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膀胱癌的定量评估作为膀胱成像报告和数据系统的补充,以预测肌肉层侵犯。

Quantitation of bladder cancer for the prediction of muscle layer invasion as a complement to the vesical imaging-reporting and data system.

机构信息

Department of Radiology, Seoul National University Bundang Hospital, Bundang-gu, Seongnam-si, Gyeonggi-do, South Korea.

Department of Pathology, Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, South Korea.

出版信息

Eur Radiol. 2021 Mar;31(3):1656-1666. doi: 10.1007/s00330-020-07224-7. Epub 2020 Sep 4.

Abstract

OBJECTIVES

To examine the diagnostic performance of Vesical Imaging-Reporting and Data System (VIRADS) and to find a quantitative indicator for predicting muscle layer invasion of bladder cancer.

METHODS

3-T MRI of 82 patients performed before transurethral resection of bladder tumors or radical cystectomy between July 2018 and June 2019 were retrospectively analyzed. For one index lesion of each patient, two radiologists independently assigned VIRADS score and measured tumor-wall interface (contact length between tumor and bladder wall) on T2-weighted, diffusion-weighted, and dynamic contrast-enhanced MRI. Inter-reader agreement was assessed, and logistic regression analysis was performed to find indicators of muscle layer invasion. Comparison of indicators' diagnostic performance was done with receiver operating characteristic (ROC) curve and generalized linear model analyses. Optimal cutoff point was determined by the Youden index J.

RESULTS

Inter-reader agreement was at least substantial for VIRADS categorization (κ 0.77-0.81), and almost perfect for tumor-wall interface (intraclass correlation coefficient 0.88-0.90). Tumor-wall interface (odds ratio [OR] 1.90-2.00) and VIRADS score (OR 8.59-8.89) were independently associated with muscle layer invasion (p ≤ 0.02). For VIRADS, area under the ROC curve (AUROC) was 0.94, and the accuracy was 0.93 at score 3, the optimal threshold for predicting muscle layer invasion. Depending on the MRI sequence, tumor-wall interface showed AUROCs of 0.90-0.92 and accuracy of 0.84-0.90 at suggested thresholds (3 ± 0.3 cm). Tumor-wall interface showed insignificant differences in accuracy compared with VIRADS (p > 0.10), except as measured on diffusion-weighted images (p = 0.01).

CONCLUSIONS

VIRADS is a good predictor of muscle layer invasion. As an independent quantitative indicator, tumor-wall interface may complement VIRADS to enhance prediction.

KEY POINTS

• Vesical Imaging-Reporting and Data System (VIRADS) is a promising predictor of muscle invasion of bladder cancer with good reproducibility, as suggested by previous studies. • VIRADS score and the tumor-wall interface (curvilinear contact length between the tumor and the bladder wall) are independent predictors of muscle layer invasion. • As an easy-to-use quantitative indicator, tumor-wall interface is expected to be used as an indicator complementary to VIRADS, a qualitative indicator.

摘要

目的

研究膀胱影像报告和数据系统(VIRADS)的诊断性能,并找到一种预测膀胱癌侵犯肌层的定量指标。

方法

回顾性分析 2018 年 7 月至 2019 年 6 月间 82 例经尿道膀胱肿瘤切除术或根治性膀胱切除术患者的 3T MRI 资料。对每位患者的一个病灶,两位放射科医生独立进行 VIRADS 评分,并在 T2 加权、弥散加权和动态对比增强 MRI 上测量肿瘤-膀胱壁界面(肿瘤与膀胱壁之间的接触长度)。评估了读者间的一致性,并进行了逻辑回归分析,以寻找肌肉层侵犯的指标。使用受试者工作特征(ROC)曲线和广义线性模型分析比较指标的诊断性能。通过 Youden 指数 J 确定最佳截断点。

结果

VIRADS 分类的读者间一致性至少为中等(κ0.77-0.81),肿瘤-膀胱壁界面的一致性几乎为完美(组内相关系数 0.88-0.90)。肿瘤-膀胱壁界面(比值比[OR]1.90-2.00)和 VIRADS 评分(OR 8.59-8.89)与肌层侵犯独立相关(p≤0.02)。对于 VIRADS,ROC 曲线下面积(AUROC)为 0.94,当评分达到 3 分时,准确性为 0.93,为预测肌层侵犯的最佳阈值。根据 MRI 序列的不同,肿瘤-膀胱壁界面的 AUROC 为 0.90-0.92,在建议的阈值(3±0.3cm)下的准确性为 0.84-0.90。肿瘤-膀胱壁界面的准确性与 VIRADS 无显著差异(p>0.10),但在弥散加权图像上的测量结果除外(p=0.01)。

结论

VIRADS 是预测膀胱癌肌层侵犯的良好指标。作为一种独立的定量指标,肿瘤-膀胱壁界面可能补充 VIRADS 以增强预测。

关键点

  • 膀胱影像报告和数据系统(VIRADS)是一种有前途的膀胱癌肌层侵犯预测指标,具有良好的可重复性,如既往研究所示。

  • VIRADS 评分和肿瘤-膀胱壁界面(肿瘤与膀胱壁之间的曲线接触长度)是肌层侵犯的独立预测指标。

  • 作为一种易于使用的定量指标,肿瘤-膀胱壁界面有望作为一种与定性指标 VIRADS 互补的指标使用。

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