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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5528_Библиотеки_им_академика_М_И_Перельмана.pdf
300 S. Kudose & A. M. Amacher
(a)
(b)
(c)
(g) (h) (i)
(d)
(e)
(f)
Figure 1. (a–c) The lesions shown are clearly visible in WL and show clearly increasedfluorescence.
(d) A lesion that, although it is visible in WL, has enhanced visibility in FL. (e, f) Images representative
of the nine lesions that were only visible in FL. Polyps are indicated by the white arrows. (a) 2-cm
pedunculated (Paris 0-Ip) tubular adenoma. (b) 4-cm subpedunculated (Paris 0-lps) tabulovillous
adenoma. (c) 2-cm sessile (Paris 0-Is) serrated polyp. (d) 5-mm flat elevated (Paris 0-IIa) tubular
adenoma. (e, f) Flat (Paris 0-IIb) tubular adenoma as 5 and 4 mm in diameter, respectively. (g) Graph
showing the relationship. Adapted from Nat Med. 2015 Aug; 21(8): 955–961.
In some cases, endogenous chromophores can be used to provide contrastwithout the use of exogenousagents. These agentsinclude hemoglobin,
melanin, and lipid. For some applicationssuch as visualization of microvasculature,
11,12
a high-resolution image, down to a label of single red blood
cell, can be obtained using photoacoustic tomography, without the use of
Molecular Applications 301
(a)
(c)
Figure 2. Photoacoustic imaging of endogenous chromophores. Adapted from Ref. [12].
(b)
any labels (Figure 2). Such a technique may be used to study wound healing and tumor–vasculature interactions. Using melanin as a chromophore,
the thickness of melanoma could be measured via photoacoustic tomog-
12,13
raphy
and may be useful in specialized setting such as in ophthalmologic evaluation of intraocular mass, where tissue sampling may not be
desirable.
14
A study by Stoffels et al.15illustrate a potential use of melanin as an
endogenous contrast agent to detect involved sentinel lymph node using
multispectral optoacoustic tomography (MSOT). In this study, authors
explored sentinel lymph node status in both in vivo and ex vivo fashions.
In the in vivo portion of their study, the authors compared detection rates
of sentinel lymph nodes using MSOT with indocyanine green acting as an
exogenous contrast agent to a standard procedure using SPECT/CT and
radioactive agent
99
Tc. In a cohort of 20 patients, they found that MSOT
coupled with an experimental integrated ultrasound device can visualize a ll
lymph nodes non-invasively down to a depth of 50 mm, with a concordance
rate of 100% compared to a standard protocol.
302 S. Kudose & A. M. Amacher
In the ex vivo portion of the study, patients undergoing sentinel lymph
node biopsy were divided into two cohorts. One cohort (n = 149) had their
lymph nodes examined pathologically using a standard EORTC Melanoma
Group protocol,
16
i.e. a lymph node was bivalved and each of the halves
was examined in six serial sections 50 µm apart, stained with H&E and
S100 and Melan-A. Lymph nodes from the other cohort (n = 65) underwent additional targeted sampling using MSOT beyond those required by
EORTC protocol (Figure 3). In particular, MSOT was performed on tissue
fixed for 24h and dissected away from the fat. Melanin and hemoglobin
were used as endogenous contrast agents. MSOT-guided sampling had no
false negativeresult with a sensitivity of 100% but had many false positives
with a specificity of only 62.3%. Causes of false positivesincluded capsular
nevi, hemorrhage, and melanophages. Since more sampling was performed
in the MSOT cohort, it is not surprising that more involved sentinel nodes
were found in this group (22.9% vs. 14.2%). However, this study nicely
illustrates the potential utility of MSOT technology in the processing of
sentinel lymph nodes as well as potential contributions from pathologists
in the design of such study and, perhaps,in an interpretationof these images.
Furthermore, intrinsic physicochemical properties of tissues and their
differential emission in response to electromagnetic waves may be manipulated to produce false-colored images that resemble traditional histochemical stains, using a neural network. A neural network is one of the
machine learning techniques that has been applied particularly successfully
in image-based applications such as image recognition and classification.
Recently, this technique has been applied in pathology, ranging from the
detection of tumor cells in lymph nodes or mitotic figures.
17
One of the applications of convolutional neural networks is a prediction of plausible color based on greyscale images. Using a similar a pproach,
Mayerich et al. and others
18,19
trained a neural network, to predict staining for hematoxylin and eosin, trichrome and immunohistochemical stains,
including cytokeratin and smooth muscle actin, on unstained slides from
98 patients using Fourier transform infrared spectroscopy.The training was
performed on unstained microarray sections of formalin-fixed paraffinembedded tissues from 96 patients, and corresponding serial sections
stained with the above histochemical and immunohistochemical stains.
