激光生物学报, 2020, 29 (6): 506, 网络出版: 2021-02-05  

一种用于活体检测模拟黑色素瘤边界的高度集成化的智能光纤光谱仪

In vivo Detection of the Margin of Simulated Melanoma Based on a Highly Integrated and Intelligent Fiber Optic Spectrometer
作者单位
广州医科大学基础医学院生物医学工程系,广州 511436
摘要
为了探索一种智能、无损检测黑色素瘤边界的方法,开发了一套具有紧凑结构和自动化软件的光纤光谱仪系统,并将其应用于皮肤色素沉积的鉴别。从样品中采集散射光谱后,用标准光谱进行相关测试,系统会根据相关系数自动给出判断。首先通过体外试验验证光纤光谱仪(FOS)系统快速智能检测色素沉积的可行性。用FOS 系统对一个红、绿、蓝三色墨水染色的琼脂模型进行光谱测量,其鉴定准确率达96%。进一步用FOS 系统检测墨水皮下注射构建的小鼠黑色素瘤模型,结果显示,墨水注射区与正常区光谱有显著差异。与标准墨水的光谱比对,墨水注射区域的相关系数为0.83±0.07,而正常组织的相关系数为0.18±0.05。从相关系数构建的图上可以清楚地识别出皮肤中墨水着色区域的边界。体内和体外试验都表明,FOS 系统对皮肤沉积色素的检测具有较高的敏感性和特异性,预示着其在黑色素瘤的临床诊断中具有广阔的应用前景。
Abstract
In order to explore an intelligent, non-destructive method for detecting the boundary of melanoma. In this study, a.ber optic spectrometer (FOS) system combined an integrated structure with an automatic software was developed and em-ployed for distinguishing the skin pigmentation. After collecting scattering spectrum from sample, a correlation test will be performed with standard spectrum. The system will automatically give a judgment based on the correlation coe.cient. The feasibility of the FOS system for rapid and intelligent detecting the pigmentation was.rst tested by an ex vivo experiment. A tricolor agar phantom stained by red, green and blue inks could be identified by the FOS system with 96% accuracy. Further-more, a melanoma mouse model induced by subcutaneous injection of ink was examined by the FOS system in vivo. There were signi.cant di.erences in spectrum between the ink-injection region and the normal region. Comparison with the stan-dard spectrum of injected ink, the correlation coefficient of the ink injection area was 0.83±0.07, while the normal tissue was 0.18±0.05. The boundary of the ink pigmentation area in the skin could be clearly identi.ed from the correlation coe.-cient map. Both the ex vivo and in vivo experiment demonstrated the FOS system could identify pigmentation with high sen-sitivity and speci.city, and indicated its great potential to be used in the clinical diagnosis of melanoma.
参考文献

[1] GRAY-SCHOPFER V, WELLBROCK C, MARAIS R. Melanoma biology and new targeted therapy[J]. Nature, 2007, 445(7130): 851-857.

[2] UONG A, ZON L I. Melanocytes in development and cancer[J]. Journal of Cellular Physiology, 2010, 222(1): 38-41.

[3] TSAO H, CHIN L, GARRAWAY L A, et al. Melanoma: from mutations to medicine[J]. Genes & Development, 2012, 26(11): 1131-1155.

[4] OLGA W H, MONIKA S, MALGORZATA O, et al. Melanoma of the oral cavity: pathogenesis, dermoscopy, clinical features, stag-ing and management[J]. Journal of Dermatological Case Reports, 2014, 8(3): 60-66.

[5] SOLARI N, GIPPONI M, STELLA M, et al. Predictive role of preoperative lymphoscintigraphy on the status of the sentinel lymph node in clinically node-negative patients with cutaneous melanoma[J]. Melanoma Research, 2009, 19(4): 243-251.

[6] BRAY F, FERLAY J, SOERJOMATARAM I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortal-ity worldwide for 36 cancers in 185 countries[J]. CA: A Cancer Journal for Clinicians, 2018, 68(6): 394-424.

[7] REBECCA L S, KIMBERLY D M, AHMEDIN J. Cancer statistics,2018[J]. CA: A Cancer Journal for Clinicians, 2018, 68(1): 7-30.

[8] SHI K, ZHU X, LIU Z, et al. Clinical characteristics of malignant melanoma in central China and predictors of metastasis[J]. On-cology Letters, 2020, 19(2): 1452-1464.

[9] THANH D N H, PRASATH V B S, HIEU L M, et al. Melanoma skin cancer detection method based on adaptive principal curva-ture, colour normalisation and feature extraction with the ABCD rule[J]. Journal of Digital Imaging, 2019, 33(3): 574-585.

[10] PELLACANI G, CESINARO A M, SEIDENARI S. Re.ectance-mode confocal microscopy of pigmented skin lesions-improvement in melanoma diagnostic specificity[J]. Journal of the American Academy of Dermatology, 2005, 53(6): 979-985.

[11] PELLACANI G, CESINARO A M, SEIDENARI S. In vivo as-sessment of melanocytic nests in nevi and melanomas by reflec-tance confocal microscopy[J]. Modern Pathology, 2005, 18(4): 469-474.

[12] PELLACANI G, WITKOWSKI A, CESINARO A M, et al. Cost-benefit of reflectance confocal microscopy in the diagnostic per-formance of melanoma[J]. Journal of the European Academy of Dermatology & Venereology, 2016, 30(3): 413-419.

