Publications

Organoids mimic human organ function, offering insights into development and disease. However, non-destructive, real-time monitoring is lacking, as traditional methods are often costly, destructive, and low-throughput. In this article, a non-destructive chemical tomographic strategy is presented for decoding cyto-proteo-genomics of organoid using volatile signaling molecules, hereby, Volatile Organic Compounds (VOCs), to indicate metabolic activity and development of organoids. Combining a hierarchical design of graphene-based sensor arrays with AI-driven analysis, this method maps VOC spatiotemporal distribution and generate detailed digital profiles of organoid morphology and proteo-genomic features. Lens- and label-free, it avoids phototoxicity, distortion, and environmental disruption. Results from testing organoids with the reported chemical tomography approach demonstrate effective differentiation between cyto-proteo-genomic profiles of normal and diseased states, particularly during dynamic transitions such as epithelial-mesenchymal transition (EMT). Additionally, the reported approach identifies key VOC-related biochemical pathways, metabolic markers, and pathways associated with cancerous transformations such as aromatic acid degradation and lipid metabolism. This real-time, non-destructive approach captures subtle genetic and structural variations with high sensitivity and specificity, providing a robust platform for multi-omics integration and advancing cancer biomarker discovery.

Lung cancer remains one of the deadliest cancers and a major public health concern. Although numerous studies have identified various risk factors, further research is essential, particularly in the biological domain. Existing data sources compile biological information on lung cancer and its subtypes but differ in structure and format, complicating data extraction and integration for artificial intelligence (AI) models. Ontologies and semantic technologies address this challenge by enabling the construction of unified knowledge graphs that promote interoperability. Lung-CABO is an ontology specifically designed for lung cancer, supporting the creation of a knowledge graph for risk factor identification and AI applications. Its modular design allows expansion to integrate additional data, such as environmental factors, further enhancing its utility and reusability.

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.

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.

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.

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.

Key Features

Description

Background: Interstitial lung diseases (ILDs) are an heterogeneous group of infiltrating lung pathologies, for which prompt diagnosis and continuous assessment are of paramount importance. While chest CT is an established diagnostic tool for ILDs, there are no formal guidelines on the follow-up regimen, leaving the frequency and modality of follow-up largely at the clinician’s discretion.

Methods: The study retrospectively evaluated the indication of chest CT in a cohort of 129 ILD patients selected from the ambulatory care polyclinic at University Hospital of Liège. The aim was to determine whether the imagining acquisition had a true impact on clinical course and follow-up. We accepted three different situations for justifying the indication of the CTs: clinical deterioration, a decrease in pulmonary function tests (at least a 10% drop in a parameter), and monitoring for oncological purposes. The other indications, mainly routine follow-up, were classified as “non-justified”. Radiation dose output was evaluated with Computed Tomography Dose Index (CTDI) and Dose Length Product (DLP).

Results: The mean number of CT scans per patient per year was 1.7 ± 0.4, determining irradiation in CTDI (mGy)/year of 34.9 ± 64.9 and DLP in (mGy*cm)/year of 1095 ± 1971. The percentage of justified CT scans was 57 ± 32%, while the scans justified a posteriori were 60 ± 34%. Around 40% of the prescribed monitoring CT scans had no impact on the management of ILD and direct patient care.

Conclusions: Our study identifies a trend of overuse in chest CT scans at follow-up (up to 40%), outside those performed for clinical exacerbation or oncological investigation. In the particular case of ILD exacerbation, CT scan value remains high, underlying the benefit of this strategy.