前列腺癌病人的组织拉曼光谱检测与分析
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摘要
前列腺癌是人类健康的杀手,前列腺癌的早期检测诊断、合理的手术方案以及有效的药物治疗都有助于前列腺癌的治疗。选取24例前列腺良性增生、7例低危前列腺癌、21例高危前列腺癌的组织进行拉曼测量,对拉曼光谱减去荧光背底并平均,利用主成分分析-线性判别分析法对3组不同病理情况的前列腺组织的拉曼光谱进行分类。实验结果表明,拉曼光谱结合主成分分析-线性判别分析法可以对不同病理情况的前列腺组织进行分类,其准确度为94.2%。还对前列腺良性增生和前列腺癌、低危前列腺癌和高危前列腺癌分别进行分析分类。其中前列腺良性增生和前列腺癌的分类准确度为91.7%,低危前列腺癌和高危前列腺癌的分类准确度为100%。研究结果显示前列腺组织的拉曼光谱具有应用于前列腺癌诊疗的临床应用前景。
Prostate cancer is a killer of human health, early detection, reasonable surgery and effective drug for therapy are essential for the therapy of the prostate cancer. The Raman spectra are acquired from tissues of 24 cases of benign prostate hyperplasia(BPH), 7 cases low-grade prostate cancer(PCa) and 21 cases of high-grade prostate cancer. The Raman spectra which are deducted fluorescence back and averaged, and classified by the principal component analysis-linear discriminant analysis(PCA-LDA) algorithm. The experimental results show that the prostate tissues of three different pathological conditions classified with PCA-LDA algorithm and the accuracy is 94.2%. Additionally, BPH and PCa, low-grade and high-grade prostate cancer are classified respectively. The accuracy of classification between BPH and PCa is 91.7%, and the accuracy of classification between low-grade PCa and high-grade PCa is 100%. It's indicated that the Raman spectroscopy of tissue have the potential clinical application in diagnose of prostate tumors.
Prostate cancer is a killer of human health, early detection, reasonable surgery and effective drug for therapy are essential for the therapy of the prostate cancer. The Raman spectra are acquired from tissues of 24 cases of benign prostate hyperplasia(BPH), 7 cases low-grade prostate cancer(PCa) and 21 cases of high-grade prostate cancer. The Raman spectra which are deducted fluorescence back and averaged, and classified by the principal component analysis-linear discriminant analysis(PCA-LDA) algorithm. The experimental results show that the prostate tissues of three different pathological conditions classified with PCA-LDA algorithm and the accuracy is 94.2%. Additionally, BPH and PCa, low-grade and high-grade prostate cancer are classified respectively. The accuracy of classification between BPH and PCa is 91.7%, and the accuracy of classification between low-grade PCa and high-grade PCa is 100%. It's indicated that the Raman spectroscopy of tissue have the potential clinical application in diagnose of prostate tumors.
引文
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[3] CAGRI O G, RAMAZAN K, ALI K M, et al. The role of transcavitary ultrasonography in diagnosis and staging of nonmuscle-1nvasive bladder cancer: A prospective non-randomized clinical study[J]. Springerplus, 2014, 3(1): 519.
[4] WOLLIN DA, DENG F M, HUANG W C, et al. Conventional and diffusion-weighted MRI features in diagnosis of metastatic lymphadenopathy in bladder cancer[J]. Canadian Journal of Urology, 2014, 21(5): 7454-7459.
[5] DI P P, VARGAS H A, KARLO C A, et al. Intradiverticular bladder cancer: CT imaging features and their association with clinical outcomes [J]. Clinical Imaging, 2014, 39(1): 94-98.
[6] LOPEZ A, LIAO J C. Emerging endoscopic imaging technologies for bladder cancer detection[J]. Current Urology Reports, 2014, 15(15): 1-8.
[7] LERNER SP, GOH A. Novel endoscopic diagnosis for bladder cancer[J]. Cancer, 2014, 121(2): 169-178.
[8] WITJES J A, GOMELLA L G, STENZL A,et al. Safety of hexaminolevulinate for blue light cystoscopy in bladder cancer. A combined analysis of the trials used for registration and postmarketing data[J]. Urology, 2014, 84(1): 122-126.
[9] ONAL B, HAN ü, YILMAZ S, et al. The use of urinary nuclear matrix protein 22 (NMP22) as a diagnostic adjunct to urine cytology for monitoring of recurrent bladder cancer-institutional experience and review[J]. Diagnostic Cytopathology, 2014, 43(4): 307-314.
[10] LIU Y, HUANG L Q, WANG J,et al. Fabrication of sliver ordered nanoarrays SERS-Active substrates and their application in bladdr cancer cells detection[J]. Spectroscopy and Spectral Analysis, 2012, 32(2): 386-390.
[11] ENGERS R. Resproducibility and reliability of tumor grading in urological neoplasms[J]. World Journal of Urology, 2007, 25(6): 595-605.
[12] 郝凤龙. 基于拉曼光谱技术的毒品检测仪器研究[J]. 国外电子测量技术, 2016, 35(12): 40-43.
[13] SHAO X,PAN J, WANG Y, et al. Evaluation of expressed prostatic secretion and serum using surface-enhanced Raman spectroscopy for the noninvasive detection of prostate cancer, a preliminary study[J]. Nanomedicine, 2017, 13(3): 1051-1059.
[14] LI S, ZHANG Y,XU J, et al. Noninvasive prostate cancer screening based on serum surface-enhanced Raman spectroscopy and support vector machine[J]. Applied Physics Letters, 2014, 105(9): 091104-091104-4.
[15] LOPES R M, SILVEIRA JR L, SILVA M A R S,et al. Diagnostic model based on Raman spectra of normal, hyperplasia and prostate adenocarcinoma tissues in vitro[J]. Spectroscopy, 2011, 25(2): 89-102.
[16] MOVASAGHI Z, REHMAN S. REHMAN I U. Raman spectroscopy of biological tissues[J]. Applied Spectroscopy Reviews, 2007, 42(5): 493-541.
[17] GRIGNON D J, SAKR W, TOTH M, et al. High levels of tissue inhibitor of metalloproteinase-2 (TIMP-2) expression are associated with poor outcome in invasive bladder cancer[J]. Cancer Research, 1996, 56(7): 1654.
[18] MOAZZAMI A A, ZHANG J X, KAMALELDIN A, et al. Nuclear magnetic resonance-based metabolomics enable detection of the effects of a whole grain rye and rye bran diet on the metabolic profile of plasma in prostate cancer patients[J]. Journal of Nutrition, 2011, 141(12): 32-2126.