Advancing Colorectal Cancer Diagnosis with AI-Powered Breathomics: Navigating Challenges and Future Directions.
Ioannis K GallosDimitrios TryfonopoulosGidi ShaniAngelos AmditisHossam HaickDimitra D DionysiouPublished in: Diagnostics (Basel, Switzerland) (2023)
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.
Keyphrases
- artificial intelligence
- big data
- machine learning
- early stage
- deep learning
- colorectal cancer screening
- current status
- loop mediated isothermal amplification
- quality improvement
- primary care
- label free
- systematic review
- papillary thyroid
- real time pcr
- randomized controlled trial
- healthcare
- cardiovascular events
- cardiovascular disease
- squamous cell carcinoma
- squamous cell
- risk assessment
- climate change
- coronary artery disease
- type diabetes
- lymph node
- adipose tissue