Artificial intelligence models for health imaging require access to large amounts of harmonized imaging data and metadata.• Main infrastructures adopted either collect centrally anonymized data or enable access to pseudonymized distributed data.• Developing a common data model for storing all relevant information is a challenge.• Trust of data providers in data sharing initiatives is essential.• An online European Union meta-tool-repository is a necessity minimizing effort duplication for the various projects in the area.

Synergies of Radiomics and Transcriptomics in Lung Cancer Diagnosis: A Pilot Study.

Radiotranscriptomics is an emerging field that aims to investigate the relationships between the radiomic features extracted from medical images and gene expression profiles that contribute in the diagnosis, treatment planning, and prognosis of cancer. This study proposes a methodological framework for the investigation of these associations with application on non-small-cell lung cancer (NSCLC). Six publicly available NSCLC datasets with transcriptomics data were used to derive and validate a transcriptomic signature for its ability to differentiate between cancer and non-malignant lung tissue. A publicly available dataset of 24 NSCLC-diagnosed patients, with both transcriptomic and imaging data, was used for the joint radiotranscriptomic analysis. For each patient, 749 Computed Tomography (CT) radiomic features were extracted and the corresponding transcriptomics data were provided through DNA microarrays. The radiomic features were clustered using the iterative K-means algorithm resulting in 77 homogeneous clusters, represented by meta-radiomic features. The most significant differentially expressed genes (DEGs) were selected by performing Significance Analysis of Microarrays (SAM) and 2-fold change. The interactions among the CT imaging features and the selected DEGs were investigated using SAM and a Spearman rank correlation test with a False Discovery Rate (FDR) of 5%, leading to the extraction of 73 DEGs significantly correlated with radiomic features. These genes were used to produce predictive models of the meta-radiomics features, defined as p-metaomics features, by performing Lasso regression. Of the 77 meta-radiomic features, 51 can be modeled in terms of the transcriptomic signature. These significant radiotranscriptomics relationships form a reliable basis to biologically justify the radiomics features extracted from anatomic imaging modalities. Thus, the biological value of these radiomic features was justified via enrichment analysis on their transcriptomics-based regression models, revealing closely associated biological processes and pathways. Overall, the proposed methodological framework provides joint radiotranscriptomics markers and models to support the connection and complementarities between the transcriptome and the phenotype in cancer, as demonstrated in the case of NSCLC.

Data Ingestion for AI in Prostate Cancer.

Prostate cancer (PCa) is one of the most prevalent cancers in the male population. Current clinical practices lead to overdiagnosis and overtreatment necessitating more effective tools for improving diagnosis, thus the quality of life of patients. Recent advances in infrastructure, computing power and artificial intelligence enable the collection of tremendous amounts of clinical and imaging data that could assist towards this end. ProCAncer-I project aims to develop an AI platform integrating imaging data and models and hosting the largest collection of PCa (mp)MRI, anonymized image data worldwide. In this paper, we present an overview of the overall architecture focusing on the data ingestion part of the platform. We describe the workflow followed for uploading the data and the main repositories for storing imaging data, clinical data and their corresponding metadata.

PANACEA resilient and secure toolkit for healthcare infrastructures.

Healthcare organizations are frequently subject to cybersecurity incidents. The outbreak of a pandemic such as COVID-19 has shown the need for specific operational and organizational measures to be in place in order to reduce the risk of successful cyberattacks. Time will be key: preparation is needed to ensure quick secure set-up of additional resources (IT, staff, medical devices) when the next emergency will hit. The PANACEA Solution Toolkit is a suite of complementary tools to provide Health Care Organizations (HCO) with assessment, guidance, technical and organizational "infrastructure" to address the cybersecurity challenges. It provides support for fortifying health organizations against cyber threats on multiple different levels (technical, behavioral, organizational, strategical) and across a diverse set of workflows and scenarios. In order to determine whether the toolkit satisfies the specific business and users' requirements in the selected use cases, a detailed validation plan and execution roadmap is established taking into account the constraints of the current emergent situation.

