NDLI: Infrared spectral histopathology using haematoxylin and eosin (H&E) stained glass slides: a major step forward towards clinical translation

Content Provider	Royal Society of Chemistry (RSC)
Author	Shanks, Jonathan H. Gardner, Peter Brown, Michael D. Pilling, Michael J. Henderson, Alex Clarke, Noel W.
Copyright Year	2017
Abstract	Infrared spectral histopathology has shown great promise as an important diagnostic tool, with the potential to complement current pathological methods. While promising, clinical translation has been hindered by the impracticalities of using infrared transmissive substrates which are both fragile and prohibitively very expensive. Recently, glass has been proposed as a potential replacement which, although largely opaque in the infrared, allows unrestricted access to the high wavenumber region (2500–3800 cm−1). Recent studies using unstained tissue on glass have shown that despite utilising only the amide A band, good discrimination between histological classes could be achieved, and suggest the potential of discriminating between normal and malignant tissue. However unstained tissue on glass has the potential to disrupt the pathologist workflow, since it needs to be stained following infrared chemical imaging. In light of this, we report on the very first infrared Spectral Histopathology SHP study utilising coverslipped H&E stained tissue on glass using samples as received from the pathologist. In this paper we present a rigorous study using results obtained from an extended patient sample set consisting of 182 prostate tissue cores obtained from 100 different patients, on 18 separate H&E slides. Utilising a Random Forest classification model we demonstrate that we can rapidly classify four classes of histology of an independent test set with a high degree of accuracy (>90%). We investigate different degrees of staining using nine separate prostate serial sections, and demonstrate that we discriminate on biomarkers rather than the presence of the stain. Finally, using a four-class model we show that we can discriminate normal epithelium, malignant epithelium, normal stroma and cancer associated stroma with classification accuracies over 95%.
Starting Page	1258
Ending Page	1268
Page Count	11
File Format	HTM / HTML PDF
ISSN	00032654
Volume Number	142
Issue Number	8
Journal	The Analyst
DOI	10.1039/c6an02224c
Language	English
Publisher	Royal Society of Chemistry
Access Restriction	Open
Subject Keyword	Histopathology Infrared Amide SHP Random forest Histology Stromal cell Epithelium Prostate Wavenumber Cancer
Content Type	Text
Resource Type	Article
Subject	Spectroscopy Environmental Chemistry Analytical Chemistry Biochemistry Electrochemistry

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