本文關鍵詞： 多參數(shù) 磁共振成像 PI-RADS 前列腺癌 列線圖　出處：《臨床放射學雜志》2017年12期 　論文類型：期刊論文

【摘要】：目的探索基于前列腺影像報告和數(shù)據系統(tǒng)第二版(PI-RADS v2)聯(lián)合前列腺癌相關生物指標建立預測前列腺高級別腫瘤的列線圖模型。方法回顧性分析2014年1月至2016年8月本院接受前列腺多參數(shù)磁共振成像檢查的患者資料,根據PI-RADS v2標準對前列腺主要病灶進行評分,納入患者年齡、PI-RADS v2、總前列腺特異抗原(t PSA)、游離前列腺特異抗原(f PSA)、前列腺體積、前列腺特異抗原密度(PSAD),游離/總前列腺抗原百分比(f/t)比值等相關指標進行多因素Logistic回歸分析,病理采用超聲引導穿刺活檢或前列腺切除作為"金標準"。各指標在前列腺高級別腫瘤中的診斷價值采用受試者工作特征(ROC)曲線分析。篩選出的預測因子通過R軟件建立nomogram模型,最后采用留一交叉驗證評估模型判別能力。結果共111例患者納入研究,ROC曲線分析顯示PSAD在診斷前列腺高級別腫瘤中曲線下面積(AUC)最大(AUC=0.84,95%CI:0.77,0.90);多因素Logistic回歸分析顯示患者年齡(OR=1.10,95%CI:1.01,1.20,P=0.023)、PI-RADS v2評分(OR=3.05,95%CI:1.70,5.49,P=0.001)、前列腺體積(OR=0.96,95%CI:0.93,0.99,P=0.020)為高級別腫瘤的獨立預測因素,擬合ROC曲線AUC達0.92(95%CI:0.87,0.97)。留一交叉驗證該模型對82%的病例進行了準確分類。結論基于患者年齡、PI-RADS v2、前列腺體積建立的前列腺高級別腫瘤預測模型診斷準確性明顯提高,值得推廣運用。
[Abstract]:Objective to establish a linear model of prostate cancer prediction based on prostate imaging report and data system (PI-RADS v2) combined with prostate cancer related biomarkers. Methods A retrospective analysis was performed from January 2014 to August 2016. The data of patients undergoing multiparameter magnetic resonance imaging of prostate in our hospital, According to the PI-RADS v2 criteria, the main prostate lesions were graded, and the patients were included in the age of PI-RADS v2, the total prostate specific antigen (TPCA), the free prostate specific antigen (PSA), the volume of the prostate, the volume of the prostate. The density of prostate specific antigen (PSAD) and the ratio of free to total prostatic antigen (f / t) were analyzed by multivariate Logistic regression analysis. Ultrasound-guided biopsy or prostatectomy was used as the "golden standard". The diagnostic value of each index in high grade prostate tumor was analyzed by using the operating characteristics of the subjects. The predicted factors were established by R software to establish the nomogram model. Results A total of 111 patients were included in the study. Results the maximum value of PSAD in diagnosing prostatic high grade tumors was 0.84% CI: 0.770.90%. Multivariate Logistic regression analysis showed that the patients suffered from the disease. The age of the patients was 1.1095 CI1: 1.01C 1.20 P0. 023a PI-RADS v2 score: 3.05% 95 CI: 1.705.49% P0.001, prostate volume OR0.9695 CI0.99P0.020) as an independent predictor of high grade neoplasms. The fitting ROC curve AUC reached 0.92% 0.87% 0.97%. The model was used to classify 82% cases accurately. Conclusion based on the patient's age and PI-RADS v2, the diagnostic accuracy of the prostatic high-grade tumor prediction model established by prostate volume is obviously improved, which is worth popularizing.
【作者單位】： 成都大學附屬醫(yī)院放射科;成都大學附屬醫(yī)院中心實驗室;
【基金】：2015年成都市衛(wèi)計委醫(yī)學科研課題(編號:2015080) 2017年四川省衛(wèi)計委科研課題(編號:17PJ430)
【分類號】：R445.2;R737.25
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本文編號：1551388