【摘要】：目的:本研究旨在評估乳腺癌前哨淋巴結(Sentinel Lymph Node,SLN)術中分子診斷技術一步核酸擴增法(One-Step Nucleic Acid Amplification,OSNA)的診斷價值,并根據OSNA檢測結果分析其對非前哨淋巴結(Non-Sentinel Lymph Node,NSLN)轉移的預測能力,進一步建立乳腺癌術中快速預測NSLN轉移的預測模型,以期有效指導乳腺癌后續(xù)治療。方法:本研究包括2010年OSNA臨床試驗入組的552例患者及此次試驗本中心前瞻性入組的348例患者,共計入組患者900例,有效病例為870例。術中根據SLN的質量及短軸長度進行切分。若SLN質量100 mg,則不進行切分,僅術后行逐層切片病理檢測;若SLN質量為100~1200 mg,則垂直于短軸進行切分。短軸長度≤4mm時,則切分為a和b兩個組織塊,a組織塊術中行OSNA檢測,b組織塊行病理檢測;短軸長度4 mm時,則切分為a,b,c,d四個組織塊,a、c組織塊術中行OSNA檢測,b、d組織塊行病理檢測。其中a,b,c,d組織塊均行術中印片細胞學(Touch Imprint Cytology,TIC)檢測。一方面,OSNA檢測結果同術后逐層連續(xù)切片病理作比較,另一方面根據OSNA結果建立NSLN轉移風險的術中快速預測模型。結果:1、OSNA檢測的診斷價值(1)以病例數為統(tǒng)計對象分析,以術后病理為金標準,OSNA與FS、TIC比較以病例數為統(tǒng)計對象分析,OSNA的敏感性、準確性、特異性、陽性預測值、陰性預測值分別為88.7%、89.0%、89.1%、72.3%、96.1%。FS的敏感性、準確性、特異性、陽性預測值、陰性預測值分別為77.9%、94.0%、100.0%、100.0%、92.3%。TIC的敏感性、準確性、特異性、陽性預測值、陰性預測值分別為83.0%、93.5%、96.8%、89.3%、94.7%。OSNA的敏感性顯著優(yōu)于FS(88.7%vs.77.9%,P=0.0095),與TIC相比并無統(tǒng)計學差異(88.7%vs.83.0%,P=0.1254)。(2)以淋巴結數為統(tǒng)計對象分析,以術后病理為金標準,OSNA與FS、TIC比較以淋巴結數為統(tǒng)計對象分析,OSNA的敏感性、準確性、特異性、陽性預測值、陰性預測值分別為85.4%、91.4%、92.6%、68.4%、97.1%。FS的敏感性、準確性、特異性、陽性預測值、陰性預測值分別為68.6%,94.3%,100.0%,100.0%,93.5%。TIC的敏感性、準確性、特異性、陽性預測值、陰性預測值分別為81.0%,94.4%,96.9%,83.2%,96.5%。OSNA的敏感性顯著優(yōu)于FS(85.4%vs.68.6%,P=0.0021),與TIC相比并無統(tǒng)計學差異(85.4%vs.81.0%,P=0.82)。(3)OSNA與FS、TIC對于宏轉移及微轉移的敏感性比較對于術后伴有宏轉移灶的SLN,OSNA檢出的敏感性是93.1%,FS檢出的敏感性是88.5%,TIC檢出的敏感性是92.2%,OSNA的敏感性與FS及TIC相似(P=0.1907,P=0.8543),并無統(tǒng)計學差異。對于術后伴有微轉移灶的SLN,OSNA檢出的敏感性是65.3%,FS檢出的敏感性是40.4%,TIC檢出的敏感性是44.0%,OSNA的敏感性均顯著優(yōu)于FS及TIC(P=0.0121,P=0.0139)。2、NSLN轉移風險的術中快速預測模型(1)建立基于分子診斷預測NSLN轉移的列線圖模型對建模組103例患者行單因素分析發(fā)現(xiàn),原發(fā)腫瘤大小(P=0.001)、脈管浸潤(P=0.007)、SLN總腫瘤負荷(Total Tumor Load,TTL)(P=0.000)、最大轉移灶大小(P=0.000)、SLN陽性數(P=0.000)、SLN陰性數(P=0.000)、SLN陽性數/SLN總數(P=0.000)均與NSLN轉移相關。Logistic多因素回歸分析后發(fā)現(xiàn)TTL(P=0.002)、原發(fā)腫瘤大小(P=0.013)、SLN陽性數(P=0.000)、SLN陰性數(P=0.001)是NSLN轉移的獨立相關因素,利用TTL、原發(fā)腫瘤大小、SLN陽性數及SLN陰性數建立預測乳腺癌NSLN轉移的列線圖模型。建模組患者NSLN轉移預測值的受試者工作特征曲線(Receiver Operating Characteristic Curve,ROC)下面積(AUC)為0.814。(2)本模型的預測性驗證研究本研究利用驗證組61例患者對建立的預測模型進行了驗證,得出AUC為0.842。(3)影像學評估的腫瘤大小與原發(fā)腫瘤大小對模型預測性差異的比較本研究利用驗證組患者影像學(超聲、鉬靶、乳腺磁共振)評估的腫瘤大小數據替代術后病理評估的原發(fā)腫瘤大小數據,對模型進行了驗證,得出AUC為0.838,與前述驗證性ROC曲線下面積相比并無統(tǒng)計學差異,P=0.7406。(4)本模型對腋窩淋巴結分期pN1及≥pN2患者的預測性研究本模型區(qū)分患者pN1及≥pN2腋窩淋巴結分期的預測風險臨界值為45.4%,AUC為0.861,P0.0001。另外,為探究術中利用影像學腫瘤大小快速預測腋窩淋巴結分期情況,利用本模型得出驗證組患者中pN1及≥p N2的預測風險臨界值為33.5%,AUC為0.839,P0.0001。(5)本模型與其他預測模型的驗證性比較利用入組患者分別對美國安德森癌癥中心(MD Anderson,MDA)的模型及法國Tenon醫(yī)院的模型進行了驗證,得出AUC分別為0.745及0.623。另外,本模型對西班牙Isabel等建立的模型進行了驗證,得出AUC為0.834。通過對比分析,本模型與MDA及Tenon模型相比有統(tǒng)計學差異,與Isabel等人的模型相比并無統(tǒng)計學差異。結論:OSNA檢測作為一種客觀的標準化技術,可以術中快速、準確的對SLN做出診斷,且操作簡單,大大降低了病理醫(yī)師的主觀性和工作負擔,可作為SLN術中診斷乃至術后診斷的首選,適合在中國推廣。基于OSNA術中分子診斷結果建立的術中快速預測NSLN轉移的模型明顯優(yōu)于其他預測模型,對后續(xù)腋窩的處理、放療靶區(qū)勾畫及全身治療具有更好的指導價值。
