Multiparametric MRI Combined with Apparent Diffusion Coefficient Histogram Analysis for Assessing Variant Histology in Urothelial Carcinoma
10.13471/j.cnki.j.sun.yat-sen.univ（med.sci）.2023.0615
- VernacularTitle:多参数磁共振成像联合表观弥散系数直方图分析预测膀胱癌的病理分化
- Author:
Ling-min KONG
1
;
Jian LING
2
;
Qian CAI
1
;
Zhi-hua WEN
1
;
Yan GUO
1
;
Huan-jun WANG
1
Author Information
1. Department of Radiology， The First Affiliated Hospital， Sun Yat-sen University， Guangzhou 510080， China
2. Department of Radiology， The East Division of the First Affiliated Hospital， Sun Yat-Sen University， Guangzhou 510700， China
- Publication Type:Journal Article
- Keywords:
urothelial carcinoma， urothelium， magnetic resonance imaging （MRI）， histogram， quantitative;
variant
- From:
Journal of Sun Yat-sen University(Medical Sciences)
2023;44(6):1008-1015
- CountryChina
- Language:Chinese
-
Abstract:
ObjectiveTo investigate the feasibility of multiparametric MRI （mpMRI） combined with histogram analysis of apparent diffusion coefficient （ADC） in the assessment of patients with variant histology （VH） of urothelial carcinoma （UC）. MethodWe retrospectively analyzed the data of patients pathologically diagnosed with UC who underwent mpMRI in the First Affiliated Hospital of Sun Yat-sen University between March 2015 and March 2023. The patients were divided into VH group （urothelial carcinoma mixed with other histologies） and non-VH group （pure urothelial carcinoma） according to pathological results. We performed propensity score 1：1 nearest-neighbor matching on the two groups based on age and gender and 49 patients were included in each group. The regions of interest （ROIs） of the whole tumor were delineated manually by using ITK-SNAP software and Pyradiomics was applied to extract ADC histogram parameters. We compared the clinicopathological data， MRI morphological features and ADC histogram parameters between the groups. Multivariate logistic regression was used to identify independent risk factors and construct the prediction model. Receiver operating characteristic （ROC） curve analyses were performed to evaluate the diagnostic performance of these parameters for determining VH of UC. ResultsMRI morphological features including the lesion shape， vesical imaging-reporting and data system （Ⅵ-RADS）score， enhancement pattern and suspicious lymph node metastasis were markedly different between the two groups （all P < 0.05）. ADC mean， ADC median， ADC25th， ADC75th， ADC10th and ADC90th were significantly lower in patients with VH than those in non-VH group （all P<0.05）. Multivariate logistic regression analysis showed enhancement pattern， ADC25th， ADC75th and ADC mean were independent predictors （P < 0.05）. The combined model yielded the best predictive performance， with an area under the ROC curve （AUC） of 0.91 （95% CI： 0.83-0.96）. ConclusionsMpMRI combined with whole-tumor histogram analysis of ADC can serve as a reliable method for evaluating the presence of VH in UC， further to assist the clinical decision making.