Emerging and Established Trends to Support Secure Health Information Exchange.

This work aims to provide information, guidelines, established practices and standards, and an extensive evaluation on new and promising technologies for the implementation of a secure information sharing platform for health-related data. We focus strictly on the technical aspects and specifically on the sharing of health information, studying innovative techniques for secure information sharing within the health-care domain, and we describe our solution and evaluate the use of blockchain methodologically for integrating within our implementation. To do so, we analyze health information sharing within the concept of the PANACEA project that facilitates the design, implementation, and deployment of a relevant platform. The research presented in this paper provides evidence and argumentation toward advanced and novel implementation strategies for a state-of-the-art information sharing environment; a description of high-level requirements for the transfer of data between different health-care organizations or cross-border; technologies to support the secure interconnectivity and trust between information technology (IT) systems participating in a sharing-data "community"; standards, guidelines, and interoperability specifications for implementing a common understanding and integration in the sharing of clinical information; and the use of cloud computing and prospectively more advanced technologies such as blockchain. The technologies described and the possible implementation approaches are presented in the design of an innovative secure information sharing platform in the health-care domain.

Cyber-attacks and threats for healthcare - a multi-layer thread analysis.

Due to the advent of novel technologies and digital opportunities allowing to simplify user lives, healthcare is increasingly evolving towards digitalization. This represent a great opportunity on one side but it also exposes healthcare organizations to multiple threats (both digital and not) that may lead an attacker to compromise the security of medial processes and potentially patients' safety. Today technical cybersecurity countermeasures are used to protect the confidentiality, integrity and availability of data and information systems - especially in the healthcare domain. This paper will report on the current state of the art about cyber security in the Healthcare domain with particular emphasis on current threats and methodologies to analyze and manage them. In addition, it will introduce a multi-layer attack model providing a new perspective for attack and threat identification and analysis.

CXCR4 and JUNB double-positive disseminated tumor cells are detected frequently in breast cancer patients at primary diagnosis.

BACKGROUND: The chemokine receptor CXCR4 and the transcription factor JUNB, expressed on a variety of tumor cells, seem to play an important role in the metastatic process. Since disseminated tumor cells (DTCs) in the bone marrow (BM) have been associated with worse outcomes, we evaluated the expression of CXCR4 and JUNB in DTCs of primary, nonmetastatic breast cancer (BC) patients before the onset of any systemic treatment. METHODS: Bilateral BM (10 ml) aspirations of 39 hormone receptor (HR)-positive, HER2-negative BC patients were assessed for the presence of DTCs using the following combination of antibodies: pan-cytokeratin (A45-B/B3)/CXCR4/JUNB. An expression pattern of the examined proteins was created using confocal laser scanning microscopy, Image J software and BC cell lines. RESULTS: CXCR4 was overexpressed in cancer cells and DTCs, with the following hierarchy of expression: SKBR3 > MCF7 > DTCs > MDA-MB231. Accordingly, the expression pattern of JUNB was: DTCs > MDA-MB231 > SKBR3 > MCF7. The mean intensity of CXCR4 (6411 ± 334) and JUNB (27725.64 ± 470) in DTCs was statistically higher compared with BM hematopoietic cells (2009 ± 456, p = 0.001; and 11112.89 ± 545, p = 0.001, respectively). The (CXCR4+JUNB+CK+) phenotype was the most frequently detected [90% (35/39)], followed by the (CXCR4-JUNB+CK+) phenotype [36% (14/39)]. However, (CXCR4+JUNB-CK+) tumor cells were found in only 5% (3/39) of patients. Those patients harboring DTCs with the (CXCR4+JUNB+CK+) phenotype revealed lower overall survival (Cox regression: p = 0.023). CONCLUSIONS: (CXCR4+JUNB+CK+)-expressing DTCs, detected frequently in the BM of BC patients, seem to identify a subgroup of patients at higher risk for relapse that may be considered for close follow up.

PharmActa: Personalized pharmaceutical care eHealth platform for patients and pharmacists.

