初診前列腺癌骨轉移判別分析模型的建立和驗證
發(fā)布時間：2018-08-16 19:05
【摘要】：第一部分初診前列腺癌骨轉移判別分析模型的建立和交叉驗證目的：對于前列腺癌患者,早期診斷骨轉移至關重要。本部分尋找與骨轉移相關的臨床指標變量,并探索變量的最佳分段方式,從而建立初診前列腺癌骨轉移判別分析模型。方法：本研究納入2009年1月至2011年11月間在復旦大學附屬中山醫(yī)院泌尿外科初次診斷為前列腺癌的患者,共488例。所有患者在確診之初都進行了锝99標記的雙磷酸鹽骨掃描檢查。如果骨掃描的結果為疑似骨轉移,利用SPECT-CT、CT或MRI對可疑病灶的性質加以進一步確認。每一位患者都有初次診斷時的PSA值、臨床TNM分期和前列腺穿刺活檢病理Gleason評分。采用多因素回歸分析研究上述各臨床指標與前列腺癌骨掃描的相關性。然后根據多因素回歸結果繪制ROC并計算出AUC。通過判別分析推斷需要進行骨掃描的前列腺患者的臨床特征。最后對判別分析結果進行l(wèi)eave-one-out法交叉驗證。結果：全部488例患者中65例(13.3%)發(fā)現(xiàn)有骨轉移。cT4期、Gleason評分≥4+3、PSA值是骨轉移的獨立預測指標。多因素回歸模型ROC曲線下面積0.87(95%可信區(qū)間為0.83-0.94)。以靈敏度和特異度分別為87.7%和73.1%的切點進行判別分析發(fā)現(xiàn),對于cT1-T3期的患者且Gleason評分≤3+4的患者,若PSA≤132.1 ng/ml骨轉移發(fā)生率為2%；若PSA132.1 ng/ml,骨轉移發(fā)生率為25%。對于cT1-T3期的患者且Gleason評分≥4+3的患者,若PSA≤44.5 ng/ml,骨轉移發(fā)生率為4%；若PSA44.5 ng/ml,骨轉移發(fā)生率為29%。cT4的患者骨轉移發(fā)生率為73%。交叉驗證結果,靈敏度和特異度分別為86.2%和71.9%,證明模型穩(wěn)定可靠。結論：cT4期、Gleaso n評分≥4+3、PSA值是骨轉移的獨立預測指標。本研究構建的判別分析模型能判斷初診前列腺癌患者骨轉移的發(fā)生率。第二部分潛在預測指標的探索與判別分析模型的優(yōu)化目的：探索可以優(yōu)化初診前列腺癌骨轉移判別分析模型的臨床指標,并通過優(yōu)化后的骨轉移判別分析模型探討對于哪些患者初診時可以不行骨掃描檢查。方法：本研究納入了2009年1月至2012年1月間在復旦大學附屬中山醫(yī)院泌尿外科初次診斷為前列腺癌的患者,共501例。所有患者的病理學診斷為前列腺腺癌�；颊叩馁Y料包括前列腺穿刺活檢病理Gleason評分、臨床T分期(根據2002年TNM分期系統(tǒng))、確診時血清PSA、ALP、LDH等臨床資料被檢索收集。其中69例患者在初次診斷時行血清鈣、磷檢查。所有患者在確診之初都進行了锝99標記的雙磷酸鹽骨掃描檢查。如果骨掃描的結果為疑似骨轉移,利用SPECT-CT、CT或MRI對可疑病灶的性質加以進一步確認。利用多因素回歸模型繪制ROC并計算AUC。將ln(PSA+1)、Gleason評分、臨床T分期構建多因素回歸模型,然后在此回歸模型中加入ALP變量得到新的回歸模型,新回歸模型與原回歸模型分別繪制ROC,比較兩者的AUC。通過AUC搜索最佳切割值,將PSA和ALP以最佳切割值分為兩段,作為判別分析的切點。將判別分析的結果進行表格化。結果：加入ALP變量后ROC曲線下面積(AUC)為0.92,95%可信區(qū)間為0.89-0.96,顯著大于未加入ALP變量時的曲線下面積(p=0.0012)。對于cTl-T3期的前列腺癌患者如果PSA≤39ng/ml且ALP≤88IU/L,那么骨轉移的風險較小,在初診時不做骨掃描是安全的。其他患者尤其是T4期的患者在初診時必須做骨掃描。結論：在第一部分篩查模型中加入ALP可以優(yōu)化此模型,可以提高預測準確性。此模型由單中心的數(shù)據建立,需要多中心的研究來驗證它的外部真實性。第三部分判別分析模型的外部驗證目的：應用外部驗證對第二部分中建立的判別分析模型進行評估。方法：外部驗證的對象是于2005年3月至2011年3月期間在復旦大學附屬腫瘤醫(yī)院泌尿外科住院治療的前列腺癌患者,共501例。患者的臨床資料包括年齡、Gleason評分、臨床分期、PSA值、ALP值等。影像學上診斷骨轉移的方法和標準與本研究一致。以第二部分建立判別分析模型作為篩查工具,預測外部數(shù)據庫中患者在前列腺癌初診時的骨轉移情況。以判別分析結果與實際骨轉移情況構建ROC曲線,計算曲線下面積。結果：外部驗證的靈敏度和特異度分別達到85.5%和64.0%。ROC曲線的AUC為0.846(95%CI：0.805-0.887),優(yōu)于其他預測模型。結論：本文建立的判別分析模型穩(wěn)定可靠,可以應用于臨床工作。
[Abstract]:The first part is the establishment and cross-validation of the discriminant analysis model of bone metastasis in newly diagnosed prostate cancer. METHODS: This study included 488 patients with prostate cancer who were initially diagnosed in the Department of Urology, Zhongshan Hospital, Fudan University, from January 2009 to November 2011. All patients underwent bone scan with 99Tc labeled diphosphate at the beginning of diagnosis. Each patient had PSA values at the time of initial diagnosis, clinical TNM staging and Gleason score of prostate biopsy pathology. Correlation between the above clinical parameters and bone scan of prostate cancer was analyzed by multivariate regression analysis. ROC was plotted according to multivariate regression results and AUC was calculated. Results: Bone metastasis was found in 65 of 488 patients (13.3%). CT4, Gleason score (> 4 + 3), PSA value was an independent predictor of bone metastasis. The area was 0.87 (95% confidence interval 0.83-0.94). The sensitivity and specificity were 87.7% and 73.1% respectively. For patients with cT1-T3 and Gleason score < 3+4, the incidence of bone metastasis was 2% if PSA < 132.1 ng/ml, and 25% if PSA132.1 ng/ml. For patients with cT1-T3 and Gleason score < 3+4, the incidence of bone metastasis was 25%. The