Examples of four breast lesions with measured and EMM fitted kinetic curves. For each kinetic curve, the measured signal intensity values are indicated with triangles, and the fitted EMM curve with solid lines. From the top to bottom: Benign mass lesion, malignant mass lesion, benign nonmass lesion, and malignant nonmass lesion. The lesions are indicated by a white arrow.
The average value standard deviation for each EMM parameter in benign (white bars) and malignant (gray bars) lesions, stratified by type of enhancement as mass or nonmass. After correcting for multiple tests of significance, the parameters SER and demonstrated significant differences among malignant and benign mass lesions.
Fitted binormal ROC curves generated by the ROCKIT software are shown for the EMM parameters with the highest, and lowest, values in mass and nonmass lesions. SER (solid blue line) and A (solid red line) had the highest values in mass and nonmass lesions, respectively. A (dashed blue line) and (dashed red line) had the lowest values in mass and nonmass lesions, respectively.
A list and description of the EMM parameters derived from the primary parameters , , and .
Distributions of BI-RADS categories for the qualitative assessment of the initial rise and delayed phased of kinetic curves for benign and malignant lesions, as well as the subtypes of benign and malignant lesions considered here. There were two benign and two malignant lesions classified as focus type enhancement, which do not appear in the table below.
The primary and derived diagnostic parameters calculated from the EMM in malignant and benign lesions. Reported values are mean standard deviation of the sample for all cases. The value after Student -test is shown for each parameter, along with the required value for significance according to the Holm–Bonferroni correction for multiple tests of significance. Numbers in bold indicate that there was a statistically significant difference between benign and malignant lesions, according to the Student’s -test and after using the Holm–Bonferroni correction for multiple comparisons.
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