Community pharmacists are critically placed in the patient care chain being an extended frontline within primary healthcare networks across Europe. They are trained to ensure safe and effective medication use, a crucial and responsible role, extending beyond the common misconception limited to just providing timely access to medicines for the population. Technology-wise, eHealth being committed to an effective, networked, patient-centered and accessible healthcare would prove a real asset in this direction by achieving improved therapy adherence with better outcomes and direct contribution to a cost-effective healthcare system. In this work, we present PharmActa, a personalized eHealth platform that addresses key features of pharmaceutical care and enhances communication of pharmacists with patients for optimizing pharmacotherapy. PharmActa empowers patients by providing pharmaceutical care services, such as drug interactions tools, reminders for assisting adhesion and compliance, information regarding adverse drug reactions, as well as pharmacovigilance along with related tools for healthcare management. In addition, it allows the pharmacists to review the medication history in order to provide personalized pharmaceutical care services; thus enhancing their role as healthcare providers. Finally, a mechanism allowing such a system to be interconnected with a developed medical repository following European and International interoperability standards, is also presented. Thus far, the evaluation results presented in this work indicate that PharmActa can be of great benefit to healthcare professionals, especially pharmacists and patients.

The prognostic value of JUNB-positive CTCs in metastatic breast cancer: from bioinformatics to phenotypic characterization.

BACKGROUND: Circulating tumor cells (CTCs) are important for metastatic dissemination of cancer. They can provide useful information, regarding biological features and tumor heterogeneity; however, their detection and characterization are difficult due to their limited number in the bloodstream and their mesenchymal characteristics. Therefore, new biomarkers are needed to address these questions. METHODS: Bioinformatics functional enrichment analysis revealed a subgroup of 24 genes, potentially overexpressed in CTCs. Among these genes, the chemokine receptor CXCR4 plays a central role. After prioritization according to the CXCR4 corresponding pathways, five molecules (JUNB, YWHAB, TYROBP, NFYA, and PRDX1) were selected for further analysis in biological samples. The SKBR3, MDA-MB231, and MCF7 cell lines, as well as PBMCs from normal (n = 10) blood donors, were used as controls to define the expression pattern of all the examined molecules. Consequently, 100 previously untreated metastatic breast cancer (mBC) patients (n = 100) were analyzed using the following combinations of antibodies: CK (cytokeratin)/CXCR4/JUNB, CK/NFYA/ΥWHΑΒ (14-3-3), and CK/TYROBP/PRDX1. A threshold value for every molecule was considered the mean expression in normal PBMCs. RESULTS: Quantification of CXCR4 revealed overexpression of the receptor in SKBR3 and in CTCs, following the subsequent scale (SKBR3>CTCs>Hela>MCF7>MDA-MB231). JUNB was also overexpressed in CTCs (SKBR3>CTCs>MCF7>MDA-MB231>Hela). According to the defined threshold for each molecule, CXCR4-positive CTCs were identified in 90% of the patients with detectable tumor cells in their blood. In addition, 65%, 75%, 14.3%, and 12.5% of the patients harbored JUNB-, TYROBP-, NFYA-, and PRDX-positive CTCs, respectively. Conversely, none of the patients revealed YWHAB-positive CTCs. Interestingly, JUNB expression in CTCs was phenotypically and statistically enhanced compared to patients' blood cells (p = 0.002) providing a possible new biomarker for CTCs. Furthermore, the detection of JUNB-positive CTCs in patients was associated with poorer PFS (p = 0.015) and OS (p = 0.002). Moreover, JUNB staining of 11 primary and 4 metastatic tumors from the same cohort of patients revealed a dramatic increase of JUNB expression in metastasis. CONCLUSIONS: CXCR4, JUNB, and TYROBP were overexpressed in CTCs, but only the expression of JUNB was associated with poor prognosis, providing a new biomarker and a potential therapeutic target for the elimination of CTCs.

PharmActa: Empowering Patients to Avoid Clinical Significant Drug⁻Herb Interactions.