[Abstract]:Objective: To evaluate the diagnostic value of one-step Nucleic Acid Amplification (OSNA), a molecular diagnostic technique for sentinel lymph node (SLN) metastasis in breast cancer, and to analyze its predictive ability for non-Sentinel Lymph Node (NSLN) metastasis according to the results of OSNA detection. Methods: This study included 552 patients enrolled in the 2010 OSNA clinical trial and 348 prospective patients enrolled in the center of this trial. A total of 900 patients were enrolled in the study and 870 effective patients were enrolled in the study. If the length of SLN is less than 4 mm, it is divided into a and B tissue blocks. When the length of the short axis is less than 4 mm, OSNA detection is performed in a tissue block, and pathological examination is performed in B tissue block. When the length of the short axis is 4 mm, it is divided into a, B and B tissue blocks. The intraoperative Touch Imprint Cytology (TIC) was performed in all the tissues of a, b, C and D. On the one hand, the results of OSNA were compared with the pathology of successive slices after operation, on the other hand, the risk of NSLN metastasis was established according to the results of OSNA. Results: 1. The diagnostic value of OSNA (1) was analyzed by case number and pathology after operation as golden standard. The sensitivity, accuracy, specificity, positive predictive value and negative predictive value of OSNA were 88.7%, 89.0%, 89.1%, 72.3%, 96.1% respectively. Specificity, positive predictive value, negative predictive value were 77.9%, 94.0%, 100.0%, 100.0%, and 92.3%, respectively. Sensitivity, accuracy, specificity, positive predictive value and negative predictive value of TIC were 83.0%, 93.5%, 96.8%, 89.3%, 94.7% respectively. OSNA was significantly more sensitive than FS (88.7% vs. 77.9%, P = 0.0095), and had no statistical difference compared with TIC (88.7% vs. 83.0%, P = 0.1254). (2) The sensitivity, accuracy, specificity, positive predictive value and negative predictive value of OSNA were 85.4%, 91.4%, 92.6%, 68.4%, 97.1% respectively. The sensitivity, accuracy, specificity, positive predictive value and negative predictive value of FS were 85.4%, 91.4%, 92.6%, 68.4% and 97.1% respectively. The sensitivity, accuracy, specificity, positive predictive value and negative predictive value of TIC were 81.0%, 94.4%, 96.9%, 83.2% and 96.5%, respectively. OSNA was significantly superior to FS (85.4% vs. 68.6%, P = 0.0021), and had no statistical difference compared with TIC (85.4% vs. 81.0%, P = 0.82). (3) OSNA and FS, TIC were more sensitive to macrometastasis and microrotation. The sensitivity of OSNA was 93.1%, FS 88.5%, TIC 92.2%, OSNA was similar to FS and TIC (P = 