本文選題：肺腺癌 + 肺亞實性結節(jié)��； 參考：《第二軍醫(yī)大學》2017年碩士論文

【摘要】：第一部分肺亞實性結節(jié)分類評估和定量測量方法的可重復性和準確性的研究【目的】對比不同窗寬窗位條件下不同維度的肺亞實性結節(jié)(SSN)分類評估和定量測量方法的可重復性和準確性,并探討實性成分大小與病理等級的相關性�！痉椒ā炕仡櫺苑治�157例患者肺內(nèi)手術證實為腺癌的159個SSN的術前胸部HRCT圖像及病理資料。根據(jù)病理結果將SSN分為無實性成分組和有實性成分組。觀察者1和觀察者2分別在肺窗、縱隔窗及-300閾值半自動分割法條件下對所有SSN進行分類評估,分類結果采用Cohen’s Kappa檢驗分析分類評估的觀察者間一致性。兩位觀察者意見不一的SSN由觀察者3判定,最終分類結果采用配對卡方檢驗和ROC曲線分析分類評估的準確性。觀察者1使用聯(lián)影后處理工作站測量所有SSN的肺窗結節(jié)一維長徑(1D-WNLW),肺窗結節(jié)二維長徑(2D-WNLW),肺窗實性一維長徑(1D-SCLW),肺窗實性二維長徑(2D-SCLW),縱隔窗實性一維長徑(1D-SCMW),縱隔窗實性二維長徑(2D-SCMW),肺窗結節(jié)體積(3D-WNLW),肺窗實性體積(3D-SCLW),縱隔窗實性體積(3D-SCMW)和-300閾值實性體積(3D-SCT),觀察者2隨機抽取50個SSN并重復上述測量。兩位觀察者測量結果采用組內(nèi)相關系數(shù)分析定量測量的觀察者間一致性。觀察者1的測量結果使用Spearman等級相關分析檢驗定量特征與病理等級的相關性。P0.05時差異有統(tǒng)計學意義�！窘Y果】有實性成分組共54個SSN,包括32個AAH和22個AIS;無實性成分組共105個SSN,包括47個MIA和58個IAC。不同窗寬窗位及不同維度條件下SSN分類評估和定量測量方法的可重復性均較高(0.71k0.9,0.71ICC)。使用-300閾值半自動分割法進行分類評估和定量測量的可重復性最高(Kappa=0.831,ICC=0.983),且分類評估的準確性最高(Sens=85%,Spec=61%,PPV=81%,NPV=67%,AUC=0.750)。所有定量特征均與病理等級存在中度正相關關系(0.4≤r0.7)�！窘Y論】在不同窗寬窗位及不同維度條件下SSN的分類評估及定量測量的諸多方法中,-300閾值半自動分割法的可重復性和準確性最高。SSN及其實性成分的大小與病理等級呈正相關關系。第二部分第一節(jié)肺亞實性結節(jié)及其實性成分不同維度大小對病理等級的預測價值【目的】探討不同窗寬窗位下肺亞實性結節(jié)(SSN)及其實性成分不同維度的大小對病理等級的預測價值�！痉椒ā炕仡櫺苑治�125例患者肺內(nèi)病理為肺腺癌的127個SSN的術前HRCT圖像及病理資料。根據(jù)術后5年生存率的不同,將所有SSN分為兩組,A組包含AIS和MIA;B組包含IAC。由一名有5年影像診斷經(jīng)驗的醫(yī)師使用聯(lián)影后處理工作站測量所有SSN的肺窗實性一維長徑(1D-SCLW)、肺窗實性一維長徑(2D-SCLW)、縱隔窗實性一維長徑(1D-SCMW)、縱隔窗實性二維長徑(2D-SCMW)、肺窗結節(jié)一維長徑(1D-WNLW)、肺窗結節(jié)二維長徑(2D-WNLW)和-300閾值實性體積(3D-SCT)。使用Mann-Whitney U檢驗進行兩組定量特征的差異性分析。采用ROC曲線檢驗定量特征對病理等級的診斷效能。對所有定量特征進行單因素Logistic回歸分析,所得有統(tǒng)計學意義的定量特征納入多因素Logistic回歸進行分析,得到病理等級的獨立預測因素,P0.05時差異有統(tǒng)計學意義�！窘Y果】A組共69個SSN,包含22個AIS和47個MIA;B組包含58個IAC。B組的1D-SCLW、2D-SCLW、1D-SCMW、2D-SCMW、1D-WNLW、2D-WNLW和3D-SCT顯著大于A組(P0.0001)。通過ROC曲線分析得出,在以上7種CT定量特征中,3D-SCT對病理等級的診斷效能最高(AUC=0.887,敏感度:81%,特異度:93%);1D-SCLW、2D-SCLW、1D-SCMW、2D-SCMW、1D-WNLW、2D-WNLW和3D-SCT的最優(yōu)閾值分別為17.50mm、14.75 mm、9.50 mm、7.75 mm、0.50 mm、1.25 mm和139.00 mm3。多因素logistic回歸分析結果表明,3D-SCT是SSN病理等級的獨立預測因素(OR=4.978,95%CI=1.430~17.331,P=0.012)。當3D-SCT≥139.00 mm3時,高度提示IAC(AUC=0.887,敏感度:81%,特異度:93%)�！窘Y論】在不同窗寬窗位下SSN及其實性成分不同維度的大小中,-300HU閾值實性成分體積是病理等級的獨立預測因素,其診斷效能優(yōu)于SSN及其實性成分的一維和二維長徑。第二部分第二節(jié)肺亞實性結節(jié)實性成分體積及其占比對病理等級的預測價值【目的】對比不同窗寬窗位下肺亞實性結節(jié)(SSN)體積和實性成分體積及其占比對病理等級的預測價值�！痉椒ā炕仡櫺苑治鑫以�125例患者肺內(nèi)手術證實為腺癌的127個SSN的術前HRCT圖像及病理資料。根據(jù)術后5年生存率的不同,將所有SSN分為兩組,A組包含AIS和MIA;B組包含IAC。由一名有5年影像診斷經(jīng)驗的醫(yī)師使用聯(lián)影后處理工作站測量所有SSN的肺窗結節(jié)體積(3D-WNLW)、肺窗實性體積(3D-SCLW)、縱隔窗實性體積(3D-SCMW)和-300閾值實性體積(3D-SCT),并計算肺窗實性占比(P-SCLW);縱隔窗實性占比(P-SCMW);-300閾值實性占比(P-SCT)。使用組內(nèi)相關系數(shù)(ICC)檢驗定量測量的觀察者間一致性。采用Mann-Whitney U檢驗比較兩組間定量特征的差異。采用單因素Logistic回歸分析對所有定量特征進行初篩,所得有統(tǒng)計學意義的定量特征納入多因素Logistic回歸進行分析,得到病理等級的獨立預測因素。采用ROC曲線評價獨立預測因素的診斷效能。P0.05時差異有統(tǒng)計學意義�！窘Y果】A組共69個SSN,包含22個AIS和47個MIA;B組包含58個IAC。B組的3D-WNLW、3D-SCLW、3D-SCMW、3D-SCT、P-SCLW、P-SCMW和P-SCT顯著大于A組(P0.0001)。單因素Logistic回歸分析顯示,3D-WNLW、3D-SCLW、3D-SCMW、3D-SCT、P-SCLW、P-SCMW和P-SCT均有統(tǒng)計學意義(P0.0001),多因素Logistic回歸分析發(fā)現(xiàn),僅P-SCT是SSN病理等級的獨立預測因素(OR=1.093,95%CI:1.047~1.141,P0.0001)。當P-SCT≥6.00%時,高度提示IAC(AUC=0.846,敏感度:79%,特異度:75%)�！窘Y論】在不同窗寬窗位下SSN體積和實性成分體積及其占比中,P-SCT是SSN病理等級的獨立預測因素,能夠有效區(qū)分IAC和AIS-MIA,與SSN及其實性成分的體積相比,能夠為手術方式的選擇提供更有價值的參考依據(jù)。
