Fast and Objective Histopathology by multimodal

Fast and Objective Histopathology by multimodal

Fast and Objective Histopathology by
multimodal tissue auto-fluorescence
and
Raman Spectroscopy
Ioan Notingher
School of Physics and Astronomy,
University of Nottingham, UK
Tissue conserving surgery
Frederic Edward Mohs (1936)
Fixation, sectioning, staining
Mohs micrographic surgery
1-2 hours
30-60 minutes
2500 secondary
reoperations per
year
Frozen-section
histopathology
1-2 hours
Spectral
histopatholog
y
Intra-operative diagnostics
Challenges:
1. Objective diagnosis (not based on subjective
interpretation of an image by specialised staff).
2. Diagnosis accuracy similar/higher to the current rates of
agreement among histopathologists
3. Fast diagnosis of large tissue samples (3-5 cm), with
high spatial resolution (50-100m)
4. Cost-effective diagnosis
XYZ
Stage
Molecular Fingerprints
Intensity
Sample
Raman Shift cm-1
SPECTRA
CCD
DATABASE
(Classification
Model)
Microscope
% match
L
A
S
E
R
Spectrograph
CLASS
(LABEL)
Raman Spectral Imaging
(raster scanning)
Classification
Model
Class A
Class B
Class C
Raman spectroscopy for diagnosis of BCC
ROC
(BCC vs all other classes)
DATABASE: 550,000 spectra (from 55 patients)
Independent validation (target 95% sensitivity):
220,000 spectra (22 patients)
Cross-validation:
100% sensitivity, 93% specificity
Can we use Raman spectroscopy to do histopathology
for skin sections?
BCC
RMS Diagnosis
20
20
40
40
Muscle
M
60
Fat
60
60
Fat
F
80
Unknown
80
80
Unknown
U
100
Dermis
100
100
Dermis
D
120
Inflammation
120
120
Inflammation
In
140
140
160
160
Epidermis
E
180
180
160
200
Epidermis
Substrate
50
100
150
200
200
50
400 μm
Muscle Fat
B
Muscle
140
BCC
BCC
40
180
H&E histopathology
20
Dermis Infl. D. Epid. Substr. Unkn.
100
150
200
200
Substrate
50
S
100
150
200
Can we use Raman spectroscopy to do histopathology
for thick resections?
Nodular BCC Superficial BCC
Infiltrative BCC
(Scale bars: 400 μm)
Healthy tissue
Raman spectroscopy for diagnosis breast tumours
(ductal carcinoma)
(b)
(a)
(e)
(d)
(c)
DC NST
20
40
Inflamatory. Stroma
60
Fat
80
1 mm
100
Stroma
120
140
Lobules and Ducts
160
180
Substrate
200
20
40
60
80
100
120
140
160
180
200
Raster scanning
BCC
Muscle
Fat
Unknown
Dermis
Inflammation
Raman
Model
Epidermis
Substrate
1mm
For 1×1cm2 tissue sample
20 μm spatial resolution
250,000 spectra !!!!
(2 s/spectrum => 5.78 days)
Multimodal tissue auto-fluorescence and Raman
Spectroscopy
Looking for plum
tomatoes…
I cannot find…
Ideal technique:
-high spatial resolution
-- very fast
-- does NOT need high specificity for BCC
Have you
checked the fresh
vegetables stand
???!
1600 points
Multimodal tissue auto-fluorescence and Raman
Spectroscopy
2×2mm2
100 × longer !!!
Scale bar:
2mm
Kong et al PNAS 2013 110 (38), 15189-15194
Receiver operating characteristic
(BCC versus all other classes)
Scale bar:
2mm
Scale bar: 2mm
PNAS 2013, 110 (38), 15189-15194
Diagnosis of BCC for un-sectioned tissue layers
Confocal autofluorescence
Segmented image
MSH diagnosis image
BCC
100
Muscle
200
Fat
Dermis
300
Inflamed D.
400
Epidermis
500
Substrate
Unknown.
600
Scale bar: 2mm
100
200
300
400
500
600
Diagnosis of BCC for un-sectioned tissue layers
BCC
500
100
BCC
Muscle
1000
Muscle
1500
Fat
200
Fat
2000
Unknown
Dermis
300
2500
Dermis
3000
400
Inflammation
500
Substrate
Unknown.
Epidermis
Inflamed D.
Epidermis
3500
4000
4500
600
5000
500
1000
1500
2000
2500
PNAS 2013, 110 (38), 15189-15194
3000
3500
100
200
300
400
500
Substrate
Conclusions
Anaesthetic Excision Tissue sectioning, staining Subjective diagnosis
Stop
Frozen-section
histopathology
YES
Clear ?
NO
45-120 min
5-15 min
1-2 hours
Anaesthetic
Excision
Objective Diagnosis
Stop
YES
Clear ?
Spectral
histopatholog
y
NO
2-5 min
Acknowledgments
Contributors
Adrian Ghita
Fazliyana Faabar
Dr Marta Larraona-Puy
Dr Chris Rowlands
Dr Kenny Kong
Prof Hywel Williams
Prof Ian Ellis
Dr William Perkins Department of Dermatology
Dr Sandeep Varma
Dr Iain Leach
Dr Emad Rakha
Dr Alexey Koloydenko
Funding:
School of Molecular
Medical Sciences, UoN
Department of Pathology
Queens Medical Centre
Nottingham University Hospital
NHS Trust
Mathematics Department,
Royal Holloway University
of London