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首页> 外文期刊>Journal of magnetic resonance imaging: JMRI >Radiomic features from pretreatment biparametric MRI predict prostate cancer biochemical recurrence: Preliminary findings
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Radiomic features from pretreatment biparametric MRI predict prostate cancer biochemical recurrence: Preliminary findings

机译:来自预处理的辐射瘤特征,双轴造影MRI预测前列腺癌生物化学复发:初步调查结果

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Background Radiomics or computer‐extracted texture features derived from MRI have been shown to help quantitatively characterize prostate cancer (PCa). Radiomics have not been explored depth in the context of predicting biochemical recurrence (BCR) of PCa. Purpose To identify a set of radiomic features derived from pretreatment biparametric MRI (bpMRI) that may be predictive of PCa BCR. Study Type Retrospective. Subjects In all, 120 PCa patients from two institutions, I 1 and I 2 , partitioned into training set D 1 ( N ?=?70) from I 1 and independent validation set D 2 ( N ?=?50) from I 2 . All patients were followed for ≥3 years. Sequence 3T, T 2 ‐weighted (T 2 WI) and apparent diffusion coefficient (ADC) maps derived from diffusion‐weighted sequences. Assessment PCa regions of interest (ROIs) on T 2 WI were annotated by two experienced radiologists. Radiomic features from bpMRI (T 2 WI and ADC maps) were extracted from the ROIs. A machine‐learning classifier ( C BCR ) was trained with the best discriminating set of radiomic features to predict BCR ( p BCR ). Statistical Tests Wilcoxon rank‐sum tests with P ??0.05 were considered statistically significant. Differences in BCR‐free survival at 3 years using p BCR was assessed using the Kaplan–Meier method and compared with Gleason Score (GS), PSA, and PIRADS‐v2. Results Distribution statistics of co‐occurrence of local anisotropic gradient orientation (CoLlAGe) and Haralick features from T 2 WI and ADC were associated with BCR ( P ??0.05) on D 1 . C BCR predictions resulted in a mean AUC?=?0.84 on D 1 and AUC?=?0.73 on D 2 . A significant difference in BCR‐free survival between the predicted classes (BCR?+?and BCR–) was observed ( P ?=?0.02) on D 2 compared to those obtained from GS ( P ?=?0.8), PSA ( P ?=?0.93) and PIRADS‐v2 ( P ?=?0.23). Data Conclusion Radiomic features from pretreatment bpMRI can be predictive of PCa BCR after therapy and may help identify men who would benefit from adjuvant therapy. Level of Evidence: 4 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2018;48:1626–1636
机译:已经显示出从MRI衍生的辐射瘤或计算机提取的纹理特征有助于定量表征前列腺癌(PCA)。在预测PCA的生物化学复发(BCR)的背景下尚未探讨辐射瘤。目的,用于鉴定源自预处理的预处理的辐射瘤特征的一组射出物特征,其可以预测PCA BCR。研究类型回顾。来自两个机构,I 1和I 2的120名PCA患者的受试者,从I 1和Idive验证设置D 2(n?=Δ70)的训练集D 1(n?=Δ70)。所有患者均为≥3岁。序列3T,T 2-重量(T 2 WI)和来自扩散加权序列的表观扩散系数(ADC)地图。 T 2 Wi的评估PCA区域(ROIS)由两位经验丰富的放射科医师注释。来自ROI的BPMRI(T 2 WI和ADC地图)的射线瘤特征。机器学习分类器(C BCR)培训,具有最佳辨别的射线组特征,以预测BCR(P BCR)。统计测试Wilcoxon RANK-SUP-SUM测试用P =ααβ-β-0.05被认为是统计学上显着的。使用Kaplan-Meier方法评估使用P BCR的3年在3年内进行BCR的存活的差异,并与Gleason得分(GS),PSA和PiRADS-V2进行比较。结果局部各向异性梯度取向(拼贴)的共发生统计(Collage)和来自T 2 Wi和ADC的Haralick特征与D 1上的BCR(p≤≤0.05)相关。 C BCR预测导致平均AUC?=?0.84 on D 1和AUC?= 0.73 ON D 2。与从GS(p≤x= 0.8),PSA(P <= 0.8),PSA(P ?=?0.93)和pirads-v2(p?= 0.23)。数据结论来自预处理BPMRI的辐射瘤功能可以在治疗后预测PCA BCR,可能有助于识别将从佐剂治疗中受益的男性。证据水平:4技术疗效:第5阶段J. MANG。恢复。 2018年成像; 48:1626-1636

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