Publications

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Lung cancer screening by low-dose computed tomography reduces lung cancer mortality, but reliable risk-based selection of participants is crucial to maximize benefits and minimize harms. Multiple risk models have been developed for this purpose, and their discrimination and calibration performance is commonly evaluated based on large-scale cohort studies. Using a recent comparative evaluation of 10 risk models as an example, we illustrate the merits, limitations and pitfalls of such evaluations.

This report provides a summary of the conclusions from the second annual meeting of the “Understanding (risk factors & determinants)” cluster within the EU Cancer Mission. The morning sessions featured scientific updates from each project, while the afternoon focused on “Cancer Mission Data Initiatives”, presented by a European Commission policy officer, and discussions on topics outlined in the common work plan deliverable. Key points included the importance of data management, AI in cancer research, risk stratification tools, and best practices in healthcare policy implementation. The meeting underscored the importance of collaborative efforts in addressing cancer research challenges and highlighted future plans for enhancing data sharing, citizen engagement, and addressing inequalities in cancer care.

This report summarizes the Policy brief formulating recommendations based on the research and innovation strand of the “Understanding” annual cluster meeting -Y2. The goal of this deliverable is to provide an annual policy brief with recommendations on Research & Innovation (R&I) on the macro scale, i.e., from the perspective of the “Understanding (risk factors & determinants)” cluster. This policy brief formulates recommendations to foster collaboration, focusing on data/material management, technological advancmeents, risk factor analysis, and policy implementation, based on the research and innovation strand of the “Understanding” annual cluster meeting in the second year. This deliverable raises common barriers and potential recommendations as well as practical recommendations for the near future.

This newsletter provides an update about the project activities.

This newsletter presents the activities of the project under the Prevention & Early Detection (Screening) cluster: DIOPTRA, LUCIA, MammoScreen, ONCOSCREEN, PANCAID and SANGUINE.

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The most well-established risk factor for lung cancer (LC) is smoking, responsible for approximately 85% of cases. The Lung Cancer Risk Assessment Tool (LCRAT) is a key advancement in this field, which predicts individual risk based on factors like smoking habits, demographic details, personal and family medical history, and environmental exposures. This paper proposes a model with fewer features that improves state of the art performance, using a simplified stacking ensemble, making it more accessible and easier to implement in routine healthcare practice. The data used in this work were derived from two cohorts in the United States: The National Lung Screening Trial (NLST) and the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial. Both our model and LCRAT achieve an AUC of 0.799 and 0.782 on test respectively. In terms of percentage of positives, in the 50% of the population, both detect 0.766 and 0.754 of the cases. The ensemble of different survival models enhances robustness by mitigating the weakness of individual models and directly impacts the efficiency of the model, increasing the efficiency and generalizability.

Volatile organic compounds (VOCs) play a crucial role in affecting health, environmental integrity, and industrial operations, from air quality to medical diagnostics. The need for highly sensitive and selective detection of these compounds has spurred innovation in sensor technologies. This editorial introduces a special collection of articles in Applied Physics Reviews, exploring the latest advancements in VOC detection technologies. The featured works cover a range of innovations, including electrostatically formed nanowires, chiral liquid crystals, and graphene-based sensors enhanced by machine learning. Together, these articles highlight the dynamic progress in VOC detection, striving for improved sensitivity, selectivity, and real-world applicability. This special collection not only showcases pioneering research but also provides valuable insights into future trends and potential applications in the field.