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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5528_Библиотеки_им_академика_М_И_Перельмана.pdf
320 D. R. Fels Elliott & A. M. Amacher
(a)
(b)
Figure 5. Multimodal imaging combining (a) fluorescent confocal microscopy to highlight cell nuclei
with (b) reflectance confocal microscopy to highlight cell cytoplasm and dermis. (c) Digital staining
transforms grayscale reflectance and fluorescence properties into multi-color images that mimic
(d) conventional H&E histology. Image reprinted with permission.
and dermis (Figure 5(a–b)).
45,46
Digital staining can then be applied to
45
(c)
(d)
transform grayscale reflectance and fluorescence properties into multicolor images that mimic conventional H&E histology (Figure 5(c–d)).
45
The digital images are produced by overlaying fluorescence mosaics that
highlight nuclei (digitally stained purple) with reflectance mosaics that
highlight cytoplasm and dermis (digitally stained pink). Gareau et al.
imaged 21 Mohs surgery BCC excisions using multimodal confocal
microscopy combined with digital staining and identified tumor successfully in 17/21 specimens.
46
The four specimens where tumor was missed
by the Mohs surgeon had low tumor burden (image fraction 0.3–2%), and
the digital staining quality was limited by weak acridine orange fluorescence in three cases. Areas of ongoing research to overcome technical
challenges in confocal microscopy include accurate image alignment and
smooth stitching of the confocal mosaics, consistency of fluorescence staining, accommodating irregularities in tissue shape, and further integration
of “strip mosaicing” to image a larger surface area of tissue.
Ex Vivo
Applications 321
Optical coherence tomography
Optical coherence tomography (OCT) has also been proposed for ex vivo
margin evaluation of Mohs surgery specimens, but preliminary results were
relatively disappointing due to challenges for the operator interpreting OCT
images and identifying tumor. Cunha et al. imaged 75 BCC Mohs surgery
sections from 38 patients and showed that OCT images were generated
rapidly in a mean time of 7 min, but tumor proved difficult to recognize
in comparison to frozen H&E sections (sensitivity 19% and specificity
47
56%).
available full-field OCT apparatus (field of view approximately 1 mm
Durkin et al. showed more promising results using a commercially
2
The authors scanned 18 BCC Mohs surgery sections from 11 patients and
showed that a dermatopathologist correctly diagnosed 9 negative sections
and 6 positive sections and deferred 3 sections (which were subsequently
positive for tumor).
48
The authors acknowledged that the diagnostic utility
of OCT was limited by the interpreter’s ability to identify small foci of
tumor particularly in the setting of tissue inflammation.
Intraoperative Evaluation of Sentinel Lymph Nodes
).
The intraoperative evaluation of sentinel lymph nodes for the presence
of tumor has the potential to enable surgeons to proceed immediately to
regional lymph node dissection without requiring a second operation. High
specificity of the test is desirable in this clinical scenario to minimize the
number of false positive results that might prompt unnecessary axillary
dissection. Ex vivo microscopy technologies have been investigated for
this purpose, particularly in breast cancer patients, including optical spectroscopy, Raman spectroscopy, and OCT. For example, Keshtagar et al.
used an optical spectroscopy probe to scan 30 positive axillary lymph nodes
and 331 negative lymph nodes and developed an algorithm to discriminate
between normal tissue and tumor, based on the elastic scattering properties of the tissue.
49
In a validation group of 129 lymph nodes the scanning
device detected macrometastases (greater than 2 mm) with a sensitivity
of 76% and specificity of 96%.
49
Similarly, Horsnell et al. used Raman
322 D. R. Fels Elliott & A. M. Amacher
spectroscopy to examine 59 lymph nodes (16 positive and 43 negative) and
demonstrated a sensitivity of 81% and specificity of 97%.
50
Studies have
also proposed using OCT for the evaluation of lymph nodes in patients
with metastatic breast cancer,
51,52
with the results somewhat limited by
reader interpretation and image artifact. For instance, Grieve et al. used
OCT to scan 71 lymph nodes excised from 38 patients and demonstrated a
sensitivity of 92% and specificity of 83% based on OCT image interpretation by a single pathologist.