incidence of bone metastasis was 4% in patients with PSA < 44.5 ng/ml, 29% in patients with PSA < 44.5 ng/ml, and 73% in patients with cT4. The sensitivity and specificity were 86.2% and 71.9% respectively. The model was stable and reliable. Predictive indicators. The discriminant analysis model constructed in this study can determine the incidence of bone metastasis in newly diagnosed prostate cancer patients. Part II Exploration of potential predictors and optimization of discriminant analysis model: To explore the clinical indicators that can optimize the discriminant analysis model of bone metastasis in newly diagnosed prostate cancer, and to discriminate the bone metastasis after optimization. Methods: This study included 501 patients with prostate cancer who were initially diagnosed as prostate cancer in the Department of Urology, Sun Yat-sen Hospital, Fudan University, from January 2009 to January 2012. All patients were pathologically diagnosed as prostate cancer. Gleason score of biopsy pathology, clinical T stage (according to TNM staging system 2002), serum PSA, ALP, LDH and other clinical data were collected at the time of diagnosis. 69 of them were examined for serum calcium and phosphorus at the time of initial diagnosis. All patients underwent technetium-99 labeled bone scan at the beginning of diagnosis. If the bone scan results were Suspected bone metastasis was further confirmed by SPECT-CT, CT or MRI. ROC was plotted by multivariate regression model and AUC was calculated. ln (PSA+1), Gleason score and clinical T-stage were used to construct multivariate regression model. A new regression model was obtained by adding ALP variables into the regression model. The results of discriminant analysis were tabulated. Results: After adding ALP variable, the area under ROC curve (AUC) was 0.92, 95% confidence interval was 0.89-0.96, which was significantly larger than that without ALP variable. If PSA < 39 ng / ml and ALP < 88 IU / L, the risk of bone metastasis is small, and it is safe not to do bone scan at the initial diagnosis. Other patients, especially those with T4, must do bone scan at the initial diagnosis. Conclusion: ALP can be optimized in the first part of the screening model. This model can improve the accuracy of prediction. This model is built from single-center data and needs multi-center research to verify its external authenticity. Part III: The external verification purpose of discriminant analysis model: Applying external verification to evaluate the discriminant analysis model established in Part II. Methods: The external verification object is 200. From March 2005 to March 2011, 501 patients with prostate cancer were hospitalized in the Department of Urology, Tumor Hospital Affiliated to Fudan University. The clinical data included age, Gleason score, clinical stage, PSA value and ALP value. As a screening tool, the ROC curves were constructed to predict the bone metastasis of the patients in the external database at the initial diagnosis of prostate cancer.Results: The sensitivity and specificity of the external verification were 85.5% and 64.0% respectively.The AUC of the ROC curves was 0.846 (95% CI: 0.805-0.887), which was superior to that of the actual bone metastasis. Conclusion: The discriminant analysis model established in this paper is stable and reliable and can be used in clinical work.