[Abstract]:The first part of the study on the reproducibility and accuracy of the taxonomy and quantitative measurements of the pulmonary nodules (objective) to compare the repeatability and accuracy of the classification and quantitative measurements of pulmonary subsolid nodules (SSN) with different dimensions under different window wide window positions, and to explore the correlation between the size of the solid components and the pathological grade. [Methods] the preoperative chest HRCT images and pathological data of 157 patients with adenocarcinoma confirmed by intrapulmonary surgery were retrospectively analyzed. According to the pathological results, the SSN was divided into the non solid component group and the solid component group. The observer 1 and the observer 2 were divided into all SSN under the condition of the lung window, the mediastinum window and the semi automatic segmentation of the -300 threshold. Class evaluation, Cohen 's Kappa test was used to analyze the inter observer consistency of the classification assessment. The two observer disagreed SSN was determined by the observer 3. The final classification results were evaluated by the paired chi square test and the ROC curve analysis. The observer 1 measured all the SSN lung window nodes using the combined post processing workstation. One dimension length diameter (1D-WNLW), two-dimensional long diameter (2D-WNLW) of pulmonary window nodules, solid one dimension diameter (1D-SCLW), solid two-dimensional long diameter (2D-SCLW) of the window, solid dimension of mediastinal window (1D-SCMW), solid two-dimensional long diameter (2D-SCMW) in the mediastinum window, volume of pulmonary window (3D-WNLW), real volume of lung window (3D-SCLW), solid volume of mediastinal window (3D-SCMW) and -300 threshold Value real volume (3D-SCT), the observer 2 randomly selected 50 SSN and repeated the above measurements. The two observer results were measured by intra group correlation coefficient, and the consistency between the observers was measured by the intra group correlation coefficient. The observer 1 measured the correlation between the quantitative characteristics and the pathological grade by Spearman correlation analysis. There was a statistical difference between the observer and the pathological grade. [results] a total of 54 SSN, including 32 AAH and 22 AIS, 105 SSN, including 47 MIA and 58 IAC. windows with different window width and different dimensions, SSN classification evaluation and quantitative measurement method with