Prediction of tumor grade and lymphovascular space invasion in endometrial adenocarcinoma with MR imaging-based radiomic analysis

被引:60
作者
Bereby-Kahane, M. [1 ]
Dautry, R. [1 ]
Matzner-Lober, E. [2 ]
Cornelis, F. [3 ]
Sebbag-Sfez, D. [4 ]
Place, V [4 ]
Mezzadri, M. [5 ]
Soyer, P. [1 ,6 ]
Dohan, A. [1 ,6 ,7 ]
机构
[1] Hop Cochin, AP HP, Dept Radiol A, 27 Rue Faubourg St Jacques, F-75014 Paris, France
[2] ENSAE Format Continue, CREST, UMR 9194, F-91120 Palaiseau, France
[3] Hop Lariboisiere, AP HP, Dept Pathol, F-75010 Paris, France
[4] Hop Lariboisiere, AP HP, Dept Radiol, F-75010 Paris, France
[5] Hop Lariboisiere, AP HP, Dept Gynecol, F-75010 Paris, France
[6] Univ Paris, Descartes Paris 5, F-75006 Paris, France
[7] Inst Cochin, F-75014 Paris, France
关键词
Endometrial adenocarcinoma; Magnetic resonance imaging (MRI); Texture analysis; Radiomic analysis; Lymphovascular space invasion; CLINICAL-PRACTICE GUIDELINES; STAGING SYSTEM; CANCER; CARCINOMA; DIAGNOSIS; RISK; HETEROGENEITY; MODEL;
D O I
10.1016/j.diii.2020.01.003
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
Purpose: To evaluate the capabilities of two-dimensional magnetic resonance imaging (MRI)-based texture analysis features, tumor volume, tumor short axis and apparent diffusion coefficient (ADC) in predicting histopathological high-grade and lymphovascular space invasion (LVSI) in endometriat adenocarcinoma. Materials and methods: Seventy-three women (mean age: 66 +/- 11.5 [SD] years; range: 45-88 years) with endometriat adenocarcinoma who underwent MRI of the pelvis at 1.5-T before hysterectomy were retrospectively included. Texture analysis was performed using TexRAD (R) software on T2-weighted images and ADC maps. Primary outcomes were high-grade and LVSI prediction using histopathological analysis as standard of reference. After data reduction using ascending hierarchical classification analysis, a predictive model was obtained by stepwise multivariate logistic regression and performances were assessed using cross-validated receiver operator curve (ROC). Results: A total of 72 texture features per tumor were computed. Texture model yielded 52% sensitivity and 75% specificity for the diagnosis of high-grade tumor (areas under ROC curve [AUC] = 0.64) and 71% sensitivity and 59% specificity for the diagnosis of LVSI (AUC =0.59). Volumes and tumor short axis were greater for high-grade tumors (P = 0.0002 and P = 0.004, respectively) and for patients with LVSI (P = 0.004 and P = 0.0279, respectively). No differences in ADC values were found between high-grade and low-grade tumors and for LVSI. A tumor short axis >= 20 mm yielded 95% sensitivity and 75% specificity for the diagnosis of high-grade tumor (AUC = 0.86). Conclusion: MRI-based texture analysis is of limited value to predict high grade and LVSI of endometrial adenocarcinoma. A tumor short axis >= 20 mm is the best predictor of high grade and LVSI. (C) 2020 Societe francaise de radiologie. Published by Elsevier Masson SAS. All rights reserved.
引用
收藏
页码:401 / 411
页数:11
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