51
In an effort to improve the results obtained
from a single OCT interpreter, Nolan et al. proposed a system of majority
52
voting to combine scores from different observers.
The authors used a
mobile intraoperative OCT imaging system to examine lymph nodes from
51 patients (128 lymph nodes) within a strict imaging time of 5–10 min per
patient. OCT interpretation with majority voting predicted the presence of
metastatic disease with a sensitivity of 58.8% and specificity of 81.4%.
Rapid Evaluation of Biopsy Adequacy
Another potential clinical application for ex vivo microscopic imaging is
rapid assessment of adequacy in biopsy samples with complete preservation of tissue for histologic processing. For example, stereotactic breast
biopsies are taken for microcalcifications visualized on mammography,
but in up to 15% of patient samples, microcalcifications are not identified
despite radiographic guidance.
et al. used diffuse reflectance spectroscopy to detect variations in optical
spectra from microcalcifications in biopsy cores from 23 patients undergoing stereotactic biopsy. Through correlation with radiography and histology, the authors developed a robust algorithm to detect microcalcifications
with a positive predictive value of 97% and a negative predictive value
53
of 88%.
Similarly, Dobbs et al. used fluorescence confocal microscopy
(FCM) to assess the adequacy of breast core needle biopsy specimens from
23 patients with a suspected diagnosis of inflammatory breast carcinoma.
Grayscale FCM images were rapidly acquired within 2 min and showed
moderate agreement with histologic sections for estimating the cellularity
of invasive cancer (kappa statistic 0.48). In fresh biopsies taken from the
53
To address this clinical scenario, Soares
54
Ex Vivo
Applications 323
upper gastrointestinal tract, Chiu et al. showed that confocal laser scanning
microscopy could differentiate between malignant lesions and benign gastric or esophageal mucosa with an overallaccuracy of 92.2% (N = 18 specimens).
55
In the genitourinary tract, optical coherence tomography (OCT)
has been investigated for its ability to detect tumor in prostate core biopsies
(diagnostic accuracy 70.6%, N = 38 patients, 119 cores)
56
and kidney
tumor biopsies (diagnostic accuracy 80%, N = 25 nephrectomy speci-
57
mens).
Yan g et al. further proposed a training protocol for OCT assessment of prostate biopsies and showed an improved diagnostic accuracy for
the detection of tumor (mean 93%, two pathologists and one urologist).
58
The feasibility of OCT to determine the adequacy of fine needle aspiration
(FNA) specimens has also been evaluated as a possible alternative to on-
59
site cytologic evaluation by cytotechnologists or pathologists.
This last
example highlights the potential advantage of ex vivo microscopic imaging
as a real-time, non-destructive tool to determine adequacy in biopsies with
scant tissue obtained from sites that are difficult to access.
Conclusion
The ex vivo microscopic imaging techniques described in this chapter show
the potential to improve clinical care for patients in several ways, including
guiding excision of surgical margins in real-time, intraoperative sentinel
lymph node diagnosis, and rapid evaluation of biopsy adequacy to improve
diagnostic yield. The minimal alteration to fresh tissue and lack of frozen
artifact in permanent histologic sections are major benefits of these technologies. Additionally, ex vivo microscopy may help guide pathologists or
pathologist-assistants in the gross room to determine the extent of tumor and
select the closest margins,potentially increasing efficiencyfor the histology
lab and pathologists signing out cases. Limitations of these technologies
include the speed at which images are generated, the need for specialized
training to interpret the images, and failure to achieve diagnostic accuracy
equivalent to permanent FFPE sections. Given these limitations, ex vivo
microscopy may be used as an adjunct to current practices, but routine histologic processing should follow as the gold standard to confirm the final
324 D. R. Fels Elliott & A. M. Amacher
diagnosis. In order to be implemented in clinical practice, such as the FDAapproved MarginProbe
, the majority of ex vivo microscopy technologies
still require multicenter, randomized controlled clinical trials to validate
their clinical utility in relevant patient populations.