higher repeatability (0.71k0.9,0.71ICC). Classification using -300 threshold semi-automatic segmentation method is used for classification. The highest repeatability (Kappa=0.831, ICC=0.983) of evaluation and quantitative measurement (Kappa=0.831, ICC=0.983) was the highest (Sens=85%, Spec=61%, PPV=81%, NPV=67%, AUC=0.750). All quantitative characteristics had moderate positive correlation with pathological grades (0.4 < < r0.7). [Conclusion] the classification evaluation of SSN under different window width and different dimensions and Among the many methods of quantitative measurement, the repeatability and accuracy of the -300 threshold semi-automatic segmentation method is the highest.SSN and the size of its actual components is positively correlated with the pathological grade. The second part of the first section of the pulmonary nodules and the different dimensions of its actual components to the pathological grade of the premeasured value [Objective] to discuss the different window wide window position The predictive value of the size of the lower pulmonary subsolid nodules (SSN) and the size of their real components to the pathological grades. [Methods] a retrospective analysis of the preoperative HRCT images and pathological data of 127 SSN in lung adenocarcinoma in 125 patients was reviewed. According to the 5 year survival rate, the SSN was divided into two groups, the A group contained AIS and MIA, and B group contained IAC. A physician with 5 years of imaging diagnostic experience measured the solid one dimension length (1D-SCLW) of all SSN lung windows, solid one dimension (2D-SCLW), solid one dimension length (1D-SCMW) of the mediastinal window, solid two-dimensional long diameter (2D-SCMW) of the mediastinum window, one dimension diameter (1D-WNLW) of the window of the lung window (1D-WNLW), and the two-dimensional length of the pulmonary window nodules (2D-WNL). W) and -300 threshold real volume (3D-SCT). Use the Mann-Whitney U test to analyze the difference between the two groups of quantitative characteristics. Use the ROC curve to test the diagnostic efficiency of the quantitative characteristics for the pathological grade. All quantitative features are analyzed by single factor Logistic regression, and the quantitative characteristics of statistical meaning are included in the multiple factor Logistic regression. There were 69 SSN of SSN in group A, including 22 AIS and 47 MIA, and B group contained 1D-SCLW of IAC.B group, 2D-SCLW, 1D-SCMW, 2D-SCMW. 3D-SCT was most effective in the diagnosis of pathological grade (AUC=0.887, sensitivity: 81%, specificity: 93%); the optimal threshold for 1D-SCLW, 2D-SCLW, 1D-SCMW, 2D-SCMW, 1D-WNLW, 2D-WNLW and 3D-SCT were 17.50mm, 14.75 mm, 9.50 mm, 0.50, 1.25, 1.25 and 139. The independent predictor (OR=4.978,95%CI=1.430~17.331, P=0.012). When 3D-SCT is more than 139 