Preoperative tumor texture analysis on MRI predicts high-risk disease and reduced survival in endometrial cancer
Preoperative tumor texture analysis on MRI predicts high-risk disease and reduced survival in endometrial cancer
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DOI：
10.1002/jmri.26184
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发表时间：
2018-12-01
影响因子：
4.4
通讯作者：
Haldorsen, Ingfrid S.
中科院分区：
文献类型：
--
作者：
Ytre-Hauge, Sigmund;Dybvik, Julie A.;Haldorsen, Ingfrid S.
背景妇科医生对子宫内膜癌术前危险分层方法的改进提出了很高的要求。纹理分析是一种量化图像异质性的方法，越来越多的报道作为一种有前途的诊断工具，在各种癌症类型，但在子宫内膜癌中很大程度上尚未探索。目的探讨术前MRI肿瘤纹理参数是否与已知的预后特征有关（子宫深部肌层浸润，宫颈间质浸润，淋巴结转移，和高危组织学亚型）和子宫内膜癌患者的预后。研究类型前瞻性队列研究。人群/受试者从2009年4月至2013年11月，180例子宫内膜癌患者被纳入研究，研究持续至2017年1月。场强/序列术前盆腔MRI，包括对比增强T-1加权（T，1）c）、1.5T下的T-2加权和弥散加权成像。评估在显示最大横截面肿瘤面积的切片上手动绘制肿瘤感兴趣区域（ROI），使用专有研究软件TexRAD进行分析。采用过滤-直方图技术，计算纹理参数标准差、熵、阳性像素均值（MPP）、偏度和峰度，统计检验纹理参数与组织学特征之间的相关性通过单变量和多变量logistic回归进行评估，包括术前活检状态和常规MRI结果的调整模型。结果表观扩散系数（ADC）图中肿瘤熵值高者独立预测深肌层浸润（OR 3.2，P lt 0.001），T1 c图中MPP值高者独立预测高危组织学亚型（OR 1.01，P = 0.004）。T（1）c图像的高峰度预示着无复发和无进展生存率的降低（危险比[HR] 1.5，P lt 0.001）。数据结论MRI衍生的肿瘤纹理参数独立预测子宫内膜癌的深肌层浸润、高风险组织学亚型和降低的生存率，因此，代表了有前途的成像生物标志物，提供了更精确的术前风险评估，最终可能使子宫内膜癌更好地定制治疗策略。
Background Improved methods for preoperative risk stratification in endometrial cancer are highly requested by gynecologists. Texture analysis is a method for quantification of heterogeneity in images, increasingly reported as a promising diagnostic tool in various cancer types, but largely unexplored in endometrial cancer.Purpose To explore whether tumor texture parameters from preoperative MRI are related to known prognostic features (deep myometrial invasion, cervical stroma invasion, lymph node metastases, and high-risk histological subtype) and to outcome in endometrial cancer patients.Study type Prospective cohort study.Population/Subjects In all, 180 patients with endometrial carcinoma were included from April 2009 to November 2013 and studied until January 2017.Field Strength/Sequences Preoperative pelvic MRI including contrast-enhanced T-1-weighted (T(1)c), T-2-weighted, and diffusion-weighted imaging at 1.5T.Assessment Tumor regions of interest (ROIs) were manually drawn on the slice displaying the largest cross-sectional tumor area, using the proprietary research software TexRAD for analysis. With a filtration-histogram technique, the texture parameters standard deviation, entropy, mean of positive pixels (MPP), skewness, and kurtosis were calculated.Statistical Tests Associations between texture parameters and histological features were assessed by uni- and multivariable logistic regression, including models adjusting for preoperative biopsy status and conventional MRI findings. Multivariable Cox regression analysis was used for survival analysis.Results High tumor entropy in apparent diffusion coefficient (ADC) maps independently predicted deep myometrial invasion (odds ratio [OR] 3.2, P lt 0.001), and high MPP in T(1)c images independently predicted high-risk histological subtype (OR 1.01, P = 0.004). High kurtosis in T(1)c images predicted reduced recurrence- and progression-free survival (hazard ratio [HR] 1.5, P lt 0.001) after adjusting for MRI-measured tumor volume and histological risk at biopsy.Data Conclusion MRI-derived tumor texture parameters independently predicted deep myometrial invasion, high-risk histological subtype, and reduced survival in endometrial carcinomas, and thus, represent promising imaging biomarkers providing a more refined preoperative risk assessment that may ultimately enable better tailored treatment strategies in endometrial cancer.