Towards automated cancer screening: Label-free classification of fixed cell samples using wavelength modulated Raman spectroscopy.
Lana WoolfordMingzhou ChenKishan DholakiaC. Simon HerringtonPublished in: Journal of biophotonics (2018)
The ability to provide quantitative, objective and automated pathological analysis would provide enormous benefits for national cancer screening programmes, in terms of both resource reduction and improved patient wellbeing. The move towards molecular pathology through spectroscopic methods shows great promise, but has been restricted by spectral quality, acquisition times and lack of direct clinical application. In this paper, we present the application of wavelength modulated Raman spectroscopy for the automated label- and fluorescence-free classification of fixed squamous epithelial cells in suspension, such as those produced during a cervical smear test. Direct comparison with standard Raman spectroscopy shows marked improvement of sensitivity and specificity when considering both human papillomavirus (sensitivity +12.0%, specificity +5.3%) and transformation status (sensitivity +10.3%, specificity +11.1%). Studies on the impact of intracellular sampling location and storage effects suggest that wavelength modulated Raman spectroscopy is sufficiently robust to be used in fixed cell classification, but requires further investigations of potential sources of molecular variation in order to improve current clinical tools.
Keyphrases
- raman spectroscopy
- deep learning
- machine learning
- label free
- single cell
- artificial intelligence
- high throughput
- big data
- cell therapy
- single molecule
- molecular docking
- computed tomography
- magnetic resonance imaging
- magnetic resonance
- optical coherence tomography
- squamous cell carcinoma
- risk assessment
- drinking water
- quality improvement
- young adults
- reactive oxygen species
- molecular dynamics simulations
- human health
- energy transfer
- case control
- data analysis