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High dose-rate brachytherapy presents a promising therapeutic avenue for prostate cancer management, involving the temporary implantation of catheters which deliver radioactive sources to the cancerous site. However, as catheters puncture and penetrate the prostate, tissue deformation is evident which may affect the accuracy and efficiency of the treatment. In this work, a data-driven in silico modelling procedure is proposed to simulate brachytherapy while accounting for prostate biomechanics. Comprehensive magnetic resonance and transrectal ultrasound images acquired prior, during and post brachytherapy are employed for model personalisation, while the therapeutic procedure is simulated via sequential insertion of multiple catheters in the prostate gland. The medical imaging data are also employed for model evaluation, thus, demonstrating the potential of the proposed in silico procedure to be utilised pre- and intra-operatively in the clinical setting.

In recent years, the number of minor migrants, both accompanied and unaccompanied, arriving in European countries increased significantly. The impact of newcomers on the school systems of various European countries has highlighted problems in education continuity and in the accomplishment of educational goals. Relying on the preliminary data from the Erasmus+ KA2 project “Continugee” in Italy and Greece, this paper analyses the current policies and practices used in refugee children education. In addition to policy analysis, interviews with families, teachers and professionals operating in migrant shelters and schools were conducted, aimed to address both institutional and relational dimensions of schooling and to point out good practices in educational incorporation of refugee youth. Research shows that – notwithstanding a common concern throughout Europe, national and local regulations, and local practices do affect their access to quality education. Age limits, gender gaps, location of shelters, lack of adequate institutional educational facilities and of professional training make effective educational placement and continuity difficult. Schools have a very different level of effectiveness and European policy innovation is often jeopardized by lack of resources and staff motivation. Recognition of qualifications and skills, a supported participation into mainstream education, and a participatory approach with families and guardians are essential for effective school integration. Keywords: refugee, minors, education, training, policy, European

Purpose/objective(s)Auto-segmentation with artificial intelligence (AI) offers an opportunity to reduce inter- and intra-observer variability in contouring, to improve the quality of contours, as well as to reduce the time taken to conduct this manual task. In this work we benchmark the AI auto-segmentation contours produced by five commercial vendors against a common dataset.Methods and materialsThe organ at risk (OAR) contours generated by five commercial AI auto-segmentation solutions (Mirada (Mir), MVision (MV), Radformation (Rad), RayStation (Ray) and TheraPanacea (Ther)) were compared to manually-drawn expert contours from 20 breast, 20 head and neck, 20 lung and 20 prostate patients. Comparisons were made using geometric similarity metrics including volumetric and surface Dice similarity coefficient (vDSC and sDSC), Hausdorff distance (HD) and Added Path Length (APL). To assess the time saved, the time taken to manually draw the expert contours, as well as the time to correct the AI contours, were recorded.ResultsThere are differences in the number of CT contours offered by each AI auto-segmentation solution at the time of the study (Mir 99; MV 143; Rad 83; Ray 67; Ther 86), with all offering contours of some lymph node levels as well as OARs. Averaged across all structures, the median vDSCs were good for all systems and compared favorably with existing literature: Mir 0.82; MV 0.88; Rad 0.86; Ray 0.87; Ther 0.88. All systems offer substantial time savings, ranging between: breast 14-20 mins; head and neck 74-93 mins; lung 20-26 mins; prostate 35-42 mins. The time saved, averaged across all structures, was similar for all systems: Mir 39.8 mins; MV 43.6 mins; Rad 36.6 min; Ray 43.2 mins; Ther 45.2 mins.ConclusionsAll five commercial AI auto-segmentation solutions evaluated in this work offer high quality contours in significantly reduced time compared to manual contouring, and could be used to render the radiotherapy workflow more efficient and standardized.

Abstract To achieve clinical impact in daily oncological practice, emerging AI-based cancer imaging research needs to have clearly defined medical focus, AI methods, and outcomes to be estimated. AI-supported cancer imaging should predict major relevant clinical endpoints, aiming to extract associations and draw inferences in a fair, robust, and trustworthy way. AI-assisted solutions as medical devices, developed using multicenter heterogeneous datasets, should be targeted to have an impact on the clinical care pathway. When designing an AI-based research study in oncologic imaging, ensuring clinical impact in AI solutions requires careful consideration of key aspects, including target population selection, sample size definition, standards, and common data elements utilization, balanced dataset splitting, appropriate validation methodology, adequate ground truth, and careful selection of clinical endpoints. Endpoints may be pathology hallmarks, disease behavior, treatment response, or patient prognosis. Ensuring ethical, safety, and privacy considerations are also mandatory before clinical validation is performed. The Artificial Intelligence for Health Imaging (AI4HI) Clinical Working Group has discussed and present in this paper some indicative Machine Learning (ML) enabled decision-support solutions currently under research in the AI4HI projects, as well as the main considerations and requirements that AI solutions should have from a clinical perspective, which can be adopted into clinical practice. If effectively designed, implemented, and validated, cancer imaging AI-supported tools will have the potential to revolutionize the field of precision medicine in oncology.

Although university’s contribution to the democratic society has been studied adequately, the establishment of its internal democratic institutions has not. Issues of autonomy and accountability exist whereas, today’s Postmodernism introduces further uncertainty. After constructing a framework for measuring democracy within a university using democracy indicators selected from international organizations, we attempt to interrelate these indicators to its democratic characteristics, raising the question: “To what extent could these characteristics be eroded before the university collapses?” Interviews with European academics were conducted and the influence of forces external to the university were studied using the Central European University in Hungary as a case study. The findings show that increased state control undermines institutional autonomy and so does imposing unnecessary restrictions. Protecting democracy and academic freedom, civil rights, and supporting an open society are of paramount importance, otherwise the university collapses. A model that captures such catastrophic state changes is finally proposed.

The three-dimensional iridium-192 (192Ir) high-dose-rate (HDR) brachytherapy manifests itself as a high-precision, hypofractionated, dose-escalating, minimally invasive method in the armamentarium of contemporary radiation oncology clinical applications. In this study, the physical aspects of the 192Ir radionuclide are presented. Its dosimetric application in HDR brachytherapy for different anatomical sites (prostate, gynecological malignancies, liver, and intrathoracic tumors) as well as the corresponding dosimetric comparison with the stereotactic body radiation therapy (SBRT) techniques based on a representative selection of dosimetric publications is reviewed and illustrated.

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.