• Amandine Crombé, Olivier Saut, Jerome Guigui, Antoine Italiano, Xavier Buy, and Michèle Kind.
• Department of Radiology, Institut Bergonié, Comprehensive Cancer Center, Bordeaux, France.
• J Magn Reson Imaging. 2019 Dec 1; 50 (6): 1773-1788.

BackgroundEvaluating heterogeneity in tumor vascularization through texture analysis could improve predictions of patients' outcome and response evaluation.PurposeTo investigate the influence of temporal parameters on texture features extracted from dynamic contrast-enhanced (DCE)-MRI parametric maps.Study TypeProspective cross-sectional study.SubjectsTwenty-five adults with soft-tissue sarcoma (STS), median age: 68 years.Field Strength/SequenceDCE-MRI acquisition using a CAIPIRINHA-Dixon-TWIST-VIBE sequence at 1.5T (temporal resolutions: 2 sec, duration: 5 min).AssessmentThe area under time-intensity curve (AUC) and Ktrans maps were generated for several temporal resolution (dt = 2 sec, 4 sec, 6 sec, 8 sec, 10 sec, 12 sec, 20 sec) and scan durations (T = 3 min, 4 min, 5 min for a 6-sec sampling) by downsampling and truncating the initial DCE-MRI sequence. Tumor volume was manually segmented and propagated on all parametric maps. Thirty-two first- and second order-texture features were extracted per map to quantify the intratumoral heterogeneity.Statistical TestsThe influence of temporal parameters on texture features was studied with repeated-measures analysis of variance (or nonparametric equivalent). The dispersion of each texture feature depending on temporal parameters was estimated with coefficients of variation (CVs). The performances of multivariate models to predict the response to chemotherapy (ie, binary logistic regression based on the baseline texture features) were compared.ResultsThe temporal resolution had a significant influence on 12/32 (37.5%) and 14/32 (43.8%) texture features evaluated on AUC and Ktrans maps, respectively (range of P < 0.0001-0.0395). Scan duration had a significant influence on 23/32 (71.9%) texture features from Ktrans map (range of P < 0.0001-0.0321). Dispersion was high (mean CV >0.5) with sampling for 2/32 (6.3%) and 10/32 (31.3%) features from AUC and Ktrans maps, respectively; and with truncating for 6/32 (18.8%) features from Ktrans map. The area under the receiver operating characteristics curve of predictive models ranged from 0.77 (95% confidence interval [CI] = [0.54-1.00], with dt = 6 sec T = 4 min) to 0.90 (95% CI = [0.74-1.00], with dt = 6 sec T = 5 min).Data ConclusionThe values of texture features extracted from DCE-MRI parametric maps can be influenced by temporal parameters, which can lead to variations in performance of predictive models.Level Of Evidence2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1773-1788.© 2019 International Society for Magnetic Resonance in Medicine.

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