Herbal medicinal products (HMPs) are the subject of increasing interest regarding their benefits for health. However, a serious concern is the potential appearance of clinically significant drug⁻herb interactions in patients. This work provides an overview of drug⁻herb interactions and an evaluation of their clinical significance. We discuss how personalized health services and mobile health applications can utilize tools that provide essential information to patients to avoid drug⁻HMP interactions. There is a specific mention to PharmActa, a dedicated mobile app for personalized pharmaceutical care with information regarding drug⁻HMPs interactions. Several studies over the years have shown that for some HMPs, the potential to present clinically significant interactions is evident, especially for many of the top selling HMPs. Towards that, PharmActa presents how we can improve the way that information regarding potential drug⁻herb interactions can be disseminated to the public. The utilization of technologies focusing on medical information and context awareness introduce a new era in healthcare. The exploitation of eHealth tools and pervasive mobile monitoring technologies in the case of HMPs will allow the citizens to be informed and avoid potential drug⁻HMPs interactions enhancing the effectiveness and ensuring safety for HMPs.

Addressing drug-drug and drug-food interactions through personalized empowerment services for healthcare.

Personalized healthcare systems support the provision of timely and appropriate information regarding healthcare options and treatment alternatives. Especially for patients that receive multi-drug treatments a key issue is the minimization of the risk of adverse effects due to drug-drug interactions (DDIs). DDIs may be the result of doctor prescribed drugs but also due to self-medication of conventional drugs, alternative medicines, food habits, alcohol or smoking. It is therefore crucial for personalized health systems, apart from assisting physicians for optimal prescription practices, to also provide appropriate information for individual users for drug-drug interactions or similar information regarding risks for modulation of the ensuing treatment. In this manuscript we describe a DDI service including drug-food, drug-herb and other lifestyle-related factors, developed in the context of a personalized patient empowerment platform. The solution enables guidance to patients for their medication on how to reduce the risk of unwanted drug interactions and side effects in a seamless and transparent way. We present and analyze the implemented services and provide examples on using an alerting service to identify potential DDIs in two different chronic diseases, congestive heart failure and osteoarthritis.

Ensuring sustainability of software tools and services by cooperation with a research infrastructure.

Sustainability of project output and especially of the maintenance and further development of software is of growing concern for the research community. In the personalized medicine project p-medicine solutions that address this sustainability problem were developed and discussed in a workshop. They involve a number of interrelated and mutually supportive measures including the creation of a service center, building modular software, using common data standards, mutual service exchange with a research infrastructure, Open Source and fee-based software provision, joint promotion and deployment of tools in a regulated, clinical trial situation. These ideas join a nascent literature seeking to understand how project output can be put into a sustainable environment and to suggest solutions that may be useful for academic projects in general.

Semantic biomedical resource discovery: a Natural Language Processing framework.

BACKGROUND: A plethora of publicly available biomedical resources do currently exist and are constantly increasing at a fast rate. In parallel, specialized repositories are been developed, indexing numerous clinical and biomedical tools. The main drawback of such repositories is the difficulty in locating appropriate resources for a clinical or biomedical decision task, especially for non-Information Technology expert users. In parallel, although NLP research in the clinical domain has been active since the 1960s, progress in the development of NLP applications has been slow and lags behind progress in the general NLP domain. The aim of the present study is to investigate the use of semantics for biomedical resources annotation with domain specific ontologies and exploit Natural Language Processing methods in empowering the non-Information Technology expert users to efficiently search for biomedical resources using natural language. METHODS: A Natural Language Processing engine which can "translate" free text into targeted queries, automatically transforming a clinical research question into a request description that contains only terms of ontologies, has been implemented. The implementation is based on information extraction techniques for text in natural language, guided by integrated ontologies. Furthermore, knowledge from robust text mining methods has been incorporated to map descriptions into suitable domain ontologies in order to ensure that the biomedical resources descriptions are domain oriented and enhance the accuracy of services discovery. The framework is freely available as a web application at ( http://calchas.ics.forth.gr/ ). RESULTS: For our experiments, a range of clinical questions were established based on descriptions of clinical trials from the ClinicalTrials.gov registry as well as recommendations from clinicians. Domain experts manually identified the available tools in a tools repository which are suitable for addressing the clinical questions at hand, either individually or as a set of tools forming a computational pipeline. The results were compared with those obtained from an automated discovery of candidate biomedical tools. For the evaluation of the results, precision and recall measurements were used. Our results indicate that the proposed framework has a high precision and low recall, implying that the system returns essentially more relevant results than irrelevant. CONCLUSIONS: There are adequate biomedical ontologies already available, sufficiency of existing NLP tools and quality of biomedical annotation systems for the implementation of a biomedical resources discovery framework, based on the semantic annotation of resources and the use on NLP techniques. The results of the present study demonstrate the clinical utility of the application of the proposed framework which aims to bridge the gap between clinical question in natural language and efficient dynamic biomedical resources discovery.

