Raman spectral imaging for automated Mohs Micrographic surgery of Basal Cell Carcinoma


Automated Detection and Imaging of Tumours During Surgery


Automated imaging and objective diagnosis of excised tissue specimens during cancer surgery would lead to increasing the efficacy of the most advanced surgical procedure. We have developed a model which enables Raman Micro-Spectroscopy (RMS) to be used as an alternative to histopathology to produce an automated and objective method for evaluation of skin tissue.  Although the proposed technology currently focuses on non-melanoma skin cancer can in principle be used during the surgery of many tissue types. Basal Cell Carcinoma (BCC) constitutes about 80% of diagnosed skin cancers. For aggressive BCCs, Mohs Micrographic Surgery (MMS) is considered the most suitable treatment. Its main disadvantage is the need of frozen section preparation and histopathology examination for all excised tissues, a non-automated, time-consuming technique. These drawback lead to an inequitable healthcare provision in the UK, as there are currently only 8 MMS centres compared to approximately 80 recommended by the National Institute for Health and Clinical Excellence.

Researchers at University of Nottingham in collaboration with the Dermatology Department at Nottingham University Hospital have demonstrated that Raman Micro-Spectroscopy (RMS) can be used as an alternative to histopathology to produce an automated and objective method for evaluation of skin tissues.

Key Benefits

The key benefits of this technology are:

    ·  Fast (within 5 minutes) analysis of tissue samples without the requirement of staining
    ·  Quantitative and Qualitative analysis of tissue and non-melanoma skin cancer through developed model
    ·  Ability to undertake analysis during operating theatre using a small, bench top instrument

IP Status

A patent application has been filed by the University of Nottingham (priority date 13th May, 2009) in order to protect the technology. - Publication number WO 2010/131045

Technical Information

RMS is a pure optical technique in which the “molecular fingerprint” of tissue is acquired by using a laser to illuminate the sample and ultra-sensitive detectors to measure the innelastically scattered light. The main advantage of this technology compared to other techniques, including optical methods (fluorescence, elastical scattering, coherent optical tomography, etc) is its high chemical sensitivity. Subtitle molecular modification in the tissue, such as increase density of nucleic acids and decrease amount of collagen, can be accurately detected and used for quantitative automated imaging.

A multivariate supervised statistical model has been developed using tissue specimens obtained during MMS and skin cancer surgery to discriminate BCC from healthy tissue with 90±9 % sensitivity and 85±9% specificity. The model was built using 329 Raman spectra measured from skin specimens from 20 patients. This multivariate model was then applied on tissue sections obtained from new patients with the aim of imaging tumour regions. The RMS image showed excellent correlation with the golden-standard of hispathology images, BCC being detected in all positive sections. These images demonstrate the potential of RMS for an automated objective method for tumour evaluation during MMS. The replacement of current histopathology during MMS by a ‘generalization’ of the proposed technique may improve the feasibility and efficacy of cutting-edge surgical procedures such as MMS, leading to a wider use according to clinical need rather than availability and costs. Typical examples of imaging and automated diagnosis are shown in figures bellow. Colour codes: dark blue=BCC, yellow=dermis, light blue=epidermis, brown=glass substrate.


Figure 1. Detection and automated diagnosis for skin tissue containing nodular BCC (area size: 500µm x 500 µm)


Figure 2. Detection and automated diagnosis of skin tissue containing morphemic BCC (area size: 240µm x 720 µm)




(EN) A new method of Raman microspectroscopy for the detection and imaging of Basal Cell Carcinoma (BCC) comprises the application of a multivariate supervised statistical classification model to distinguish between dermis, epidermis and BCC. The resulting Raman images provide a tool for the automated and objective evaluation of a tissue sample.

Patent Information:
For Information, Contact:
Jonathan Gibbons
Senior Licensing Executive - Healthcare
The University of Nottingham
+44 (0) 115 82 32189
Ioan Notingher
Hywel Williams
William Perkins
Sandeep Varma
© 2024. All Rights Reserved. Powered by Inteum