References
1. Vogler, N., Heuke, S., Bocklitz, T. W., Schmitt, M., and Popp, J. Multimodal imaging
spectroscopy of tissue. Annual Review of Analytical Chemistry, 8: 359–387 (2015).
2. Ikeda, T., Akiyama, F., Hiraoka, M., Inaji, H., et al. Surgical margin status as a cause
of local failure after breast conserving therapy. Breast Cancer, 6: 93–97 (1999).
3. Chagpar, A. B., Martin, R.C.2., Hagendoorn, L. J., Chao, C., and McMasters, K. M.
Lumpectomy margins are affected by tumor size and histologic subtype but not by
biopsy technique. American Journal of Surgery, 188: 399–402 (2004).
4. Jeevan, R., Cromwell, D. A., Trivella, M., Lawrence, G., et al. Reoperation rates after
breast conserving surgery for breast cancer among women in England: Retrospective
study of hospital episode statistics. British Medical Journal, 345: e4505 (2012).
5. McCahill, L. E., Single, R. M., Aiello Bowles, E. J., Feigelson, H.S., et al. Variability
in reexcision following breast conservation surgery. Journal of the American Medical
Association, 307: 467–475 (2012).
6. Kurniawan, E. D., Wong, M. H., Windle, I., Rose, A., et al. Predictors of surgical
margin status in breast-conserving surgery within a breast screening program. Annals
of Surgical Oncology, 15: 2542–2549 (2008).
7. Houssami, N., Macaskill, P., Marinovich, M. L., Dixon, J. M., et al. Meta-analysis of
the impact of surgical margins on local recurrence in women with early-stage invasive
breast cancer treated with breast-conserving therapy. European Journal of Cancer,
46: 3219–3232 (2010).
8. Thill, M., Baumann, K., and Barinoff, J. Intraoperative assessment of margins in breast
conservative surgery — Still in use? Journal of Surgical Oncology, 110: 15–20 (2014).
9. Landercasper, J., Whitacre, E., Degnim, A. C., and Al-Hamadani, M. Reasons for
re-excision after lumpectomy for breast cancer: Insight from the American Society of
Breast Surgeons Mastery(SM) database. Annals of Surgical Oncology, 21: 3185–3191
(2014).
10. Thill, M. MarginProbe: Intraoperative margin assessment during breast conserving
surgery by using radiofrequency spectroscopy. Expert Review of Medical Devices,
10: 301–315 (2013).
11. Pani, S., Cook, E. J., Horrocks, J. A., Jones, J. L., and Speller, R. D. Characterization of
breast tissue using energy-dispersive X-ray diffraction computed tomography.Applied
Radiation and Isotopes, 68: 1980–1987 (2010).
Ex Vivo
12. Doyle, T. E., Factor, R. E., Ellefson, C. L., Sorensen, K. M., et al. High-frequency
ultrasound for intraoperative margin assessments in breast conservation surgery: A
feasibility study. BMC Cancer, 11: 444 (2011).
13. Tang,R., Buckley,J. M., Fernandez, L., Coopey, S., et al. Micro-computed tomography
(Micro-CT): A novel approach for intraoperative breast cancer specimen imaging.
Breast Cancer Research and Treatment, 139: 311–316 (2013).
14. Thill, M., Dittmer, C., Baumann, K., Friedrichs, K., and Blohmer, J. U. MarginProbe(R) — Final results of the German post-market study in breast conserving
surgery of ductal carcinoma in situ. Breast, 23: 94–96 (2014).
15. Karni, T., Pappo, I., Sandbank, J., Lavon, O., et al. A devicefor real-time, intraoperative
margin assessment in breast-conservation surgery. American Journal of Surgery, 194:
467–473 (2007).
16. Pappo, I., Spector, R., Schindel, A., Morgenstern, S., et al. Diagnostic performance
of a novel device for real-time margin assessment in lumpectomy specimens. Journal
of Surgical Research, 160: 277–281 (2010).
17. Allweis, T. M., Kaufman, Z., Lelcuk, S., Pappo, I., et al. A prospective, randomized,
controlled, multicenter study of a real-time, intraoperative probe for positive margin
detection in breast-conserving surgery. American Journal of Surgery, 196: 483–489
(2008).
