Hee Jung Shin1, Hak Hee Kim2, Ki Chang Shin3, Yoo Sub Sung3, Joo Hee Cha2, Jong Won Lee4, Byung Ho Son4, Sei Hyun Ahn4. 1. Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan, College of Medicine, 88 Olympic-ro, 43-gil, Songpa-gu, Seoul, 138-736, South Korea. Electronic address: docshin@amc.seoul.kr. 2. Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan, College of Medicine, 88 Olympic-ro, 43-gil, Songpa-gu, Seoul, 138-736, South Korea. 3. Department of Radiology and Research Institute of Radiology, Medical Imaging Laboratory, Asan Medical Center, University of Ulsan, College of Medicine, 88 Olympic-ro, 43-gil, Songpa-gu, Seoul, 138-736, South Korea. 4. Department of Breast and Endocrine Surgery, Asan Medical Center, University of Ulsan, College of Medicine, 88 Olympic-ro, 43-gil, Songpa-gu, Seoul, 138-736, South Korea.
Abstract
PURPOSE: To assess whether perfusion and diffusion parameters were different between low-risk tumors and non-low-risk tumors. MATERIALS AND METHODS: We prospectively enrolled 87 patients with 91 tumors patients (mean, 49.6 years; range, 29-74 years) who underwent definitive surgery. We defined estrogen receptor (ER)-positive tumors with low histologic grade (HG), low Ki67 (<14%), and negative lymph node metastasis as a low-risk breast cancer. We obtained quantitative and semiquantitative perfusion parameters and apparent diffusion coefficient (ADC) for all tumors. We compared perfusion parameters and ADCs between low-risk tumors (n=33; 36%) and the others (n=58; 64%) using Fisher's exact test, Chi-square test, and student t-test. We developed empirical model to predict low-risk tumor using logistic regression analysis and receiver operating characteristics (ROC) analysis. RESULTS: On univariate analysis, wash-in and the initial area under the curve on qualitative analysis (iAUCqualitative) were significantly different according to HG, ER, HER-2, Ki67 and lymphovascular invasion (P<.05 for all variables). ADCdiff was significantly different according to HG, HER-2, and Ki67 status (P=.010, .007, and .013). On multivariate analysis, Ktrans, iAUCqualitative, and ADCdiff were the significant variables for the prediction of low-risk tumors, and the area under the ROC curve (AUC) of combined parameters was 0.78, which was higher than those of the individual parameter. ADCdiff was positively correlated with wash-in (r=0.263) and iAUCqualitative (r=0.245), respectively. CONCLUSION: The prediction model using Ktrans, wash in, iAUCqualitative, and ADCdiff on DCE-MRI and DWI could be helpful for identifying of low-risk breast cancer and may be used as an imaging biomarker to guide the treatment plan.
PURPOSE: To assess whether perfusion and diffusion parameters were different between low-risk tumors and non-low-risk tumors. MATERIALS AND METHODS: We prospectively enrolled 87 patients with 91 tumorspatients (mean, 49.6 years; range, 29-74 years) who underwent definitive surgery. We defined estrogen receptor (ER)-positive tumors with low histologic grade (HG), low Ki67 (<14%), and negative lymph node metastasis as a low-risk breast cancer. We obtained quantitative and semiquantitative perfusion parameters and apparent diffusion coefficient (ADC) for all tumors. We compared perfusion parameters and ADCs between low-risk tumors (n=33; 36%) and the others (n=58; 64%) using Fisher's exact test, Chi-square test, and student t-test. We developed empirical model to predict low-risk tumor using logistic regression analysis and receiver operating characteristics (ROC) analysis. RESULTS: On univariate analysis, wash-in and the initial area under the curve on qualitative analysis (iAUCqualitative) were significantly different according to HG, ER, HER-2, Ki67 and lymphovascular invasion (P<.05 for all variables). ADCdiff was significantly different according to HG, HER-2, and Ki67 status (P=.010, .007, and .013). On multivariate analysis, Ktrans, iAUCqualitative, and ADCdiff were the significant variables for the prediction of low-risk tumors, and the area under the ROC curve (AUC) of combined parameters was 0.78, which was higher than those of the individual parameter. ADCdiff was positively correlated with wash-in (r=0.263) and iAUCqualitative (r=0.245), respectively. CONCLUSION: The prediction model using Ktrans, wash in, iAUCqualitative, and ADCdiff on DCE-MRI and DWI could be helpful for identifying of low-risk breast cancer and may be used as an imaging biomarker to guide the treatment plan.
Authors: Marco Macchini; Martina Ponziani; Andrea Prochowski Iamurri; Mirco Pistelli; Mariagrazia De Lisa; Rossana Berardi; Gian Marco Giuseppetti Journal: Radiol Med Date: 2018-06-05 Impact factor: 3.469
Authors: Christopher Kurz; Giulia Buizza; Guillaume Landry; Florian Kamp; Moritz Rabe; Chiara Paganelli; Guido Baroni; Michael Reiner; Paul J Keall; Cornelis A T van den Berg; Marco Riboldi Journal: Radiat Oncol Date: 2020-05-05 Impact factor: 3.481