Tamoxifen induces a pluripotency signature in breast cancer cells and human tumors.

Tamoxifen is the treatment of choice in estrogen receptor alpha breast cancer patients that are eligible for adjuvant endocrine therapy. However, ∼50% of ERα-positive tumors exhibit intrinsic or rapidly acquire resistance to endocrine treatment. Unfortunately, prediction of de novo resistance to endocrine therapy and/or assessment of relapse likelihood remain difficult. While several mechanisms regulating the acquisition and the maintenance of endocrine resistance have been reported, there are several aspects of this phenomenon that need to be further elucidated. Altered metabolic fate of tamoxifen within patients and emergence of tamoxifen-resistant clones, driven by evolution of the disease phenotype during treatment, appear as the most compelling hypotheses so far. In addition, tamoxifen was reported to induce pluripotency in breast cancer cell lines, in vitro. In this context, we have performed a whole transcriptome analysis of an ERα-positive (T47D) and a triple-negative breast cancer cell line (MDA-MB-231), exposed to tamoxifen for a short time frame (hours), in order to identify how early pluripotency-related effects of tamoxifen may occur. Our ultimate goal was to identify whether the transcriptional actions of tamoxifen related to induction of pluripotency are mediated through specific ER-dependent or independent mechanisms. We report that even as early as 3 hours after the exposure of breast cancer cells to tamoxifen, a subset of ERα-dependent genes associated with developmental processes and pluripotency are induced and this is accompanied by specific phenotypic changes (expression of pluripotency-related proteins). Furthermore we report an association between the increased expression of pluripotency-related genes in ERα-positive breast cancer tissues samples and disease relapse after tamoxifen therapy. Finally we describe that in a small group of ERα-positive breast cancer patients, with disease relapse after surgery and tamoxifen treatment, ALDH1A1 (a marker of pluripotency in epithelial cancers which is absent in normal breast tissue) is increased in relapsing tumors, with a concurrent modification of its intra-cellular localization. Our data could be of value in the discrimination of patients susceptible to develop tamoxifen resistance and in the selection of optimized patient-tailored therapies.

A network-based approach to enrich gene signatures for the prediction of breast cancer metastases.

Despite the multiplicity of the gene expression analysis studies for the identification of genomics based origins of cancerous diseases, the presented gene signatures have generally little overlap. The genes do not function in isolation and therefore a more holistic approach that takes into account the interactions among them is needed. In this study we present a stepwise refinement methodology where starting from some initial set of biomarkers we expand and enrich this set taking into account existing biological information. In particular, we start with a 27 gene signature previously identified as indicative of the presence of circulating tumor cells (CTCs) in peripheral blood of breast cancer patients. We use the manually curated HINT database of protein-protein interactions as the background biological network to locate the network-based similarity of the input genes and how they connect to each other. The result is an enriched connected set of genes that is subsequently expanded to form an even bigger network based on the ability of the surrounding genes to strongly correlate with the phenotypes of a training set of breast cancer patient cases. The induced network is then used as a new gene signature for the classification of breast brain metastases in an independent dataset. The results are encouraging for the validity of this method.

