早期发现、早期诊断、早期治疗是提高恶性肿瘤治愈率、延长患者生命的关键，将肿瘤标志物的检测作为恶性肿瘤早期辅助诊断的依据一直是临床肿瘤学的热点之一。肿瘤特异性生长因子(Tumor Specific Growth Factor，TSGF)作为一种新的肿瘤...早期发现、早期诊断、早期治疗是提高恶性肿瘤治愈率、延长患者生命的关键，将肿瘤标志物的检测作为恶性肿瘤早期辅助诊断的依据一直是临床肿瘤学的热点之一。肿瘤特异性生长因子(Tumor Specific Growth Factor，TSGF)作为一种新的肿瘤标志物已应用于临床。我们应用TSGF检测试剂对101例恶性肿瘤患者、展开更多
Background Hepatocellular carcinoma tends to present at a late clinical stage with poor prognosis. Therefore, it is urgent to explore and develop a simple, rapid diagnostic method, which has high sensitivity and speci...Background Hepatocellular carcinoma tends to present at a late clinical stage with poor prognosis. Therefore, it is urgent to explore and develop a simple, rapid diagnostic method, which has high sensitivity and specificity for hepatocellular carcinoma at an early stage. In this study, the serum proteins in patients with hepatocellular carcinoma or liver cirrhosis and in normal controls were analysed. Surface enhanced laser desorption/ionization time-of-flight mass （SELDI-TOF-MS） spectrometry was used to fingerprint serum protein using the protein chip technique and explore the value of the fingerprint, coupled with artificial neural network, to diagnose hepatocellular carcinoma. Methods Of the 106 serum samples obtained, 52 were from patients with hepatocellular carcinoma, 22 from patients with liver cirrhosis and 32 from healthy volunteers. The samples were randomly assigned into a training group （n = 70, 35 patients with hepatocellular carcinoma, 14 with liver cirrhosis, and 21 normal controls） and a testing group （n = 36, 17 patients with hepatocellular carcinoma, 8 with liver cirrhosis, and 11 normal controls）. An artificial neural network was trained on data from 70 individuals in the training group to develop an artificial neural network diagnostic model and this model was tested. The 36 sera in the testing group were analysed with blind prediction by using the same flowchart and procedure of data collection. The 36 serum protein spectra were clustered with the preset clustering method and the same mass/charge （M/Z） peak values as those in the training group. Matrix transfer was performed after data were output. Then the data were input into the previously built artificial neural network model to get the prediction value. The M/Z peaks of the samples with more than 2000 M/Z were normalized with biomarker wizard of ProteinChip Software version 3. 1 for noise filtering. The first threshold for noise filtering was set at 5, and the second was set at 2. The 10% was the minimum threshold for clu展开更多
文摘早期发现、早期诊断、早期治疗是提高恶性肿瘤治愈率、延长患者生命的关键，将肿瘤标志物的检测作为恶性肿瘤早期辅助诊断的依据一直是临床肿瘤学的热点之一。肿瘤特异性生长因子(Tumor Specific Growth Factor，TSGF)作为一种新的肿瘤标志物已应用于临床。我们应用TSGF检测试剂对101例恶性肿瘤患者、
文摘Background Hepatocellular carcinoma tends to present at a late clinical stage with poor prognosis. Therefore, it is urgent to explore and develop a simple, rapid diagnostic method, which has high sensitivity and specificity for hepatocellular carcinoma at an early stage. In this study, the serum proteins in patients with hepatocellular carcinoma or liver cirrhosis and in normal controls were analysed. Surface enhanced laser desorption/ionization time-of-flight mass （SELDI-TOF-MS） spectrometry was used to fingerprint serum protein using the protein chip technique and explore the value of the fingerprint, coupled with artificial neural network, to diagnose hepatocellular carcinoma. Methods Of the 106 serum samples obtained, 52 were from patients with hepatocellular carcinoma, 22 from patients with liver cirrhosis and 32 from healthy volunteers. The samples were randomly assigned into a training group （n = 70, 35 patients with hepatocellular carcinoma, 14 with liver cirrhosis, and 21 normal controls） and a testing group （n = 36, 17 patients with hepatocellular carcinoma, 8 with liver cirrhosis, and 11 normal controls）. An artificial neural network was trained on data from 70 individuals in the training group to develop an artificial neural network diagnostic model and this model was tested. The 36 sera in the testing group were analysed with blind prediction by using the same flowchart and procedure of data collection. The 36 serum protein spectra were clustered with the preset clustering method and the same mass/charge （M/Z） peak values as those in the training group. Matrix transfer was performed after data were output. Then the data were input into the previously built artificial neural network model to get the prediction value. The M/Z peaks of the samples with more than 2000 M/Z were normalized with biomarker wizard of ProteinChip Software version 3. 1 for noise filtering. The first threshold for noise filtering was set at 5, and the second was set at 2. The 10% was the minimum threshold for clu
