Details

Autor(en) / Beteiligte

• Crombé, Amandine
• Saut, Olivier
• Guigui, Jerome
• Italiano, Antoine
• Buy, Xavier
• Kind, Michèle

Titel

Influence of temporal parameters of DCE‐MRI on the quantification of heterogeneity in tumor vascularization

Ist Teil von

• Journal of magnetic resonance imaging, 2019-12, Vol.50 (6), p.1773-1788

Ort / Verlag

Hoboken, USA: John Wiley & Sons, Inc

Erscheinungsjahr

2019

Quelle

Wiley-Blackwell Journals

Beschreibungen/Notizen

• Background Evaluating heterogeneity in tumor vascularization through texture analysis could improve predictions of patients' outcome and response evaluation. Purpose To investigate the influence of temporal parameters on texture features extracted from dynamic contrast‐enhanced (DCE)‐MRI parametric maps. Study type Prospective cross‐sectional study. Subjects Twenty‐five adults with soft‐tissue sarcoma (STS), median age: 68 years. Field Strength/Sequence DCE‐MRI acquisition using a CAIPIRINHA‐Dixon‐TWIST‐VIBE sequence at 1.5T (temporal resolutions: 2 sec, duration: 5 min). Assessment The 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 Tests The 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. Results The 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 Conclusion The 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 Evidence: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1773–1788.