(a)
(b)
(c)
Figure 3. Comparison of SLN analysis of patients with and without metastasis. (a) MIP of a lateral view of a patient where histology did
not reveal a metastasis. The melanin distribution, depicted along the lateral axiz, suggested sampling positions based on signal intensity for histopathology
marked as dotted lines in the plot. The third column shows an example cross section, with a histological section stained for MelanA at low and hi gh
magnifications in the fourth column. (b) MSOT and histology for a patient with high melanin signal in the MSOT scan, but with no metastasis detected by
histology. However, a structure resembling a nevus is marked by red arrows in the low-magnification histology image, whereas individual melanophages
are highlighted in the high magnification — both explaining the false-positive signals determined by MSOT analysis. (c) Images of an SLN that was positive
for MelanA and was predicted by MSOT. Sci Transl Med. 2015 Dec 9, 7(317): 317ra199.
Molecular Applications 303
304 S. Kudose & A. M. Amacher
Figure 4. An example of virtual staining using a convolutional neural network. Adapted from Ref.
[19].
Validation was performed in an independent set of 98 patients. Although
performance metrics were not provided in this study, predicted staining
resembled actually stained slides according to the provided figures. Such
approaches may be used to provide images more familiar to pathologists
in the future (Figure 4), if such virtual staining can be produced in a timely
manner in in vivo setting, i.e. in non-fixed tissues that are not sectioned.
The use of these probes coupled with in vivo microscopy is still largely
at the proof-of-principle stage. Because these probes are typically tested
initially in ex vivo setting due to obvious regulatory hurdles involved especially in using exogenous probes, pathologists are well situated to both
assist in the design of these studies and to provide logistical support in
carrying out these studies. In particular, pathologic examinations, using
traditional staining methods, still serve as a gold standard for establishing
the diagnosis of most neoplastic entities, and, therefore, pathologists are
well situated to design new criteria based on morphologic features in new
microscopic modalities. Furthermore, pathologists may be able to help in
the design of validation studies by providing insights into disease processes
at more granular levels. For example, while pathologists are keenly aware
Molecular Applications 305
of potential interobservervariability in grading dysplasia in esophagus,
20,21
as well a possibility of dysplasia buried beneath a re-epithelialized surface
squamous epithelium, such considerations may not be obvious to some
scientists and engineers who are more familiar with in vivo microscopy.
10
When applied in ex vivo setting, these imaging modalities may be used
to facilitate adequacy assessment, allocation of tissue for further clinical
and research study, or margin assessment.
22
Lastly, pathologists with an
understanding of current workflow and clinical standards are well poised
to suggest ways in which these technologies can be integrated into clinical
practice in a useful and non-disruptive way so that they can be beneficial
to both patients and practitioners in the future.
References
1. Horobin, R. W., Biological staining: Mechanisms and theory. Biotech Histochem,
77(1): 3–13 (2002).
2. Resch-Genger, U., et al., Quantum dots versus organic dyes as fluorescent labels.
Nature Methods, 5(9): 763–775 (2008).
3. Karstensen, J. G., et al., Molecular confocal laser endomicroscopy: A novel tech-
nique for in vivo cellular characterization of gastrointestinal lesions. World Journal
Gastroenterol, 20(24): 7794–800 (2014).
4. Yi, X., et al., Near-infrared fluorescent probes in cancer imaging and therapy: An
emerging field. International Journal Nanomedicine, 9: 1347–1365 (2014).
5. Zackrisson, S., van de Ven, S. and Gambhir, S., Light in and sound out: Emerging
translational strategies for photoacoustic imaging. Cancer Research, 74(4): 979–1004
(2014).
6. Panchapakesan, B., et al., Gold nanoprobes for theranostics. Nanomedicine (Lond),
6(10): 1787–1811 (2011).
7. Chinen,A.B.,et al., Nanoparticle probes for the detection of cancer biomarkers, cells,
and tissues by fluorescence. Chemical Reviews, 115(19): 10530–10574 (2015).
8. He, X., et al., Near-infrared fluorescent nanoprobes for cancer molecular imaging:
Status and challenges. Trends Molecular Medicine, 16(12): 574–583 (2010).
9. Burggraaf, J., et al., Detection of colorectal polyps in humans using an intravenously
administered fluorescent peptide targeted against c-Met. Nature Medicine, 21(8):
955–961 (2015).
10. Sturm, M. B., et al., Targeted imaging of esophageal neoplasia with a fluorescently
labeled peptide: First-in-human results. Science Translational Medicine, 5(184):
184ra61 (2013).
306 S. Kudose & A. M. Amacher
11. Hu, S. and Wang, L. V. Photoacoustic imaging and characterization of the microvasculature. Journal Biomedical Optics, 15(1): 011101 (2010).
12. Weber, J., Beard, P. C., and Bohndiek, S. E. Contrast agents for molecular photoacoustic imaging. Nature Methods, 13(8): 639–650 (2016).
13. Oh,J.T.,et al., Three-dimensional imaging of skin melanoma in vivo by dual-
wavelength photoacoustic microscopy. Journal of Biomedical Optics, 11(3): 34032
(2006).
14. Xu, G., et al., Photoacoustic imaging features of intraocular tumors: Retinoblastoma
and uveal melanoma. PLoS One, 12(2): e0170752 (2017).
