本文選題：子宮內膜癌　切入點：MRI　出處：《吉林大學》2017年碩士論文　論文類型：學位論文

【摘要】：目的:探討基于整個腫瘤體積的表觀彌散系數(apparent diffusion coefficient,ADC)值直方圖評估子宮內膜癌不同病理組織學特征及鑒別子宮內膜良惡性組織的價值。方法:收集2014年05月~2016年12月在吉林大學第一醫(yī)院診治并且經手術后病理確診的51名子宮內膜癌患者入病例組;30名同一時期患有宮頸癌且術后病理確診子宮內膜正常的患者入對照組。術前所有患者均行常規(guī)磁共振(Magnetic resonance imaging,MRI)序列、彌散加權成像(Diffusion weighted imaging,DWI)以及普通增強(MR contrast enhanced,CE-MR)掃描。將DWI掃描后自動生成的ADC圖原始數據傳輸至后處理軟件(Siemens Syngo),人工繪制ADC圖每一層面感興趣區(qū)(ROI)直至包括整個瘤體或正常子宮體內膜并由后處理軟件自動生成相應層面的ADC值直方圖,進而轉換為頻數分布表輸入SPSS 20.0軟件,重建出整個瘤體或正常子宮內膜的ADC值直方圖進行數據分析。將病例組患者根據術后病理類型及組織學分級、肌層浸潤的深度、有無宮頸間質浸潤分組,比較各組間以及病例組與對照組的ADC值直方圖參數,包括平均值(mean)、第5百分位數(P5)、第10百分位數(P10)、第25百分位數(P25)、第50百分位數(P50)、第75百分位數(P75)、第90百分位數(P90)及第95百分位數(P95)。兩組間比較使用獨立樣本t檢驗,四組間比較使用單因素方差分析,P0.05被認為具有統(tǒng)計學意義。對以上參數進行受試者工作曲線分析(receiver operating characteristic curve,ROC)并計算曲線下面積(area under the ROC curve,AUC),獲取最佳診斷截止值及相應的敏感度、特異度。結果:1.ADC值直方圖參數鑒別病例組不同病理組織學特征的情況:(1)ADC P5、P10、P25值在子宮內膜樣腺癌G1、G2、G3與非子宮內膜樣癌中差異有統(tǒng)計學意義(P0.05);(2)子宮內膜樣腺癌(G1+G2+G3)較非子宮內膜樣癌的ADC P5值減低,差異有統(tǒng)計學意義(P0.05),其ROC曲線下面積AUC=0.654,取最佳截止值為719.00x10-6mm2/s,相應的敏感度、特異度分別為0.29、1.00;(3)深肌層浸潤較淺肌層浸潤的ADC mean、P5、P10、P25以及P50值均降低,差異有統(tǒng)計學意義(P0.05),其中ADC P5值鑒別腫瘤侵犯肌層深度的效能最高,其AUC=0.725,取最佳截止值為653.10x10-6mm2/s,相應的敏感度為0.51,特異度為1.00;(4)宮頸間質浸潤陰性與陽性兩組間的ADC值直方圖各觀察指標差異均無統(tǒng)計學意義(P0.05)。2.病例組與對照組的ADC mean、P_5、P_(10)、P_(25)、P_(50)、P_(75)、P_(90)及P_(95)值差異均有統(tǒng)計學意義(P0.05),其中P90的診斷效能最高,AUC=0.987,取最佳截止值為1438.90x10~(-6)mm~2/s,相應的敏感度、特異度分別為0.97、0.94。結論:1.基于整個腫瘤體積的ADC值直方圖有助于預測子宮內膜癌高危因素患者,如子宮內膜樣腺癌G3、非子宮內膜樣癌以及深肌層浸潤,但對于有無宮頸間質浸潤則無明顯差異。2.ADC值直方圖有助于術前鑒別子宮內膜癌與正常子宮內膜,其中第90百分位數被認為是最有意義的參數。3.ADC值直方圖可以對病變進行量化分析,為臨床醫(yī)生提供更精準的信息。
[Abstract]:Objective: To investigate the apparent diffusion coefficient of the tumor volume table (apparent diffusion coefficient ADC, based on histogram) assessment of endometrial carcinoma of different pathological features and differential diagnosis of primary endometrial benign and malignant tissue. Methods: from 2014 05 months ~2016 year in December in No.1 Hospital of Jilin University and by surgical pathology confirmed 51 endometrial cancer patients the case group; 30 the same period with cervical cancer and postoperative pathological diagnosis of uterine endometrial normal patients in the control group. All patients underwent conventional magnetic resonance (Magnetic resonance imaging, MRI) sequence and diffusion-weighted imaging (Diffusion weighted imaging DWI (MR contrast) and common enhanced enhanced, CE-MR scan) the automatic generation of DWI. After scanning ADC image of original data to the postprocessing software (Siemens Syngo), artificial drawing ADC of every level of region of interest (R OI) up to and including the whole tumor or normal uterus in vivo membrane and automatically generate the corresponding level ADC histogram by postprocessing software, and then converted into a frequency distribution table input SPSS 20 software to reconstruct the whole tumor or normal endometrium of ADC histogram is used to analyze the data. The patients according to the postoperative pathological type and histological grade, myometrial invasion depth, there