Preoperative vascular heterogeneity and aggressiveness assessment of pituitary macroadenoma based on dynamic contrast-enhanced MRI texture analysis

• Published in: European journal of radiology Vol. 129; p. 109125
• Main Authors: Liu, YangYing Qiu, Gao, Bing Bing, Dong, Bin, Padikkalakandy Cheriyath, Shesnia Salim, Song, Qing Wei, Xu, Bin, Wei, Qiang, Xie, Li Zhi, Guo, Yan, Miao, Yan Wei
• Format: Journal Article
• Language: English
• Published: Ireland Elsevier B.V 01.08.2020
• Subjects: Adenoma - blood supply; Adenoma - diagnostic imaging; Adenoma - pathology; Adult; Aged; Contrast Media; DCE-MRI; Female; Humans; Image Enhancement - methods; Magnetic Resonance Imaging - methods; Male; Microvascular permeability; Middle Aged; Neoplasm Invasiveness; Neovascularization, Pathologic - diagnostic imaging; Neovascularization, Pathologic - pathology; Pituitary macroadenoma; Pituitary Neoplasms - blood supply; Pituitary Neoplasms - diagnostic imaging; Pituitary Neoplasms - pathology; Preoperative Care - methods; Texture analysis; Vascular heterogeneity; PRC; GLRLM; Pituitary macroadenoma; Texture analysis; LAVA; DCE-MRI; ICA; Microvascular permeability; DCE; ROC; ICC; GLCM; PA; EES; Vascular heterogeneity; PM; AIF; WHO
• ISSN: 0720-048X; 1872-7727
• DOI: 10.1016/j.ejrad.2020.109125

•DCE-MRI texture analysis has significant value for the diagnosis of aggressive PM.•Total model provide more diagnostic confidence on aggressive PM.•Therapeutic schedule for a patient with PM can be more appropriate To assess the vascular heterogeneity and aggressiveness of pituitary macroadenomas (PM) using texture analysis based on Dynamic Contrast-Enhanced MRI (DCE-MRI). Fifty patients with pathologically confirmed PM, including 32 patients with aggressive PM (aggressive group) and 18 patients with non-aggressive PM (non-aggressive group), were included in this study. The preoperative DCE-MRI and clinical data were collected from all patients. The features based on Ktrans, Ve, and Kep were generated using Omni-Kinetics software. Independent-samples t-test and Mann-Whitney U test were used for comparison between two groups. Logistic regression analysis was used to determine the optimal model for distinguishing aggressive and non-aggressive PM. Six features related to tumor morphology, 24 features in Ktrans, 20 features in Ve, and 3 features in Kep were significantly different between the aggressive and non-aggressive groups. Volume count, gray-level non-uniformity in Ktrans, voxel value sum in Ve and run-length non-uniformity in Kep (AUC = 0.816, 0.903, 0.785, 0.813) were considered the best feature for tumor diagnosis. After modeling, the diagnosis efficiency of mean model and total model was desirable (AUC = 0.859 and 0.957), and the diagnostic efficiency of morphological, Ktrans, Ve and Kep features model was improved (AUC = 0.845, 0.951, 0.847, 0.804). Texture analysis based on DCE-MRI elucidates the vascular heterogeneity and aggressiveness of pituitary adenoma. The total model could be used as a new noninvasive method for predicting the aggressiveness of pituitary macroadenoma.