Publications

This deliverable has been conceived in the frame of T4.3 “General population screening”, T4.4 “Diagnosis and precision follow-up and stratification” and T4.5 “Contextual-empirical investigations to evaluate the realization of identified values”. These tasks are devoted to the recruitment of a prospective cohort of around 3,000 volunteers that will be followed up for 2 years and on the diagnosis, including Indeterminate Pulmonary Nodules (IPN) characterization, with an accentuation on the Never Smokers and Reduced Smokers (NSRS) patients, incorporating Breath Analyser (BAN), Wide-biomarker-spectrum Multi-Use Sensing Patch (WBSP) and spectrometry-on-card (SPOC) into clinical studies.

Regarding Task 4.5, it examines whether identified socio-technical values (in WP1-3) (e.g., transparency, bias, accountability, explanability) are realized when the technology is used. To achieve this goal, the different contexts of the use of technology are to be analysed as different contextual variables come into play to impact the way values are understood.

Volunteers have been recruited from different clinical centers (“Servicio Andaluz de Salud” (SAS) and “Osakidetza Servicio Vasco de Salud” (OSA), in Spain; “Centre Hospitalier Universitaire de Liège” (CHUL) in Belgium, and “Centre for Tuberculosis and Lung Diseases (CTLD) of Riga East University Hospital (REUH)” in Latvia). Non-invasive devices such as Breath Analyzer (BAN), Multiomics (MO) and spectrometry-on-card (SPOC) are monitoring these participants.

The entire study cohort is currently being followed up. For 2 years, participants of the study will attend to 4 visits: baseline, month 6, month 12 and month 24. During these visits, the following tests and procedures will be carried out:

– Baseline visit: blood test, spirometry, lifestyle questionnaires, sociodemographic data, medical record data, exposure to harmful agents data, physical exploration, LDCT scan and new lung cancer screening devices testing (breath analyzer and spectrometry-on-card)
– 6 months visit: remote visit where sociodemographic data, medical record data and exposure to harmful agents data will be recorded.
– 12 months visit: spirometry, lifestyle questionnaires, sociodemographic data, medical record data, exposure to harmful agents’ data and physical exploration.
– 24 months visit: spirometry, ifestyle questionnaires, sociodemographic data, medical record data, exposure to harmful agents’ data, physical exploration, low-dose computed tomography (LDCT) scan and new lung cancer screening devices testing (breath analyzer and spectrometry-on-card).

(See the following of the Executive Summary in file).

BACKGROUND
The US Preventive Services Task Force (USPSTF) recommends lung cancer screening for individuals aged 50–80 with ≥20 pack-years of smoking and ≤15 quit-years. This implies that former heavy smokers with ≥20 pack-years would either be offered screening from age 50 onwards or not at all, depending on a dichotomous classification by time since cessation. An alternative strategy that would better match individual risks could be to define risk-adapted starting ages of screening, according to time since cessation.

METHODS
Based on data from the UK Biobank cohort, we assessed the relationship between smoking cessation time, pack-years, age, and lung cancer risk among ever heavy smokers using multivariable Cox proportional hazards models. We estimated “risk postponement periods” (RPPs) from the regression coefficients for the time since cessation and age and used these RPP estimates to derive risk-adapted starting ages for lung cancer screening among former heavy smokers, using 50 years as the reference starting age for current heavy smokers.

RESULTS
The RPPs for smoking cessation ranged from 3.1 (95% CI 1.7, 4.5) years for former heavy smokers who quit 6-10 years ago to 14.6 (95% CI 13.0, 16.3) years for former heavy smokers who quit more than 15 years ago, which translate to risk-adapted starting ages of screening between 53 and 65 years.

CONCLUSIONS
Our analysis provides an empirical basis for risk-adapted starting ages of lung cancer screening among former heavy smokers.

Somatic cells can transform into tumors due to mutations, and the tumors further evolve towards increased aggressiveness and therapy resistance. We develop DiffInvex, a framework for identifying changes in selection acting on individual genes in somatic genomes, drawing on an empirical mutation rate baseline derived from non-coding DNA that accounts for shifts in neutral mutagenesis during cancer evolution. We apply DiffInvex to >11,000 somatic whole-genome sequences from ~30 cancer types or healthy tissues, identifying genes where point mutations are under conditional positive or negative selection during exposure to specific chemotherapeutics, suggesting drug resistance mechanisms occurring via point mutation. DiffInvex identifies 11 genes exhibiting treatment-associated selection for different classes of chemotherapies, linking selected mutations in PIK3CA, APC, MAP2K4, SMAD4, STK11 and MAP3K1 with drug exposure. Various gene-chemotherapy associations are further supported by differential functional impact of mutations pre- versus post-therapy, and are also replicated in independent studies. In addition to nominating drug resistance genes, we contrast the genomes of healthy versus cancerous cells of matched human tissues. We identify noncancerous expansion-specific drivers, including NOTCH1 and ARID1A. DiffInvex can also be applied to diverse analyses in cancer evolution to identify changes in driver gene repertoires across time or space.

