Texture features and pharmacokinetic parameters in differentiating benign and malignant breast lesions by dynamic contrast enhanced magnetic resonance imaging

Abstract

Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) has become a powerful tool for the diagnosis of breast cancer in the clinical setting due to its high sensitivity and specificity. Pharmacokinetic parameters, including Ktrans and area under the curve (AUC), and texture features derived from DCE-MRI have been used to specify the characteristics inside tumors. In the present study, 56 patients (average age 45.3±11.1; range 25-69 years) with histopathologically proved breast tumors were analyzed using the pharmacokinetic parameters and texture features. Malignant tumors displayed higher Ktrans and AUC values than the benign, Ktrans exhibited a significantly difference between the malignant and benign tumors (P=0.001) compared with the AUC values (P=0.029); texture features from DCE-MRI images and pharmacokinetic parameter maps also showed a good diagnostic ability. Alongside the routine method, principal components analysis (PCA) and Fisher discriminant analysis (FDA) were employed on these texture features to differentiate the breast lesions automatically. The Factor-1 scores of PCA were used to divide the patients into two groups, and the diagnosing accuracies of the FDA method on the texture features from DCE-MRI images, Ktrans maps, AUC maps were 93, 98 and 98%, with a cross validation accuracies of 82, 77 and 77%, respectively. To conclude, pharmacokinetic parameters, texture features and the combined computer-assisted classification method were discussed. All method involved in this study may be a potential assisted tool for radiological analysis on breast.

Performance evaluation of texture analysis based on kinetic parametric maps from breast DCE‐MRI in classifying benign from malignant lesions

“…In our study, SPSS was used for statistical analysis ( P < .05 was considered statistically significant). The Kolmogorov‐Smirnov test was used to test the normality of texture features extracted from the lesion area on each kinetic parametric map 42‐44 . If the distribution was normal, a Student t test was used to investigate the difference between the texture features of benign and malignant breast lesions 42‐44 .…”

“…The Kolmogorov‐Smirnov test was used to test the normality of texture features extracted from the lesion area on each kinetic parametric map 42‐44 . If the distribution was normal, a Student t test was used to investigate the difference between the texture features of benign and malignant breast lesions 42‐44 . Otherwise, the Mann‐Whitney U test was used 42‐44 .…”

“…If the distribution was normal, a Student t test was used to investigate the difference between the texture features of benign and malignant breast lesions 42‐44 . Otherwise, the Mann‐Whitney U test was used 42‐44 . Those statistically significantly different features were used as input for establishing the classification models subsequently.…”

Male axillary accessory breast cancer

“…The age of onset is above 50 years old. [ 5 – 6 ] Patients often come for further treatments with the presentation of a palpable and painful mass in the axilla. Female patients often feel discomfort in axilla, which is related to menstrual cycle.…”

Morphological Estimation of Cellularity on Neo-Adjuvant Treated Breast Cancer Histological Images

“…After segmentation, many features can be extracted, for instance, cell shape, size, and texture [ 33 , 34 , 35 ], and also, features from regional and the global image can provide valuable information [ 36 ]. These parameters can be used for diagnostic purposes to classify tissue malignancy [ 36 , 37 , 38 , 39 ] and also for grade disease level assignment. Ref.…”