15. Stoffels, I., et al., Metastatic status of sentinel lymph nodes in melanoma determined noninvasively with multispectral optoacoustic imaging. Science Translational
Medicine, 7(317): 317ra199 (2015).
16. van Akkooi, A. C., et al., High positive sentinel node identification rate by EORTC
melanoma group protocol. Prognostic indicators of metastatic patterns after sentinel
node biopsy in melanoma. European Journal of Cancer, 42(3): 372–380 (2006).
17. Roux, L., et al., Mitosis detection in breast cancer histological images An ICPR 2012
contest. Journal of Pathology Informatics, 4: 8 (2013).
18. Mayerich, D., et al., Stain-less staining for computed histopathology. Technology
(Singapore World Scientific), 3(1): 27–31 (2015).
19. Rivenson, Y., et al., PhaseStain: The digital staining of label-free quantitative phase
microscopy images using deep learning. Light: Science & Applications, 8: 23 (2019).
20. Montgomery, E., et al., Reproducibility of the diagnosis of dysplasia in Barrett esophagus: A reaffirmation. Human Pathology, 32(4): 368–378 (2001).
21. Vennalaganti, P., et al., Discordance among pathologists in the United States and
Europe in diagnosis of low-grade dysplasia for patients with Barrett’s esophagus.
Gastroenterology, 152(3): 564–570 e4, (2017).
22. Hariri, L. P., In vivo microscopy: Will the microscope move from our desk into the
patient? Archives of Pathology & Laboratory Medicine, 139(6): 719–720 (2015).
© 2024 World Scientific Publishing Company
https://doi.org/10.1142/9789813206984_0016
Ex Vivo
Applications Chapter
16
Daffolyn Rachael Fels Elliott∗,
and Anne Marie Amacher
Introduction
Ex vivo microscopy involves real-time imaging of human tissue that has
been excisedfrom the patientto obtain diagnostic pathological information.
Ex vivo microscopy enables rapid assessment of tissue specimens without
the need to section, freeze and stain the tissue using conventional histology.
Potential clinical applications for ex vivo microscopy include evaluation of
surgical margins for the presence of tumor, determining sentinel lymph
node involvement by malignancy, assessing the adequacy of small biopsies, and obtaining high-yield sections for histologic processing (Table 1).
Ex vivo microscopic imaging technologies aim to rapidly scan a relatively
large surface area of tissue at an adequate depth of tissue penetration while
maximizing the image resolution. This chapter will discuss how these
†
∗
Clinical Assistant Professor, Department of Pathology, The University of Kansas Health
System, Kansas City, KS, USA.
†
Pathology Department, SSM DePaul Health Center, Bridgeton, MO, USA.
307
Table 1. Summary of clinical applications for ex v ivo microscopic imaging technologies.
Clinical application Technical considerations Ex vivo microscopy techniques
308 D. R. Fels Elliott & A. M. Amacher
Intraoperative
evaluation of
margins in breast
conservation surgery
Intraoperative
evaluation of
margins in Mohs
micrographic
surgery
• Aim: to differentiate malignant from benign
breast tissue (positive or negative result)
• Imaging requirements:
◦ Minimum 1–2 mm depth of penetration to
determine distance of tumor from margin
◦ High resolution to detect small tumor foci
◦ Rapid production of images
intraoperatively
• Aim: to differentiate malignant from benign
skin tissue (positive or negative result)
• Imaging requirements:
◦ High resolution to differentiate tumor
from benign adnexal structures
◦ Rapid production of images
intraoperatively
• Radiofrequency spectroscopy
(MarginProbe
is FDA-approved)
14
• Optical spectroscopy
◦ Diffuse reflectance spectroscopy
19,21
(DRS)
◦ Broadband reflectance spectroscopy
24
◦ Multimodal optical spectroscopy (DRS
and intrinsic fluorescence spectroscopy)
• Raman spectroscopy
• Optical coherence tomography (OCT)
25, 27,28
30,31
• Multimodal imaging (OCT and confocal
microscopy)
• Confocal laser scanning microscopy
32
36
◦ Reflectance-mode confocal
microscopy
37–39
◦ Fluorescence confocal microscopy
35,40
(FCM)
◦ Multimodal confocal microscopy
47,48
• OCT
45,46
22
Intraoperative
evaluation of
sentinel lymph nodes
Rapid evaluation of
biopsy adequacy
• Aim: to identify metastatic tumor and
enable surgeons to proceed to regional
lymph node dissection
• Imaging requirements:
◦ High specificity to minimize false
positives
◦ High resolution to detect small tumor foci
◦ Rapid production of images
intraoperatively
• Aim: to identify and semi-quantify tumor
.
and/or microcalcifications
• Imaging requirements:
◦ High resolution to detect small tumor foci
◦ Rapid production of images during
procedure
• Optical spectroscopy
• Raman spectroscopy
51,52
• OCT
49
50
• DRS (breast microcalcifications
• FCM (inflammatory breast cancer
• OCT (prostate cancer,
57
tumors
)
56, 58
kidney
Ex Vivo
53
)
54
)
Applications 309