18. Schnabel, F., Boolbol, S. K., Gittleman, M., Karni, T., et al. A randomized prospective study of lumpectomy margin assessment with use of MarginProbe in patients
with nonpalpable breast malignancies. Annals of Surgical Oncology, 21: 1589–1595
(2014).
19. Wilke, L. G., Brown, J. Q., Bydlon, T. M., Kennedy, S. A., et al. Rapid noninvasive
optical imaging of tissue composition i n breast tumor margins. American Journal of
Surgery, 198: 566–574 (2009).
20. Bydlon, T. M., Kennedy, S. A., Richards, L. M., Brown, J. Q., et al. Performance
metrics of an optical spectral imaging system for intra-operative assessment of breast
tumor margins. Optics Express, 18: 8058–8076 (2010).
21. Kennedy, S., Caldwell, M., Bydlon, T., Mulvey, C., et al. Correlation of breast tissue
histology and optical signatures to improve margin assessment techniques. Journal of
Biomedical Optics, 21: 66014 (2016).
22. Lue, N., Kang, J. W., Y u, C. C., Barman, I., et al. Portable optical fiber probe-based
spectroscopic scanner for rapid cancer diagnosis: A new tool for intraoperative margin
assessment. PLoS One, 7: e30887 (2012).
23. Laughney, A. M., Krishnaswamy, V., Garcia-Allende, P. B., Conde, O. M., et al. Automated classification of breast pathologyusing local measures of broadband reflectance.
Journal of Biomedical Optics
24. Laughney,A. M., Krishnaswamy,V., Rizzo, E. J., Schwab, M. C., et al. Scatter spectroscopic imaging distinguishes between breast pathologies in tissues relevant to surgical
margin assessment. Clinical Cancer Research, 18: 6315–6325 (2012).
Applications 325
, 15: 066019 (2010).
326 D. R. Fels Elliott & A. M. Amacher
25. Haka, A. S., Shafer-Peltier, K. E., Fitzmaurice, M., Crowe, J., et al. Diagnosing breast
cancer by using Raman spectroscopy. Proceedings of the National Academy of Sci-
ences of the United States of America, 102: 12371–12376 (2005).
26. Haka, A. S., Volynskaya, Z., Gardecki, J. A., Nazemi, J., et al. In vivo margin assessment during partial mastectomy breast surgery using Raman spectroscopy. Cancer
Research, 66: 3317–3322 (2006).
27. Haka, A. S., Volynskaya, Z., Gardecki, J. A., Nazemi, J., et al. Diagnosing breast cancer
using Raman spectroscopy: Prospective analysis. Journal of Biomedical Optics, 14:
054023 (2009).
28. Keller, M. D., Vargis, E., de Matos Granja, N., Wilson, R. H., et al. Development of a
spatially offset Raman spectroscopy probe for breast tumor surgical margin evaluation.
Journal of Biomedical Optics, 16: 077006 (2011).
29. Gambichler, T., Jaedicke, V. and Terras, S. Optical coherence tomography in dermatology: Technical and clinical aspects. Archives of Dermatological Research, 303:
457–473 (2011).
30. Nguyen, F . T ., Zysk, A. M., Chaney, E. J., Kotynek, J. G., et al. Intraoperative evalu-
ation of breast tumor margins with optical coherence tomography. Cancer Research,
69: 8790–8796 (2009).
31. Zysk, A. M., Chen, K., Gabrielson, E., Tafra, L., et al. Intraoperative assessment of
final margins with a handheld optical imaging probe during breast-conserving surgery
may reduce the reoperation rate: Results of a multicenter study. Annals of Surgical
Oncology, 22: 3356–3362 (2015).
32. Zhou, C., Cohen, D. W., Wang, Y., Lee, H. C., et al. Integrated optical coherence
tomography and microscopy for ex vivo multiscale evaluation of human breast tissues.
Cancer Research, 70: 10071–10079 (2010).
33. Lee, H. C., Zhou, C., Cohen, D. W., Mondelblatt, A. E., et al. Integrated optical
coherence tomography and optical coherence microscopy imaging of ex vivo human
renal tissues. The Journal of Urology, 187: 691–699 (2012).