【學位授予單位】：復旦大學
【學位級別】：博士
【學位授予年份】：2014
【分類號】：R737.25
本文編號：2186898
[Abstract]:The first part is the establishment and cross-validation of the discriminant analysis model of bone metastasis in newly diagnosed prostate cancer. METHODS: This study included 488 patients with prostate cancer who were initially diagnosed in the Department of Urology, Zhongshan Hospital, Fudan University, from January 2009 to November 2011. All patients underwent bone scan with 99Tc labeled diphosphate at the beginning of diagnosis. Each patient had PSA values at the time of initial diagnosis, clinical TNM staging and Gleason score of prostate biopsy pathology. Correlation between the above clinical parameters and bone scan of prostate cancer was analyzed by multivariate regression analysis. ROC was plotted according to multivariate regression results and AUC was calculated. Results: Bone metastasis was found in 65 of 488 patients (13.3%). CT4, Gleason score (> 4 + 3), PSA value was an independent predictor of bone metastasis. The area was 0.87 (95% confidence interval 0.83-0.94). The sensitivity and specificity were 87.7% and 73.1% respectively. For patients with cT1-T3 and Gleason score < 3+4, the incidence of bone metastasis was 2% if PSA < 132.1 ng/ml, and 25% if PSA132.1 ng/ml. For patients with cT1-T3 and Gleason score < 3+4, the incidence of bone metastasis was 25%. The incidence of bone metastasis was 4% in patients with PSA < 44.5 ng/ml, 29% in patients with PSA < 44.5 ng/ml, and 73% in patients with cT4. The sensitivity and specificity were 86.2% and 71.9% respectively. The model was stable and reliable. Predictive indicators. The discriminant analysis model constructed in this study can determine the incidence of bone metastasis in newly diagnosed prostate cancer patients. Part II Exploration of potential predictors and optimization of discriminant analysis model: To explore the clinical indicators that can optimize the discriminant analysis model of bone metastasis in newly diagnosed prostate cancer, and to discriminate the bone metastasis after optimization. Methods: This study included 501 patients with prostate cancer who were initially diagnosed as prostate cancer in the Department of Urology, Sun Yat-sen Hospital, Fudan University, from January 2009 to January 2012. All patients were pathologically diagnosed as prostate cancer. Gleason score of biopsy pathology, clinical T stage (according to TNM staging system 2002), serum PSA, ALP, LDH and other clinical data were collected at the time of diagnosis. 69 of them were examined for serum calcium and phosphorus at the time of initial diagnosis. All patients underwent technetium-99 labeled bone scan at the beginning of diagnosis. If the bone scan results were Suspected bone metastasis was further confirmed by SPECT-CT, CT or MRI. ROC was plotted by multivariate regression model and AUC was calculated. ln (PSA+1), Gleason score and clinical T-stage were used to construct multivariate regression model. A new regression model was obtained by adding ALP variables into the regression model. The results of discriminant analysis were tabulated. Results: After adding ALP variable, the area under ROC curve (AUC) was 0.92, 95% confidence interval was 0.89-0.96, which was significantly larger than that without ALP variable. If PSA < 39 ng / ml and ALP < 88 IU / L, the risk of bone metastasis is small, and it is safe not to do bone scan at the initial diagnosis. Other patients, especially those with T4, must do bone scan at the initial diagnosis. Conclusion: ALP can be optimized in the first part of the screening model. This model can improve the accuracy of prediction. This model is built from single-center data and needs multi-center research to verify its external authenticity. Part III: The external verification purpose of discriminant analysis model: Applying external verification to evaluate the discriminant analysis model established in Part II. Methods: The external verification object is 200. From March 2005 to March 2011, 501 patients with prostate cancer were hospitalized in the Department of Urology, Tumor Hospital Affiliated to Fudan University. The clinical data included age, Gleason score, clinical stage, PSA value and ALP value. As a screening tool, the ROC curves were constructed to predict the bone metastasis of the patients in the external database at the initial diagnosis of prostate cancer.Results: The sensitivity and specificity of the external verification were 85.5% and 64.0% respectively.The AUC of the ROC curves was 0.846 (95% CI: 0.805-0.887), which was superior to that of the actual bone metastasis. Conclusion: The discriminant analysis model established in this paper is stable and reliable and can be used in clinical work.
【學位授予單位】：復旦大學
【學位級別】：博士
【學位授予年份】：2014
【分類號】：R737.25
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