本文選題：侵襲性垂體腺瘤 + DCE-MRI��； 參考：《大連醫(yī)科大學》2017年碩士論文

【摘要】：目的:應用DCE-MRI參數(shù)(Ktrans值、Ve值、Kep值)直方圖對侵襲性與非侵襲性垂體腺瘤進行全域定量分析,并探究其與Ki-67、p53之間的相關性,評估DCE-MRI參數(shù)直方圖對垂體腺瘤侵襲性的診斷價值。材料與方法:收集經(jīng)手術及病理證實的37例垂體腺瘤患者的臨床資料、術前MRI資料、術后病理和p53、Ki-67資料,按Knosp分級分為侵襲組(18例)與非侵襲性組(19例)。MRI掃描采用美國GE SignaHDXT3.0T MR機及配套的8通道頭顱線圈檢查,掃描序列包括垂體常規(guī)平掃和動態(tài)增強(Dynamic Contrast Enhanced MRI,DCE-MRI),DCE-MRI掃描采用LAVA-Flex序列,連續(xù)掃描30期。將DCE-MRI的DICOM格式原始數(shù)據(jù)拷貝至個人電腦,使用Omni-Kinetics軟件進行后處理。測量腫瘤最大徑;參照冠狀位T1WI圖像,在包含腫瘤的每一個層面上沿腫瘤邊緣手動描繪ROI,當腫瘤包繞頸內(nèi)動脈時避開頸內(nèi)動脈。將所有層面的ROI累加為一個3D ROI,軟件將自動計算出Ktrans值、Ve值、Kep值的直方圖及其所有參數(shù),包括:最小值、最大值、平均值、第5百分位數(shù)、第10百分位數(shù)、第25百分位數(shù)、第50百分位數(shù)、第75百分位數(shù)、第90百分位數(shù)、第95百分位數(shù)、值域、標準差、方差、平均差、相對差、偏度、峰度、一致性、能量值及熵值。應用社會科學統(tǒng)計軟件包(statistics package for social science,SPSS)18.0 版進行數(shù)據(jù)分析:當數(shù)據(jù)符合正態(tài)分布且方差齊性時,以"均數(shù)士標準差"表示,應用單因素方差分析與兩獨立樣本t檢驗,不符合正態(tài)分布者以"中位數(shù)士四分位間距"表示,采用Kruskal-Wallis檢驗與Mann-Whitney U檢驗,分析侵襲組及非侵襲組腫瘤的最大徑、Knosp分級、Ki-67、p53及Ktrans值、Ve值、Kep值直方圖參數(shù)的差異性;應用Spearman相關性分析knosp分級與ki-67、p53之間的相關性,Ktrans值、Ve值、Kep值直方圖參數(shù)與垂體腺瘤knosp分級、與ki-67、p53之間的相關性;利用接受者操作特性(receiver operating characteristic,ROC)曲線來確定腫瘤最大直徑、Ki-67、p53、Ktrans值、Ve值、Kep值直方圖參數(shù)對二者的診斷能力;應用Logistic回歸分析模型得到的聯(lián)合變量,并利用ROC曲線來確定對二者的診斷能力。p0.05為差異有統(tǒng)計學意義。結果:1.侵襲性與非侵襲性垂體腺瘤Ktrans值直方圖參數(shù)中最大值、平均值、第50百分位數(shù)、第75百分位數(shù)、第90百分位數(shù)、第95百分位數(shù)、值域、平均差、偏度、一致性、能量值、熵值在兩組間有顯著差異(P=0.016,0.002,0.025,0.010,0.006,0.003,0.012,0.008,0.025,0.012,0.028,0.024);利用平均數(shù)、第 95百分位數(shù)、第90百分位數(shù)鑒別侵襲性與非侵襲性垂體腺瘤ROC曲線的效能較好,AUC 分別為 0.792、0.784、0.766;2.侵襲性與非侵襲性垂體腺瘤Ve值直方圖參數(shù)中最大值、平均值、第95百分位數(shù)、值域、標準差、方差、相對差、偏度、峰度、一致性、能量值、熵值在兩組間有顯著差異(P=0.006,0.036,0.023,0.013,0.029,0.027,0.005,0.042,0.013,0.022,0.033,0.046);利用相對差、最大值、值域鑒別二者的效能較好,AUC 分別為 0.772、0.766、0.741;3.侵襲性與非侵襲性垂體腺瘤Kep值直方圖參數(shù)中第90百分位數(shù)、第95百分位數(shù)、值域、相對差、偏度在兩組間有顯著差異(P=0.031,0.013,0.042,0.005,0.023);利用相對差、偏度、第95百分位數(shù)鑒別二者的效能較好,AUC分別為0.769、0.740、0.737;4.Ktrans值平均數(shù)、Ve值相對差、Kep值相對差融合成的聯(lián)合變量1,及聯(lián)合變量1與腫瘤最大徑融合成的聯(lián)合變量2,通過ROC曲線鑒別侵襲性與非侵襲性垂體腺瘤的AUC分別為0.877、0.942;5.垂體腺瘤knosp分級與ki-67、p53之間具有較好的相關性(r=0.547,P0.001;r=0.617,P0.001);6.垂體腺瘤DCE-MRI參數(shù)直方圖中Ktrans值直方圖平均值與Knosp分級的相關性最佳(r=0.660,p0.001),Kep值直方圖峰度與Ki-67的相關性最佳(r=0.746,p0.001),Ktrans值直方圖平均差與p53的相關性最佳(r=0.388,p=0.018)。結論:1.DCE-MRI參數(shù)直方圖可反映侵襲性垂體腺瘤微血管異質(zhì)性,有助于侵襲性垂體腺瘤和非侵襲性垂體腺瘤的鑒別;2.聯(lián)合應用DCE-MRI參數(shù)直方圖及腫瘤最大徑可提高對垂體腺瘤侵襲性的診斷;3.垂體腺瘤Kep值直方圖峰度、Ktrans值直方圖平均差分別與Ki-67、p53有.一定相關性,可以作為監(jiān)測病情演變的影像學指標。
[Abstract]:Objective: to use DCE-MRI parameters (Ktrans, Ve, Kep) histogram for quantitative analysis of invasive and non-invasive pituitary adenomas, and to explore the correlation between Ki-67 and p53, and to evaluate the diagnostic value of DCE-MRI parameter histogram for pituitary adenoma invasiveness. Materials and methods: 37 cases of pituitary adenoma confirmed by surgery and pathology were collected. The patients' clinical data, preoperative MRI data, postoperative pathology and p53, Ki-67 data were divided into invasive group (18 cases) and non invasive group (19 cases) with.MRI scan of American GE SignaHDXT3.0T MR machine and 8 channel head coils. The scan sequence included routine plain scan and dynamic enhancement (Dynamic Contrast Enhanced MRI). E-MRI), DCE-MRI scan uses LAVA-Flex sequence and continuously scan 30 phases. Copy the DICOM original data of DCE-MRI to personal computer, use Omni-Kinetics software for post processing. Measure the maximum diameter of tumor; refer to the coronary T1WI image, manually depict ROI on the edge of the tumor at each level of the tumor and move around the neck around the neck. Pulse to avoid the internal carotid artery. Add all levels of ROI into a 3D ROI, and the software will automatically calculate the Ktrans, Ve, Kep value histogram and all the parameters, including the minimum, maximum, average, fifth percentile, tenth percentile, twenty-fifth percentile, fiftieth percentile, seventy-fifth percentile, ninetieth percentile, ninety-fifth 100 Quantile, range, standard deviation, variance, mean deviation, mean deviation, relative deviation, deviation, kurtosis, consistency, energy