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appendix 2: Cutaneous melanoma (2): eUpdate published online September 2016 (http://www.esmo.org/Guidelines/Melanoma).附录2:皮肤黑色素瘤(2):2016年9月在线发布的e更新内容(http://www.esmo.org/Guidelines/Melanoma)
Ann Oncol. 2016 Sep;27(suppl 5):v136-v137. doi: 10.1093/annonc/mdw432.
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Noninvasive, label-free, three-dimensional imaging of melanoma with confocal photothermal microscopy: Differentiate malignant melanoma from benign tumor tissue.利用共焦光热显微镜对黑色素瘤进行无创、无标记的三维成像:区分恶性黑色素瘤与良性肿瘤组织。
Sci Rep. 2016 Jul 22;6:30209. doi: 10.1038/srep30209.
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Stage-specific survival and recurrence in patients with cutaneous malignant melanoma in Europe - a systematic review of the literature.欧洲皮肤恶性黑色素瘤患者的阶段特异性生存和复发——文献系统综述
Clin Epidemiol. 2016 May 26;8:109-22. doi: 10.2147/CLEP.S99021. eCollection 2016.
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Inflammation-Induced Plasticity in Melanoma Therapy and Metastasis.炎症诱导的黑色素瘤治疗和转移中的可塑性。
Trends Immunol. 2016 Jun;37(6):364-374. doi: 10.1016/j.it.2016.03.009. Epub 2016 Apr 15.
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The burden of malignant melanoma--lessons to be learned from Austria.恶性黑色素瘤的负担——从奥地利汲取的经验教训
Eur J Cancer. 2016 Mar;56:45-53. doi: 10.1016/j.ejca.2015.11.026. Epub 2016 Jan 20.
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Cancer statistics, 2016.癌症统计数据,2016 年。
CA Cancer J Clin. 2016 Jan-Feb;66(1):7-30. doi: 10.3322/caac.21332. Epub 2016 Jan 7.
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Methodology for diagnosing of skin cancer on images of dermatologic spots by spectral analysis.通过光谱分析对皮肤斑点图像进行皮肤癌诊断的方法。
Biomed Opt Express. 2015 Sep 9;6(10):3876-91. doi: 10.1364/BOE.6.003876. eCollection 2015 Oct 1.
8
Cutaneous melanoma: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up.皮肤黑色素瘤:ESMO诊断、治疗及随访临床实践指南
Ann Oncol. 2015 Sep;26 Suppl 5:v126-32. doi: 10.1093/annonc/mdv297.
9
Comparison of Efficacy of Differing Partner-Assisted Skin Examination Interventions for Melanoma Patients: A Randomized Clinical Trial.不同伴侣辅助皮肤检查干预措施对黑色素瘤患者的疗效比较:一项随机临床试验。
JAMA Dermatol. 2015 Sep;151(9):945-51. doi: 10.1001/jamadermatol.2015.0690.
10
Practical application of new technologies for melanoma diagnosis: Part II. Molecular approaches.黑色素瘤诊断新技术的实际应用:第二部分。分子方法。
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黑色素瘤细胞的识别:一种基于特征谱密度的平均方差的方法。

Identification of melanoma cells: a method based in mean variance of signatures via spectral densities.

作者信息

Guerra-Rosas Esperanza, Álvarez-Borrego Josué, Angulo-Molina Aracely

机构信息

Centro de Investigación Científica y de Educación Superior de Ensenada (CICESE), División de Física Aplicada, Departamento de Óptica, Carretera Ensenada-Tijuana No. 3918, Fraccionamiento Zona Playitas, C.P. 22860, Ensenada, Baja California, Mexico.

Departamento de Ciencias Químico Biológicas, Universidad de Sonora (UNISON), Luis Encinas y Rosales S/N, Col. Centro, C.P. 83000, Hermosillo, Sonora, Mexico.

出版信息

Biomed Opt Express. 2017 Mar 15;8(4):2185-2194. doi: 10.1364/BOE.8.002185. eCollection 2017 Apr 1.

DOI:10.1364/BOE.8.002185
PMID:28736664
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5516832/
Abstract

In this paper a new methodology to detect and differentiate melanoma cells from normal cells through 1D-signatures averaged variances calculated with a binary mask is presented. The sample images were obtained from histological sections of mice melanoma tumor of 4 [Formula: see text] in thickness and contrasted with normal cells. The results show that melanoma cells present a well-defined range of averaged variances values obtained from the signatures in the four conditions used.

摘要

本文提出了一种新方法,通过使用二进制掩码计算的一维签名平均方差来检测黑色素瘤细胞并将其与正常细胞区分开来。样本图像取自厚度为4[公式:见正文]的小鼠黑色素瘤肿瘤组织切片,并与正常细胞进行对比。结果表明,在所用的四种条件下,黑色素瘤细胞呈现出从签名中获得的明确平均方差值范围。