34. Moehrle, M., Kaflein, L., Ziefle, S., and Metzler, G. Rapid lump examination (RLE) A new tool for Mohs surgery? Journal Der Deutschen Dermatologischen Gesellschaft,
9: 34–538 (2011).
35. Bennassar, A., Carrera, C., Puig, S., Vilalta, A., and Malvehy, J. Fast evaluation of 69
basal cell carcinomas with ex vivo fluorescence confocal microscopy: Criteria description, histopathological correlation, and interobserver agreement. JAMA Dermatology,
149: 839–847 (2013).
36. Gareau, D. S., Karen, J. K., Dusza, S. W., Tudisco, M., et al. Sensitivity and specificity
for detecting basal cell carcinomas in Mohs excisions with confocal fluorescence
mosaicing microscopy. Journal of Biomedical Optics, 14: 034012 (2009).
37. Patel, Y. G., Nehal, K. S., Aranda, I., Li, Y., et al. Confocal reflectance mosaicing of
basal cell carcinomas in Mohs surgical skin excisions. Journal of Biomedical Optics,
12: 034027 (2007).
Ex Vivo
38. Chung, V. Q., Dwyer, P. J., Nehal, K. S., Rajadhyaksha, M., et al. Use of ex vivo confocal scanning laser microscopy during Mohs surgery for nonmelanoma skin cancers.
Dermatologic Surgery, 30: 1470–1478 (2004).
39. Ziefle, S., Schule, D., Breuninger, H., Schippert, W., and Moehrle, M. Confocal laser
scanning microscopy vs 3-dimensional histologic imaging in basal cell carcinoma.
Archives of Dermatology, 146: 843–847 (2010).
40. Karen, J. K., Gareau, D. S., Dusza, S. W., Tudisco, M., et al. Detection of basal
cell carcinomas in Mohs excisions with fluorescence confocal mosaicing microscopy.
British Journal of Dermatolog, 160: 1242–1250 (2009).
41. Longo, C., Rajadhyaksha, M., Ragazzi, M., Nehal, K., et al. Evaluating ex vivo fluorescence confocal microscopy images of basal cell carcinomas in Mohs excised tissue.
British Journal of Dermatology, 171: 561–570 (2014).
42. Abeytunge, S., Li, Y., Larson, B., T oledo-Cro w, R. and Rajadhyaksha, M. Rapid
confocal imaging of large areas of excised tissue with strip mosaicing. Journal of
Biomedical Optics, 16: 050504 (2011).
43. Abeytunge, S., Li, Y., Larson, B., Peterson, G., et al. Confocal microscopy with strip
mosaicing for rapid imaging over large areas of excised tissue. Journal of Biomedical
Optics, 18: 61227 (2013).
44. Larson, B., Abeytunge, S., Seltzer, E., Rajadhyaksha, M., and Nehal, K. Detection
of skin cancer margins in Mohs excisions with high-speed strip mosaicing confocal
microscopy:A feasibilitystudy.British Journal of Dermatology, 169: 922–926 (2013).
45. Bini, J., Spain, J., Nehal, K., Hazelwood, V., et al. Confocal mosaicing microscopy
of human skin ex vivo: Spectral analysis for digital staining to simulate histology-like
appearance. Journal of Biomedical Optics, 16: 076008 (2011).
46. Gareau, D. S., Jeon, H., Nehal, K. S., and Rajadhyaksha, M. Rapid screening of cancer
margins in tissue with multimodal confocal microscopy. Journal of Surgical Research,
178: 533–538 (2012).
47. Cunha, D., Richardson, T., Sheth, N., Orchard, G., et al. Comparison of ex vivo optical coherence tomography with conv entional frozen-section histology for visualizing
basal cell carcinoma during Mohs micrographic surgery. British Journal of Derma-
tology, 165: 576–580 (2011).
48. Durkin, J. R., Fine, J. L., Sam, H., Pugliano-Mauro, M., and Ho, J. Imaging of Mohs
micrographic surgery sections using full-field optical coherence tomography: A pilot
study. Dermatologic Surgery, 40: 266–274 (2014).
