OBJECTIVE: This study aims to find out the benefits of adding histogram analysis of apparent diffusion coefficient (ADC) maps onto dynamic contrast-enhanced MRI (DCE-MRI) in predicting breast malignancy. METHODS: This study included 95 patients who were found with breast mass-like lesions from January 2014 to March 2016 (47 benign and 48 malignant). These patients were estimated by both DCE-MRI and diffusion-weighted imaging (DWI) and classified into two groups, namely, the benign and the malignant. Between these groups, the DCE-MRI parameters, including morphology, enhancement homogeneity, maximum slope of increase (MSI) and time-signal intensity curve (TIC) type, as well as histogram parameters generated from ADC maps were compared. Then, univariate and multivariate logistic regression analyses were conducted to determine the most valuable variables in predicting malignancy. Receiver operating characteristic curve analyses were taken to assess their clinical values. RESULTS: The lesion morphology, MSI and TIC Type (p < 0.05) were significantly different between the two groups. Multivariate logistic regression analyses revealed that irregular morphology, TIC Type II/III and ADC10 were important predictors for breast malignancy. Increased area under curve (AUC) and specificity can be achieved with Model 2 (irregular morphology + TIC Type II/III + ADC10 < 1.047 ×10-3 mm2 s-1) as the criterion than Model 1 (irregular morphology + TIC Type II/III) only (Model 2 vs Model 1; AUC, 0.822 vs 0.705; sensitivity, 68.8 vs 75.0%; specificity, 95.7 vs 66.0%). CONCLUSION: Irregular morphology, TIC Type II/III and ADC10 are indicators for predicting breast malignancy. Histogram analysis of ADC maps can provide additional value in predicting breast malignancy. Advances in knowledge: The morphology, MSI and TIC types in DCE-MRI examination have significant difference between the benign and malignant groups. A higher AUC can be achieved by using ADC10 as the diagnostic index than other ADC parameters, and the difference in AUC based on ADC10 and ADCmean was statistically significant. The irregular morphology, TIC Type II/III and ADC10 were significant predictors for malignant lesions.
OBJECTIVE: This study aims to find out the benefits of adding histogram analysis of apparent diffusion coefficient (ADC) maps onto dynamic contrast-enhanced MRI (DCE-MRI) in predicting breast malignancy. METHODS: This study included 95 patients who were found with breast mass-like lesions from January 2014 to March 2016 (47 benign and 48 malignant). These patients were estimated by both DCE-MRI and diffusion-weighted imaging (DWI) and classified into two groups, namely, the benign and the malignant. Between these groups, the DCE-MRI parameters, including morphology, enhancement homogeneity, maximum slope of increase (MSI) and time-signal intensity curve (TIC) type, as well as histogram parameters generated from ADC maps were compared. Then, univariate and multivariate logistic regression analyses were conducted to determine the most valuable variables in predicting malignancy. Receiver operating characteristic curve analyses were taken to assess their clinical values. RESULTS: The lesion morphology, MSI and TIC Type (p < 0.05) were significantly different between the two groups. Multivariate logistic regression analyses revealed that irregular morphology, TIC Type II/III and ADC10 were important predictors for breast malignancy. Increased area under curve (AUC) and specificity can be achieved with Model 2 (irregular morphology + TIC Type II/III + ADC10 < 1.047 ×10-3 mm2 s-1) as the criterion than Model 1 (irregular morphology + TIC Type II/III) only (Model 2 vs Model 1; AUC, 0.822 vs 0.705; sensitivity, 68.8 vs 75.0%; specificity, 95.7 vs 66.0%). CONCLUSION: Irregular morphology, TIC Type II/III and ADC10 are indicators for predicting breast malignancy. Histogram analysis of ADC maps can provide additional value in predicting breast malignancy. Advances in knowledge: The morphology, MSI and TIC types in DCE-MRI examination have significant difference between the benign and malignant groups. A higher AUC can be achieved by using ADC10 as the diagnostic index than other ADC parameters, and the difference in AUC based on ADC10 and ADCmean was statistically significant. The irregular morphology, TIC Type II/III and ADC10 were significant predictors for malignant lesions.
Authors: Lidia Rosi Medeiros; Célia Scapin Duarte; Daniela Dornelles Rosa; Maria Isabel Edelweiss; Marcia Edelweiss; Fábio Rosa Silva; Erik Paul Winnnikow; Patrícia Duarte Simões Pires; Maria Inês Rosa Journal: Breast Cancer Res Treat Date: 2011-01-08 Impact factor: 4.872
Authors: F D Pineda; M Medved; X Fan; M K Ivancevic; H Abe; A Shimauchi; G M Newstead; G S Karczmar Journal: Br J Radiol Date: 2015-03-18 Impact factor: 3.039
Authors: L De Smedt; J Lemahieu; S Palmans; O Govaere; T Tousseyn; E Van Cutsem; H Prenen; S Tejpar; M Spaepen; G Matthijs; C Decaestecker; X Moles Lopez; P Demetter; I Salmon; X Sagaert Journal: Br J Cancer Date: 2015-06-11 Impact factor: 7.640
Authors: Fergus Davnall; Connie S P Yip; Gunnar Ljungqvist; Mariyah Selmi; Francesca Ng; Bal Sanghera; Balaji Ganeshan; Kenneth A Miles; Gary J Cook; Vicky Goh Journal: Insights Imaging Date: 2012-10-24
Authors: Joao V Horvat; Aditi Iyer; Elizabeth A Morris; Aditya Apte; Blanca Bernard-Davila; Danny F Martinez; Doris Leithner; Olivia M Sutton; R Elena Ochoa-Albiztegui; Dilip Giri; Katja Pinker; Sunitha B Thakur Journal: Contrast Media Mol Imaging Date: 2019-11-22 Impact factor: 3.161