[13] MENZIES S W, KREUSCH J, BYTH K, et al. Dermoscopic eval-uation of amelanotic and hypomelanotic melanoma[J]. Archives of Dermatology, 2008, 144(9): 1120-1127.

[14] PIZZICHETTA M A, TALAMINI R, STANGANELLI I, et al. Amelanotic/hypomelanotic melanoma: clinical and dermoscopic features[J]. British Journal of Dermatology, 2004, 150(6): 1117-1124.

[15] ELBAUM M, KOPF A W, RABINOVITZ H S, et al. Automatic differentiation of melanoma from melanocytic nevi with multi-spectral digital dermoscopy: a feasibility study[J]. Journal of the American Academy of Dermatology, 2001, 44(2): 207-218.

[16] HANS S, LIGIA T, MANFRED F, et al. Limitations of dermosco-py in the recognition of melanoma[J]. Archives of Dermatology, 2005, 141(2): 155-160.

[17] SVAASAND L O, SPOTT T, FISHKIN J B, et al. Reflectance measurements of layered media with di.use photon-density waves: a potential tool for evaluating deep burns and subcutaneous lesions[J]. Physics in Medicine & Biology, 1999, 44(3): 801-813.

[18] BLANCO M, COELLO J, EUSTAQUIO A, et al. Development and validation of a method for the analysis of a pharmaceutical preparation by near-infrared di.use re.ectance spectroscopy[J]. Journal of Pharmaceutical Sciences, 1999, 88(5): 551-556.

[19] LAU D P, HUANG Z, LUI H, et al. Raman spectroscopy for opti-cal diagnosis in normal and cancerous tissue of the nasopharynx-preliminary findings[J]. Lasers in Surgery & Medicine, 2003,32(3): 210-214.

[20] HEINTZELMAN D L, UTZINGER U, FUCHS H, et al. Optimal excitation wavelengths for in vivo detection of oral neoplasia us-ing fluorescence spectroscopy[J]. Photochemistry & Photobiol-ogy, 2000, 72(1): 103-113.

[21] STONE N, KENDALL C, SHEPHERD N, et al. Near-infrared Raman spectroscopy for the classi.cation of epithelial pre-cancers and cancers[J]. Journal of Raman Spectroscopy, 2002, 33(7): 564-573.

[22] RAMANUJAM N. Fluorescence spectroscopy of neoplastic and non-neoplastic tissues[J]. Neoplasia, 2000, 2(1): 89-117.

[23] MOVASAGHI Z, REHMAN S, REHMAN I U. Raman spectrosco-py of biological tissues[J]. Applied Spectroscopy Review, 2007,42(5): 493-541.

[24] MOURANT J R, BIGIO I J, BOYER J, et al. Spectroscopic diag-nosis of bladder cancer with elastic light scattering[J]. Lasers in Surgery & Medicine, 1995, 17(4): 350-357.

[25] PAN D, XUN M, LAN H, et al. Selective, sensitive, and fast deter-mination of S-layer proteins by a molecularly imprinted photonic polymer coated.lm and a.ber-optic spectrometer[J]. Analytical and Bioanalytical Chemistry, 2019, 411(29): 7737-7745.

[26] WANG Y, HAN M, WANG A. High-speed.ber-optic spectrometer for signal demodulation of inteferometric.ber-optic sensors[J]. Optics Letters, 2006, 31(16): 2408-2410.

[27] PETR H, CIPRIAN D. Birefringence dispersion in a quartz crystal retrieved from a channeled spectrum resolved by a fiber-optic spectrometer[J]. Optics Communications, 2011, 284(12): 2683-2686.

[28] CHOUDHARY R, BOWSER T J, WECKLER P, et al. Rapid esti-mation of lycopene concentration in watermelon and tomato puree by fiber optic visible reflectance spectroscopy[J]. Postharvest Biology & Technology, 2009, 52(1): 103-109.

[29] CEN H, LU R. Optimization of the hyperspectral imaging-based spatially-resolved system for measuring the optical proper-ties of biological materials[J]. Optics Express, 2010, 18(16): 17412-17432.

[30] LI H, HE G, GUO Q. Similarity retrieval method of organic mass spectrometry based on the Pearson correlation coefifcient[J]. Chemical Analysis and Meterage, 2015, 24(3): 33-37.

[31] FENG C, ZHAO N, YIN G, et al. Recognition of waterborne pathogens based on spectral similarity analysis[J]. Acta Optica Sinica, 2020, 40(3): 200-206.

李腾, 陆兆荣, 李治, 邱婷, 蓝银涛, 陈天彬, 湘, 傅洪波, 张建. 一种用于活体检测模拟黑色素瘤边界的高度集成化的智能光纤光谱仪[J]. 激光生物学报, 2020, 29(6): 506. LI Teng, LU Zhaorong, LI Zhi, QIU Ting, LAN Yintao, CHEN Tianbin, XIANG Xiang, FU Hongbo, ZHANG Jian. In vivo Detection of the Margin of Simulated Melanoma Based on a Highly Integrated and Intelligent Fiber Optic Spectrometer[J]. Acta Laser Biology Sinica, 2020, 29(6): 506.

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