Luisa Nogueira1, Sofia Brandão2, Eduarda Matos3, Rita Gouveia Nunes4, Hugo Alexandre Ferreira4, Joana Loureiro2, Isabel Ramos5. 1. Department of Radiology, School of Health Technology of Porto/Polytechnic Institute of Porto (ESTSP/IPP), Rua Valente Perfeito, 4400-330 Vila Nova de Gaia, Portugal; Department of Radiology, Hospital de São João/Faculty of Medicine of Porto University (FMUP), Alameda Prof. Hernâni Monteiro, 4200-319 Porto, Portugal. Electronic address: mlpnogueira@med.up.pt. 2. MRI Unit, Department of Radiology, Hospital de São João, Alameda Prof. Hernâni Monteiro, 4200-319 Porto, Portugal. 3. Department of Health Community, Institute of Biomedical Sciences Abel Salazar of Porto University (ICBAS), Porto, Portugal. 4. Institute of Biophysics and Biomedical Engineering (IBEB), Faculty of Sciences, University of Lisbon, Campo Grande, 1749-016 Lisboa, Portugal. 5. Department of Radiology, Hospital de São João/Faculty of Medicine of Porto University (FMUP), Alameda Prof. Hernâni Monteiro, 4200-319 Porto, Portugal.
Abstract
AIM: To assess how the joint use of apparent diffusion coefficient (ADC) and kinetic parameters (uptake phase and delayed enhancement characteristics) from dynamic contrast-enhanced (DCE) can boost the ability to predict breast lesion malignancy. MATERIALS AND METHODS: Breast magnetic resonance examinations including DCE and diffusion-weighted imaging (DWI) were performed on 51 women. The association between kinetic parameters and ADC were evaluated and compared between lesion types. Models with binary outcome of malignancy were studied using generalized estimating equations (GEE), (GEE), and using kinetic parameters and ADC values as malignancy predictors. Model accuracy was assessed using the corrected maximum quasi-likelihood under the independence confidence criterion (QICC). Predicted probability of malignancy was estimated for the best model. RESULTS: ADC values were significantly associated with kinetic parameters: medium and rapid uptake phase (p<0.001) and plateau and washout curve types (p=0.004). Comparison between lesion type showed significant differences for ADC (p=0.001), early phase (p<0.001), and curve type (p<0.001). The predicted probabilities of malignancy for the first ADC quartile (≤1.17×10(-3) mm(2)/s) and persistent, plateau and washout curves, were 54.6%, 86.9%, and 97.8%, respectively, and for the third ADC quartile (≥1.51×10(-3) mm(2)/s) were 3.2%, 15.5%, and 54.8%, respectively. The predicted probability of malignancy was less than 5% for 18.8% of the lesions and greater than 33% for 50.7% of the lesions (24/35 lesions, corresponding to a malignancy rate of 68.6%). CONCLUSION: The best malignancy predictors were low ADCs and washout curves. ADC and kinetic parameters provide differentiated information on the microenvironment of the lesion, with joint models displaying improved predictive performance.
AIM: To assess how the joint use of apparent diffusion coefficient (ADC) and kinetic parameters (uptake phase and delayed enhancement characteristics) from dynamic contrast-enhanced (DCE) can boost the ability to predict breast lesion malignancy. MATERIALS AND METHODS: Breast magnetic resonance examinations including DCE and diffusion-weighted imaging (DWI) were performed on 51 women. The association between kinetic parameters and ADC were evaluated and compared between lesion types. Models with binary outcome of malignancy were studied using generalized estimating equations (GEE), (GEE), and using kinetic parameters and ADC values as malignancy predictors. Model accuracy was assessed using the corrected maximum quasi-likelihood under the independence confidence criterion (QICC). Predicted probability of malignancy was estimated for the best model. RESULTS: ADC values were significantly associated with kinetic parameters: medium and rapid uptake phase (p<0.001) and plateau and washout curve types (p=0.004). Comparison between lesion type showed significant differences for ADC (p=0.001), early phase (p<0.001), and curve type (p<0.001). The predicted probabilities of malignancy for the first ADC quartile (≤1.17×10(-3) mm(2)/s) and persistent, plateau and washout curves, were 54.6%, 86.9%, and 97.8%, respectively, and for the third ADC quartile (≥1.51×10(-3) mm(2)/s) were 3.2%, 15.5%, and 54.8%, respectively. The predicted probability of malignancy was less than 5% for 18.8% of the lesions and greater than 33% for 50.7% of the lesions (24/35 lesions, corresponding to a malignancy rate of 68.6%). CONCLUSION: The best malignancy predictors were low ADCs and washout curves. ADC and kinetic parameters provide differentiated information on the microenvironment of the lesion, with joint models displaying improved predictive performance.
Authors: Ceren Yalnız; Juliana Rosenblat; David Spak; Wei Wei; Marion Scoggins; Carisa Le-Petross; Mark J Dryden; Beatriz Adrada; Başak E Doğan Journal: Eur J Breast Health Date: 2019-10-01