Developing an Integrated Genomic Profile for Cancer Patients with the Use of NGS Data

Next Generation Sequencing (NGS) technologies has revolutionized genomics data research by facilitating high-throughput sequencing of genetic material that comes from different sources, such as Whole Exome Sequencing (WES) and RNA Sequencing (RNAseq). The exploitation and integration of this wealth of heterogeneous sequencing data remains a major challenge. There is a clear need for approaches that attempt to process and combine the aforementioned sources in order to create an integrated profile of a patient that will allow us to build the complete picture of a disease. This work introduces such an integrated profile using Chronic Lymphocytic Leukemia (CLL) as the exemplary cancer type. The approach described in this paper links the various NGS sources with the patients’ clinical data. The resulting profile efficiently summarizes the large-scale datasets, links the results with the clinical profile of the patient and correlates indicators arising from different data types. With the use of state-of-the-art machine learning techniques and the association of the clinical information with these indicators, which served as the feature pool for the classification, it has been possible to build efficient predictive models. To ensure reproducibility of the results, open data were exclusively used in the classification assessment. The final goal is to design a complete genomic profile of a cancer patient. The profile includes summarization and visualization of the results of WES and RNAseq analysis (specific variants and significantly expressed genes, respectively) and the clinical profile, integration/comparison of these results and a prediction regarding the disease trajectory. Concluding, this work has managed to produce a comprehensive clinico-genetic profile of a patient by successfully integrating heterogeneous data sources. The proposed profile can contribute to the medical research providing new possibilities in personalized medicine and prognostic views

The holistic perspective of the INCISIVE project : artificial intelligence in screening mammography

Finding new ways to cost-effectively facilitate population screening and improve cancer diagnoses at an early stage supported by data-driven AI models provides unprecedented opportunities to reduce cancer related mortality. This work presents the INCISIVE project initiative towards enhancing AI solutions for health imaging by unifying, harmonizing, and securely sharing scattered cancer-related data to ensure large datasets which are critically needed to develop and evaluate trustworthy AI models. The adopted solutions of the INCISIVE project have been outlined in terms of data collection, harmonization, data sharing, and federated data storage in compliance with legal, ethical, and FAIR principles. Experiences and examples feature breast cancer data integration and mammography collection, indicating the current progress, challenges, and future directions

PhD courses and the intersectoral experience:a comprehensive survey

Abstract It has been found that most PhD graduates (>85%) do not achieve a long-term academic career and thus there is a growing need to re-imagine PhD education that incentivizes doctoral students to engage with research consumers, not only within their discipline, but also, across other disciplines and sectors to have real social impact for an improved society. The aim of this work is to identify intersectoral/interdisciplinary courses that are considered to broaden student career outside and inside academia. For this purpose, a survey was designed to identify modules which lead to the improvement of students’ skills while an analysis of their attributes was also performed. Two target groups have been considered: (a) young researchers and (b) program directors each of which can provide different information regarding the courses of interest. 52 students and 11 directors from 5 European Universities, participated in the study. An absence of such courses in the standard PhD program was observed, while any intersectoral/interdisciplinary activities were conducted outside the PhD program, and organized by collaboration of academia and other organizations. The survey findings reveal the need to restructure the PhD programs

Position of the AI for Health Imaging (AI4HI) network on metadata models for imaging biobanks

A huge amount of imaging data is becoming available worldwide and an incredible range of possible improvements can be provided by artificial intelligence algorithms in clinical care for diagnosis and decision support. In this context, it has become essential to properly manage and handle these medical images and to define which metadata have to be considered, in order for the images to provide their full potential. Metadata are additional data associated with the images, which provide a complete description of the image acquisition, curation, analysis, and of the relevant clinical variables associated with the images. Currently, several data models are available to describe one or more subcategories of metadata, but a unique, common, and standard data model capable of fully representing the heterogeneity of medical metadata has not been yet developed. This paper reports the state of the art on metadata models for medical imaging, the current limitations and further developments, and describes the strategy adopted by the Horizon 2020 "AI for Health Imaging" projects, which are all dedicated to the creation of imaging biobanks.This work has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement Nos. 826494 (PRIMAGE), 952172 (Chaimeleon), 952103 (EuCanImage), 952159 (ProCancer-I), and 952179 (INCISIVE)

Position of the AI for Health Imaging (AI4HI) network on metadata models for imaging biobanks

A huge amount of imaging data is becoming available worldwide and an incredible range of possible improvements can be provided by artificial intelligence algorithms in clinical care for diagnosis and decision support. In this context, it has become essential to properly manage and handle these medical images and to define which metadata have to be considered, in order for the images to provide their full potential. Metadata are additional data associated with the images, which provide a complete description of the image acquisition, curation, analysis, and of the relevant clinical variables associated with the images. Currently, several data models are available to describe one or more subcategories of metadata, but a unique, common, and standard data model capable of fully representing the heterogeneity of medical metadata has not been yet developed. This paper reports the state of the art on metadata models for medical imaging, the current limitations and further developments, and describes the strategy adopted by the Horizon 2020 “AI for Health Imaging” projects, which are all dedicated to the creation of imaging biobanks