Optical Spectroscopy and Imaging for Cancer Diagnostics

The following sections are included:

• Dedication
• Foreword
• Preface
• Contents
• Editor
• Contributing Authors

The following sections are included:

• Introduction
• Hallmarks of cancer: molecular drivers of tumor heterogeneity and optical detection
• Envisioned future state
• References

The following sections are included:

• Light and its applications in optics
• Absorption and emission of electromagnetic radiation
• Lasers
• References

Biomedical optics studies the interaction of light with biological tissues. The reader is already familiar with some interactions such as reflection, refraction, diffraction, and dispersion. Such phenomenon also occurs in biological tissues:

• reflection — or specular reflection — can be observed when imaging the cornea with optical coherence tomography (OCT) as the interface between cornea and air is smooth compared to the scale of resolution of an OCT system (Auksorius and Boccara 2015);
• refraction or the bending of light rays when traversing a boundary between two dielectrics with different refractive indices can also be found to warp OCT images of flow within cylindrical vessels (Villey et al. 2010);
• diffraction or the bending of light from the regular occurrence of wavelength-size structures has been observed when imaging striated (or skeletal) muscle composed of an alternation of actin and myosin filaments spaced by 1.6 microns (Lieber and Friden 1998);
• dispersion or the refractive index variation with wavelength. Dispersion occurs whenever multiple wavelength are present, which means that its management is critical when using ultra-fast pulses to image biological tissues in applications such as nonlinear microscopy (Labroille et al. 2010), or simply large bandwidth sources such as in OCT (Wojtkowski et al. 2004; Attendu et al. 2019b);…

The following sections are included:

• Introduction
• Delivering light while minimizing its loss
• Filtering and dispersing light
• Combining light with light
• Capturing light
• Using lasers in medicine
• Concluding remarks
• References

The following sections are included:

• Introduction
• Mathematical concepts
• Theoretical foundations of machine learning
• Practical aspects of machine learning
• Key machine learning algorithms
• Machine learning software
• References

The following sections are included:

• List of abbreviations
• Introduction
• Instrumentation for polarization measurements and imaging
• Quantitative FPI for the detection of cancer at the cellular level
• FPI for the detection of cancer at the tissue level
• Summary
• References

The following sections are included:

• Introduction
• Immunosensors
• Detection methods
• Laser ablation inductively coupled plasma mass spectrometry
• Laser-induced breakdown spectroscopy
• Summary and future perspectives
• Acknowledgments
• References

The following sections are included:

• Introduction
• FTIR spectroscopy in breast cancer research
• Multivariate analysis of IR spectroscopic data
• Multivariate analysis of IR spectra of blood sera of patients with breast cancer
• Conclusion
• References

The following sections are included:

• Introduction
• Laser-induced breakdown spectroscopy
• Experimental procedures
• Data analysis
• Results and discussion
• Conclusions
• References

The following sections are included:

• Introduction
• Machine learning for Raman spectroscopy
• Recommendations and evaluation of classification-based models built for Raman spectroscopy
• Machine-learning classification for cancer diagnosis
• Conclusions
• References

The following sections are included:

• Introduction
• Principle and detection methods of fluorescence lifetime imaging
• Fluorophores for FLIM measurement
• Application of fluorescence lifetime imaging in cancer diagnosis
• Summary and outlook
• References

A major limiting factor for the complete surgical resection of tumors is the physician’s ability to identify complex tumor margins, with visual and tactile feedback still relied upon during surgery. Tissue autofluorescence has been identified as a viable source of real-time, non-invasive, endogenous, diagnostic contrast without the need for an exogenous contrast agent to be administered. Time-resolved autofluorescence techniques such as Fluorescence Lifetime Imaging (FLIm) have been shown to detect changes in the molecular composition of tissue (matrix proteins, metabolic co-factors), allowing for the discrimination of normal and diseased tissue to be observed for various pathologies including breast cancer and head and neck cancer. Machine learning methods such as random forests, neural networks and support vector machines have been used with a variety of medical imaging modalities (OCT, MRI, PET, ultrasound), leading to superior discrimination for a range of segmentation and classification tasks. A combination of machine learning and a real-time, label-free optical technique such as fluorescence imaging can lead to the development of intraoperative classification and visualization tools for tasks such as tumor margin delineation, assisting the physicians in their assessments and leading to improved outcomes.

The following sections are included:

• Introduction
• Instrumentation
• Preclinical applications: NP distribution
• Medical applications: Help for medical diagnoses
• Conclusion and perspectives
• Acknowledgments
• References

The following sections are included:

• Overview
• Introduction to the Raman effect and Raman imaging
• Machine learning and deep learning for image analysis
• Application of coherent Raman imaging and deep learning to Cancer Research and diagnosis
• Conclusion
• Acknowledgments
• References

The following sections are included:

• Introduction
• Review on existing melanoma detection modalities
• Principle of optical coherence tomography (OCT)
• Quantitative OCT
• Conclusion and future remarks
• References

The following sections are included:

• Introduction
• Phase-shifting white light interference microscopy with color fringe analysis
• Phase shifting fringe analysis
• Label-free phase imaging of breast cancer cells
• Machine learning modal for differentiation of normal and cancerous cells
• Raman spectroscopy for differentiating normal and cancerous cells
• Conclusions and future prospects
• Acknowledgments
• References

The following section is included:

• Index