Early detection of colorectal cancer is crucial for improving outcomes and reducing mortality. While there is strong evidence of effectiveness, currently adopted screening methods present several shortcomings which negatively impact the detection of early stage carcinogenesis, including low uptake due to patient discomfort. As a result, developing novel, non-invasive alternatives is an important research priority. Recent advancements in the field of breathomics, the study of breath composition and analysis, have paved the way for new avenues for non-invasive cancer detection and effective monitoring. Harnessing the utility of Volatile Organic Compounds in exhaled breath, breathomics has the potential to disrupt colorectal cancer screening practices. Our goal is to outline key research efforts in this area focusing on machine learning methods used for the analysis of breathomics data, highlight challenges involved in artificial intelligence application in this context, and suggest possible future directions which are currently considered within the framework of the European project ONCOSCREEN.

Background: There is an emerging need for evidence-based approaches harnessing large amounts of health care data and novel technologies (such as artificial intelligence) to optimize public health policy making. Objective: The aim of this review was to explore the data analytics tools designed specifically for policy making in noncommunicable diseases (NCDs) and their implementation. Methods: A scoping review was conducted after searching the PubMed and IEEE databases for articles published in the last 10 years. Results: Nine articles that presented 7 data analytics tools designed to inform policy making for NCDs were reviewed. The tools incorporated descriptive and predictive analytics. Some tools were designed to include recommendations for decision support, but no pilot studies applying prescriptive analytics have been published. The tools were piloted with various conditions, with cancer being the least studied condition. Implementation of the tools included use cases, pilots, or evaluation workshops that involved policy makers. However, our findings demonstrate very limited real-world use of analytics by policy makers, which is in line with previous studies. Conclusions: Despite the availability of tools designed for different purposes and conditions, data analytics is not widely used to support policy making for NCDs. However, the review demonstrates the value and potential use of data analytics to support policy making. Based on the findings, we make suggestions for researchers developing digital tools to support public health policy making. The findings will also serve as input for the European Union–funded research project ONCODIR developing a policy analytics dashboard for the prevention of colorectal cancer as part of an integrated platform.

The principal objective served by this article is to identify key literature and provide an overview of the breadth of research in the field of machine learning applications on exposomics data with a focus on cardiovascular diseases. Secondarily, this study aimed at identifying common limitations and meaningful directives to be addressed in the future. Most of the identified literature focuses on Disease Understanding, Prevention, and Management and the remaining on healthcare resource planning and management. Linear, non-linear, and ensemble machine learning methods have been applied, with tree-based methods generally being the most popular, and Random Forests specifically reported as being the best performer most of the time. Non-traditional CVD risk factors spanning behavioural/ lifestyle, socio-economic and a wide range of environmental factors have been investigated in the identified literature. According to our findings, the most reported category is the environmental risk factors which are often considered alone rather than in conjunction with the others. Applications of machine Learning have been a substantial accelerating factor on the field, enabling the analysis of high dimensional data, improving accuracy, investigating novel risk factors and expanding our knowledge on the impact of exposome on cardiovascular diseases. However, several challenges persist, including data heterogeneity and the lack of standardized protocols, which introduces bias and hinders reproducibility and comparability across studies, as well as concerns about the validity of inference when applying machine learning methods to identify associations between exposure and health outcomes. Addressing these issues requires collaborative and interdisciplinary effort to integrate standardized exposome data frameworks akin to those of other –omics fields, apply causal inference methods to validate findings, and further expand the use of Explainable Artificial Intelligence to build insights and enable comparability and understanding.

Colorectal cancer (CRC) is one of the most common cancer types and its increased incidence is related to a number of modifiable risk factors. There is a need for data-driven approaches to support public health policy making for CRC prevention. This paper presents a predictive analytics framework supporting country-level analysis of CRC incidence and risk factors through policy scenarios by linking population-level risk factor exposure to CRC incidence projections across multiple time horizons. The framework integrates Linear Mixed Effects Models, Generalized Additive Models and XGBoost to capture population-level trends and non-linear associations between risk factors and incidence rates. It is intended to serve as the analytical core for scenario-based policy analysis for CRC decision makers. All analyses rely on statistical associations and are designed for exploratory and comparative purposes rather than causal inference or direct policy prescription.

Ovarian cancer remains difficult to detect before dissemination, and individual circulating biomarkers are unlikely to capture its biological and clinical heterogeneity. Multimodal liquid biopsy offers a potential route forward, but most biomarker combinations are developed empirically, with limited consideration of the molecular relationships among the measured signals. In this Perspective, we propose that intracellular miRNA dysregulation may provide an organizing layer for integrating glycoprotein/glycan remodeling with blood-derived volatile organic compound (VOC) signatures, while corresponding circulating miRNA profiles constitute a distinct measurable biomarker layer that may only partially reflect the underlying intracellular regulatory state. The miRNA–glycosylation axis is supported by direct mechanistic evidence that selected miRNAs regulate glycogenes and pathways controlling fucosylation, sialylation, and other glycan features. By contrast, the proposed connection between miRNA dysregulation and VOC production is indirect and likely mediated through metabolic reprogramming, mitochondrial dysfunction, oxidative stress, ferroptosis, and lipid peroxidation. We evaluate the evidence supporting each biomarker domain, prioritize candidate bridge miRNAs, including members of the miR-200 family and miR-34a, and define the principal boundary conditions of the framework, including causal inference, disease specificity, histological heterogeneity, VOC source ambiguity, temporal variability, and cross-platform standardization. We further outline a translational strategy based on matched biospecimens, mechanism-aware computational integration, experimental perturbation, longitudinal analysis, and independent validation at clinically relevant specificity thresholds. The objective is not to assert a fixed causal chain, but to provide a testable, clinically oriented framework for moving multimodal ovarian cancer diagnostics from statistical combination to biologically informed integration.

BACKGROUND There is an emerging need for evidence-based approaches harnessing large amounts of health care data and novel technologies (such as artificial intelligence) to optimize public health policy making. OBJECTIVE The aim of this review was to explore the data analytics tools designed specifically for policy making in noncommunicable diseases (NCDs) and their implementation. METHODS A scoping review was conducted after searching the PubMed and IEEE databases for articles published in the last 10 years. RESULTS Nine articles that presented 7 data analytics tools designed to inform policy making for NCDs were reviewed. The tools incorporated descriptive and predictive analytics. Some tools were designed to include recommendations for decision support, but no pilot studies applying prescriptive analytics have been published. The tools were piloted with various conditions, with cancer being the least studied condition. Implementation of the tools included use cases, pilots, or evaluation workshops that involved policy makers. However, our findings demonstrate very limited real-world use of analytics by policy makers, which is in line with previous studies. CONCLUSIONS Despite the availability of tools designed for different purposes and conditions, data analytics is not widely used to support policy making for NCDs. However, the review demonstrates the value and potential use of data analytics to support policy making. Based on the findings, we make suggestions for researchers developing digital tools to support public health policy making. The findings will also serve as input for the European Union–funded research project ONCODIR developing a policy analytics dashboard for the prevention of colorectal cancer as part of an integrated platform.