Biological Field Effect Transistors (Bio-FETs) are redefining the standard of biosensing by enabling label-free, real-time, and extremely sensitive detection of biomolecules. At the center of this innovation is the fundamental empowering role of advanced materials, such as graphene, molybdenum disulfide, carbon nanotubes, and silicon. These materials, when harnessed with the downstream biomolecular probes like aptamers, antibodies, and enzymes, allow Bio-FETs to offer unrivaled sensitivity and precision. This review is an exposition of how advancements in materials science have permitted Bio-FETs to detect biomarkers in extremely low concentrations, from femtomolar to attomolar levels, ensuring device stability and reliability. Specifically, the review examines how the incorporation of cutting-edge materials architectures, like flexible / stretchable and multiplexed designs, is expanding the frontiers of biosensing and contributing to the development of more adaptable and user-friendly Bio-FET platforms. A key focus is placed on the synergy of Bio-FETs with artificial intelligence (AI), the Internet of Things (IoT), and sustainable materials approaches as fast-tracking toward transition from research into practical healthcare applications. The review also explores current challenges such as material reproducibility, operational durability, and cost-effectiveness. It outlines targeted strategies to address these hurdles and facilitate scalable manufacturing. By emphasizing the transformative role played by advanced materials and their cementing position in Bio-FETs, this review positions Bio-FETs as a cornerstone technology for the future healthcare solution for precision applications. These advancements would lead to an era where material innovation would herald massive strides in biomedical diagnostics and subsume.

As part of the LUCIA project, funded under Horizon Europe, Yaghma facilitated Social Lab workshops to identify and address challenges in implementing novel lung cancer screening technologies including an AI-driven risk prediction algorithm. Engaging healthcare professionals, policymakers, and patient representatives, these participatory sessions helped uncover key barriers of implementing LUCIA technologies to lung cancer screening.

Through structured discussions, 80 actionable barriers have been identified across 10 key categories, including trust, feasibility, ethical considerations, and regulation. These insights were mapped in an Impact-Influence map through a prioritization survey, ensuring that the project focuses on the most critical and actionable challenges.

Yaghma’s role in this process underscores the importance of stakeholder engagement in responsible AI adoption, ensuring that AI technologies in cancer screening are practical, inclusive, and aligned with real-world healthcare and patient needs.

How is the LUCIA Project Study Conducted in Latvia

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This review article explores the transformative potential of smart dust systems by examining how existing chemical sensing technologies can be adapted and advanced to realize their full capabilities. Smart dust, characterized by submillimeter-scale autonomous sensing platforms, offers unparalleled opportunities for real-time, spatiotemporal chemical mapping across diverse environments. This article introduces the technological advancements underpinning these systems, critically evaluates current limitations, and outlines new avenues for development. Key challenges, including multi-compound detection, system control, environmental impact, and cost, are discussed alongside potential solutions. By leveraging innovations in miniaturization, wireless communication, AI-driven data analysis, and sustainable materials, this review highlights the promise of smart dust to address critical challenges in environmental monitoring, healthcare, agriculture, and defense sectors. Through this lens, the article provides a strategic roadmap for advancing smart dust from concept to practical application, emphasizing its role in transforming the understanding and management of complex chemical systems.

Background
While different lung cancer risk prediction models have been established as essential tools to identify high-risk participants for lung cancer screening programs, evaluations of their risk discriminatory performances have reported heterogenous findings in different research cohorts. We therefore aimed to summarise results of head-to-head comparisons of the predictive performance of various lung cancer risk models performed within the same study population.

Methods
In this systematic review and meta-analysis, we performed a systematic search of PubMed and Web of Science databases for primary studies published from inception to Oct 16, 2024. Articles comparing the performance of questionnaire-based lung cancer risk models in an independent, external validation cohort of participants with previous or current smoking exposure were included. The main reasons for exclusion of studies were if only one model was assessed in the external population or risk discrimination was not evaluated. Random-effects meta-analyses were conducted to synthesize differences in the area under the curve (AUC) of two models compared in multiple populations. To assess the risk of bias, PROBAST (the Prediction model Risk of Bias Assessment Tool) was used. The study was registered with PROSPERO, CRD42023427911.

Findings
The systematic search yielded 5568 records. In total, 15 eligible studies were included in the meta-analysis, comprising 4,134,648 individuals with previous or current smoking exposure, of whom 45,448 (1.10%) developed LC within 5–7 years. Among the nine models that were compared, AUC differences reached up to 0.050 between two models. The Lung Cancer Risk Assessment Tool (LCRAT), Bach model and PLCOm2012 model consistently had a higher AUC when compared to any other model, with AUC differences ranging between 0.018 (95% CI 0.011, 0.026) and 0.044 (95% CI 0.038, 0.049). The risk of bias and applicability concerns were deemed low in eight, and high in seven of the included studies. Results excluding studies with high risk of bias were mostly consistent. Among eight of the 24 model pairs that were compared, there was notable between-study heterogeneity (I2 ≥50%).

Interpretation
Our systematic review and meta-analyses of head-to-head comparisons disclose major differences in predictive performance of widely used lung cancer risk models. Although our review is limited to the availability of head-to-head comparisons, evidence from current cohort-based model comparisons indicates that the LCRAT, Bach and PLCOm2012 consistently outperformed alternative questionnaire-based risk prediction tools.