mm3, IAC (AUC=0.887, sensitivity: 81%, specificity: 93%). [Conclusion] the volume of -300HU threshold value is an independent predictor of pathological grade in the size of different dimensions of SSN and its actual components at different window wide window positions. One and two dimensional length of effectiveness superior to SSN and its actual components. The volume of solid components in the second part of the second nodular pulmonary nodules and its predictive value to the pathological grade [Objective] to compare the volume of SSN volume and the volume of solid components under different window wide window positions and the predictive value of its proportion to the pathological grade. A retrospective analysis of the preoperative HRCT images and pathological data of 125 patients with adenocarcinoma confirmed by intrapulmonary surgery in 125 patients. According to the 5 year survival rate, all SSN were divided into two groups, the A group included AIS and MIA, and the B group included a 5 year imaging doctor with a 5 year postprocessing workstation to measure all the lungs of all SSN. Window nodule volume (3D-WNLW), pulmonary window volume (3D-SCLW), mediastinal real volume (3D-SCMW) and -300 threshold real volume (3D-SCT), and calculated the ratio of real lung window (P-SCLW); mediastinal window real occupying ratio (P-SCMW); -300 threshold real ratio (P-SCT). The consistency of quantitative measurement with intra group correlation coefficient (ICC) was used. Mann-Whi Tney U test compared the differences in quantitative characteristics between the two groups. Using single factor Logistic regression analysis, all quantitative characteristics were screened. The quantitative characteristics were statistically analyzed by multiple factor Logistic regression, and the independent predictors of pathological grade were obtained. The diagnostic effectiveness of independent predictors by ROC curve was used to evaluate the diagnostic efficiency.P0.05 [results] there were 69 SSN in group A, including 22 AIS and 47 MIA, and B group including 3D-WNLW, 3D-SCLW, 3D-SCMW, 3D-SCT, P-SCLW. 0001), multiple factor Logistic regression analysis found that only P-SCT was an independent predictor of SSN pathological grade (OR=1.093,95%CI:1.047~1.141, P0.0001). When P-SCT was more than 6%, IAC (AUC=0.846, sensitivity: 79%, specificity: 75%). [Conclusion] P-SCT is SSN disease in the volume and proportion of SSN and real components at different window wide window positions. The independent predictors of the grade can effectively distinguish between IAC and AIS-MIA, which can provide a more valuable reference for the choice of surgical methods compared with the volume of SSN and its actual components.
【學位授予單位】：第二軍醫(yī)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：R734.2;R730.44

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