Digital patient: Personalized and translational data management through the MyHealthAvatar EU project.

The advancements in healthcare practice have brought to the fore the need for flexible access to health-related information and created an ever-growing demand for the design and the development of data management infrastructures for translational and personalized medicine. In this paper, we present the data management solution implemented for the MyHealthAvatar EU research project, a project that attempts to create a digital representation of a patient's health status. The platform is capable of aggregating several knowledge sources relevant for the provision of individualized personal services. To this end, state of the art technologies are exploited, such as ontologies to model all available information, semantic integration to enable data and query translation and a variety of linking services to allow connecting to external sources. All original information is stored in a NoSQL database for reasons of efficiency and fault tolerance. Then it is semantically uplifted through a semantic warehouse which enables efficient access to it. All different technologies are combined to create a novel web-based platform allowing seamless user interaction through APIs that support personalized, granular and secure access to the relevant information.

On the identification of circulating tumor cells in breast cancer.

Breast cancer is a highly heterogeneous disease and very common among western women. The main cause of death is not the primary tumor but its metastases at distant sites, such as lymph nodes and other organs (preferentially lung, liver, and bones). The study of circulating tumor cells (CTCs) in peripheral blood resulting from tumor cell invasion and intravascular filtration highlights their crucial role concerning tumor aggressiveness and metastasis. Genomic research regarding CTCs monitoring for breast cancer is limited due to the lack of indicative genes for their detection and isolation. Instead of direct CTC detection, in our study, we focus on the identification of factors in peripheral blood that can indirectly reveal the presence of such cells. Using selected publicly available breast cancer and peripheral blood microarray datasets, we follow a two-step elimination procedure for the identification of several discriminant factors. Our procedure facilitates the identification of major genes involved in breast cancer pathology, which are also indicative of CTCs presence.

Web-based workflow planning platform supporting the design and execution of complex multiscale cancer models.

Significant Virtual Physiological Human efforts and projects have been concerned with cancer modeling, especially in the European Commission Seventh Framework research program, with the ambitious goal to approach personalized cancer simulation based on patient-specific data and thereby optimize therapy decisions in the clinical setting. However, building realistic in silico predictive models targeting the clinical practice requires interactive, synergetic approaches to integrate the currently fragmented efforts emanating from the systems biology and computational oncology communities all around the globe. To further this goal, we propose an intelligent graphical workflow planning system that exploits the multiscale and modular nature of cancer and allows building complex cancer models by intuitively linking/interchanging highly specialized models. The system adopts and extends current standardization efforts, key tools, and infrastructure in view of building a pool of reliable and reproducible models capable of improving current therapies and demonstrating the potential for clinical translation of these technologies.

The technologically integrated oncosimulator: combining multiscale cancer modeling with information technology in the in silico oncology context.

This paper outlines the major components and function of the technologically integrated oncosimulator developed primarily within the Advancing Clinico Genomic Trials on Cancer (ACGT) project. The Oncosimulator is defined as an information technology system simulating in vivo tumor response to therapeutic modalities within the clinical trial context. Chemotherapy in the neoadjuvant setting, according to two real clinical trials concerning nephroblastoma and breast cancer, has been considered. The spatiotemporal simulation module embedded in the Oncosimulator is based on the multiscale, predominantly top-down, discrete entity-discrete event cancer simulation technique developed by the In Silico Oncology Group, National Technical University of Athens. The technology modules include multiscale data handling, image processing, invocation of code execution via a spreadsheet-inspired environment portal, execution of the code on the grid, and the visualization of the predictions. A refining scenario for the eventual coupling of the oncosimulator with immunological models is also presented. Parameter values have been adapted to multiscale clinical trial data in a consistent way, thus supporting the predictive potential of the oncosimulator. Indicative results demonstrating various aspects of the clinical adaptation and validation process are presented. Completion of these processes is expected to pave the way for the clinical translation of the system.