is no cervical stromal invasion group were compared between groups, the case group and the control group of ADC histogram parameters, including the average value (mean), the fifth percentile (P5), the tenth percentile number (P10), the twenty-fifth percentile (P25) and the fiftieth percentile (P50), the seventy-fifth percentile (P75), the ninetieth percentile (P90) and 9 thousand and 500 percentile (P95). The two groups were compared using independent samples t test, the four groups were compared using single factor analysis of variance, P0.05 was considered to be statistically significant. On the above parameters. Analysis of curve line (receiver operating characteristic curve, by ROC) and calculate the area under the curve (area under the ROC curve, AUC), to obtain the best diagnostic cut-off value and the corresponding sensitivity and specificity. Results: the 1.ADC value of parameter identification cases with histogram of histopathological features: (1) ADC P5, P10, P25 in endometrial adenocarcinoma G1, G2, there are statistically significant differences between G3 and non endometrioid carcinoma (P0.05); (2) endometrial adenocarcinoma (G1+G2+G3) compared with non endometrioid carcinoma of the ADC P5 value decreased, the difference was statistically significant (P0.05). The area under the ROC curve of AUC=0.654, the optimal cut-off value of 719.00x10-6mm2/s, the sensitivity and specificity were respectively 0.29,1.00; (3) the depth of myometrial invasion shallow muscle layer infiltration of ADC mean, P5, P10, P25 and P50 were decreased, the difference was statistically significant (P0.05), ADC P5 the differential value of tumor invasion Make the muscle layer depth of AUC=0.725, the highest efficiency, the optimal cut-off value of 653.10x10-6mm2/s, the sensitivity was 0.51, specificity was 1; (4) cervical stromal invasion with positive and negative two group ADC values difference between each observation index histogram were not statistically significant (P0.05) cases of.2. group and control group ADC mean, P_5, P_ (10), P_ (25), P_ (50), P_ (75), P_ (90) and P_ (95) values of the differences were statistically significant (P0.05), the diagnostic efficiency of P90 is the highest, AUC=0.987, the optimal cutoff value for 1438.90x10~ (-6) mm~2/s. The corresponding sensitivity and specificity for 0.97,0.94. conclusion: 1. the tumor volume were ADC based on histogram is helpful to predict the patients with high risk factors of uterine endometrial carcinoma, such as endometrial adenocarcinoma G3, non uterine endometrioid carcinoma and deep myometrial invasion, but there is no cervical stromal invasion of.2.ADC was no significant difference the histogram is helpful to preoperative diagnosis Endometrial cancer and normal endometrium, of which ninetieth percentile is considered to be the most significant parameter..3.ADC histogram can make quantitative analysis of lesions and provide more accurate information for clinicians.

【學位授予單位】：吉林大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：R737.33;R445.2

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