value and entropy. Data analysis is carried out by the 18 edition of statistics package for social science, SPSS): when the data conforms to normal distribution and the variance is homogeneous, the single factor variance is applied to the single factor variance. The analysis and two independent sample t test did not conform to the normal distribution with the "median interval of four points". Kruskal-Wallis test and Mann-Whitney U test were used to analyze the maximum diameter, Knosp classification, Ki-67, p53 and Ktrans value, the difference of the Ve value and Kep value histogram parameters of the invasive and non invasive groups; Spearman correlation analysis kn was used. The correlation between OSP classification and Ki-67, p53, Ktrans value, Ve value, Kep value histogram parameters and the knosp classification of pituitary adenoma, the correlation with Ki-67, p53, using the receiver operating characteristics (receiver operating characteristic) curve to determine the maximum diameter of the tumor. Breaking ability; using the Logistic regression analysis model, and using the ROC curve to determine the difference in the diagnostic ability of the two, the difference was statistically significant. Results: 1. the maximum, average, fiftieth percentile, seventy-fifth percentile, ninetieth percentile, ninth of the invasive and non invasive pituitary adenoma's histogram parameters. 500 quantiles, range, mean difference, bias, consistency, energy value, entropy value were significantly different among the two groups (P=0.016,0.002,0.025,0.010,0.006,0.003,0.012,0.008,0.025,0.012,0.028,0.024); the effectiveness of using average, ninety-fifth percentile, and ninetieth percentile to identify invasive and noninvasive pituitary adenomas was better, AUC was 0.792,0.784,0.766; 2. the maximum, average, ninety-fifth percentile, range, standard deviation, variance, relative difference, deviation, kurtosis, conformance, energy value and entropy value were significant differences between the two groups (P= 0.006,0.036,0.023,0.013,0.029,0.027,0.005,0.042,0.013,0.022,0.033,0.046) in invasive and noninvasive pituitary adenomas. The effectiveness of the two persons with relative difference, maximum value and range identification was better, AUC was 0.772,0.766,0.741, and ninetieth percentile, ninety-fifth percentile, relative difference and bias between the two groups of invasive and non invasive pituitary adenomas were significantly different between the two groups (P =0.031,0.013,0.042,0.005,0.023); relative deviation, bias, ninety-fifth were used. The effectiveness of the percentile identification of the two was better, AUC was 0.769,0.740,0.737, the average number of 4.Ktrans, the relative difference of Ve, the combined variable of Kep, the combined variable of the combined variable 1 and the maximum diameter of the tumor were 2, and the AUC of the invasive and non invasive pituitary adenomas was identified by the ROC curve, and the 5. pituitary gland respectively. There was a good correlation between the knosp classification of adenoma and Ki-67, p53 (r=0.547, P0.001; r=0.617, P0.001). 6. the correlation between the mean value of the Ktrans value histogram in the DCE-MRI parameter histogram of the pituitary adenoma and the Knosp classification was the best (r=0.660, p0.001). The best correlation with p53 (r=0.388, p=0.018). Conclusion: the histogram of 1.DCE-MRI parameters can reflect the microvascular heterogeneity of invasive pituitary adenomas and is helpful for the identification of invasive pituitary adenomas and non-invasive pituitary adenomas; 2. the combined use of the DCE-MRI parameter histogram and the maximum diameter of the tumor can improve the diagnosis of invasive pituitary adenomas; 3. pituitary glands. Tumor Kep histogram kurtosis and Ktrans histogram average difference were correlated with Ki-67 and p53 respectively. They could be used as imaging indicators for monitoring the evolution of the disease.

【學位授予單位】：大連醫(yī)科大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：R736.4;R445.2

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