Amandine Crombé1,2, Olivier Saut2, Jerome Guigui1, Antoine Italiano3, Xavier Buy1, Michèle Kind1. 1. Department of Radiology, Institut Bergonié, Comprehensive Cancer Center, Bordeaux, France. 2. University of Bordeaux, IMB, UMR CNRS 5251, INRIA Project Team Monc, Talence, France. 3. Department of Medical Oncology, Institut Bergonié, Comprehensive Cancer Center, Bordeaux, France.
Abstract
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.
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.
Authors: Thomas Gp Grünewald; Marta Alonso; Sofia Avnet; Ana Banito; Stefan Burdach; Florencia Cidre-Aranaz; Gemma Di Pompo; Martin Distel; Heathcliff Dorado-Garcia; Javier Garcia-Castro; Laura González-González; Agamemnon E Grigoriadis; Merve Kasan; Christian Koelsche; Manuela Krumbholz; Fernando Lecanda; Silvia Lemma; Dario L Longo; Claudia Madrigal-Esquivel; Álvaro Morales-Molina; Julian Musa; Shunya Ohmura; Benjamin Ory; Miguel Pereira-Silva; Francesca Perut; Rene Rodriguez; Carolin Seeling; Nada Al Shaaili; Shabnam Shaabani; Kristina Shiavone; Snehadri Sinha; Eleni M Tomazou; Marcel Trautmann; Maria Vela; Yvonne Mh Versleijen-Jonkers; Julia Visgauss; Marta Zalacain; Sebastian J Schober; Andrej Lissat; William R English; Nicola Baldini; Dominique Heymann Journal: EMBO Mol Med Date: 2020-10-13 Impact factor: 12.137