0.1907, P = 0.8543), and there was no significant difference between them. Sensitivity was 40.4%. Sensitivity of TIC was 44.0%. OSNA was significantly superior to FS and TIC (P = 0.0121, P = 0.0139). Intraoperative rapid prediction model of NSLN metastasis risk (1) Establishment of a molecular diagnosis-based contour map model for predicting NSLN metastasis in 103 patients in the modeling group by univariate analysis found that primary tumor size (P = 0.001), vascular vessels. Infiltration (P = 0.007), total Tumor Load (TTL) (P = 0.000), maximum metastasis size (P = 0.000), SLN positive (P = 0.000), SLN negative (P = 0.000), and SLN positive / SLN total (P = 0.000) were all associated with NSLN metastasis. Negative number (P = 0.001) was an independent risk factor for NSLN metastasis. TTL, primary tumor size, SLN positive number and SLN negative number were used to establish a contour map model for predicting NSLN metastasis in breast cancer. Predictive validation of the model This study used 61 patients in the validation group to validate the predictive model. AUC was 0.842. (3) Comparison of predictive differences between the size of the tumor and the size of the primary tumor in the imaging evaluation. This study used validation group imaging (ultrasound, mammography, MRI) to assess tumor size. The AUC of the model was 0.838, which was not significantly different from the area under the validated ROC curve (P = 0.7406). (4) The model predicted axillary lymph node staging in patients with pN1 and < pN2. The model distinguished between pN1 and < pN2 axillary lymph nodes. The predictive risk threshold for staging was 45.4%, AUC was 0.861, P 0.0001. In addition, the predictive risk threshold for pN1 and < P N2 was 33.5%, AUC was 0.839, P 0.0001. (5) The validity of this model with other predictive models was verified. Compared with the model of Anderson Cancer Center (MDA) in USA and Tenon Hospital in France, the AUC was 0.745 and 0.623 respectively. In addition, the model was validated by Isabel in Spain and the AUC was 0.834. Conclusion: OSNA, as an objective standardized technique, can diagnose SLN quickly and accurately during operation, and the operation is simple, which greatly reduces the subjectivity and workload of pathologists, and can be used as the first diagnosis of SLN during operation and even after operation. The model based on OSNA intraoperative molecular diagnosis is superior to other predictive models in predicting NSLN metastasis. It has better guiding value for the follow-up axillary treatment, radiotherapy target delineation and systemic treatment.
【學位授予單位】：濟南大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：R737.9

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