49. Keshtgar, M. R., Chicken, D. W., Austwick, M. R., Somasundaram, S. K., et al.
Optical scanning for rapid intraoperative diagnosis of sentinel node metastases in
breast cancer. British Journal of Surgery, 97: 1232–1239 (2010).
50. Horsnell, J. D., Smith, J. A., Sattlecker, M., Sammon, A., et al.
A potential new method for the intra-operative assessment of axillary lymph nodes.
Surgeon, 10: 123–127 (2012).
Applications 327
Raman spectroscopy—
328 D. R. Fels Elliott & A. M. Amacher
51. Grieve, K., Mouslim, K., Assayag, O., Dalimier, E., et al. Assessment of sentinel node
biopsies with full-field optical coherence tomography. Technology in Cancer Research
and Treatment, 15: 266–274 (2016).
52. Nolan, R. M., Adie, S. G., Marjanovic, M., Chaney, E. J., et al. Intraoperative optical
coherence tomography for assessing human lymph nodes for metastatic cancer. BMC
Cancer, 16: 144 (2016).
53. Soares, J. S., Barman, I., Dingari, N. C., Volynskaya, Z., et al. Diagnostic power of
diffuse reflectance spectroscopy for targeted detection of breast lesions with microcalcifications. Proceedings of the National Academy of Sciences of the United States
of America, 110: 471–476 (2013).
54. Dobbs, J., Krishnamurthy, S., Kyrish, M., Benveniste, A. P., et al. Confocal fluorescence microscopy for rapid evaluation of invasive tumor cellularity of inflammatory
breast carcinoma core needle biopsies. Breast Cancer Research and Treatment, 149:
303–310 (2015).
55. Chiu, P. W., Inoue, H., Satodate, H., Kazawa, T., et al. Validation of the quality of
histological images obtained of fresh and formalin-fixed specimens of esophageal
and gastric mucosa by laser-scanning confocal microscopy. Endoscopy, 38: 236–240
(2006).
56. Lopater, J., Colin, P., Beuvon, F., Sibony, M., et al. Real-time cancer diagnosis dur-
ing prostate biopsy: Ex vivo evaluation of full-field optical coherence tomography
(FFOCT) imaging on biopsy cores. World Journal of Urology, 34: 237–243 (2016).
57. Jain, M., Robinson, B. D., Salamoon, B., Thouvenin, O., et al. Rapid evaluation of
fresh ex vivo kidney tissue with full-field optical coherence tomography. Journal of
Pathology Informatics, 6: 53 (2015).
58. Yang, C., Ricco, R., Sisk, A., Duc, A., et al. High efficiency for prostate biopsy qualification with full-field OCT after training. SPIE Proceedings, 9689: 96891J (2016).
59. Grieve,K., Palazzo, L., Dalimier, E., Vielh, P., and Fabre, M. A feasibility studyof fullfield optical coherence tomography for rapid evaluation of EUS-guided microbiopsy
specimens. Gastrointestinal Endoscopy, 81: 342–350 (2015).
© 2024 World Scientific Publishing Company
https://doi.org/10.1142/9789813206984_bmatter
Index
A
adenoma, 113–118
B
Barrett’s esophagus, 67–69, 71–73
C
central nervous system, 189–201
cervix, 244–256
chromophore, 300–301
confocal laser endomicroscopy (CLE),
67–70, 189, 194–197, 199, 217,
220–221, 223–225, 284, 289–291, 308,
310, 323
confocal microscopy, 5–6
cornea, 1–8
corneal topography, 1–3
coronary artery disease, 29
D
diagnosis, 55–60
dysplasia, 113–118
E
electrical impedance spectroscopy (EIS),
57, 61–64
EUS-nCLE/pCLE, 132–133, 140–142,
144–149
ex vivo microscopy, 307–310, 321,
323
ex vivo tissues, 156, 158, 161–166
F
fluorescence confocal microscopy,
156–157
full-field optical coherence tomography
(FFOCT), 156, 159, 162
329