Abstract
BackgroundThe apparent diffusion coefficient (ADC) is a highly diagnostic factor in discriminating malignant and benign breast masses in diffusion-weighted magnetic resonance imaging (DW-MRI). The combination of ADC and other pictorial characteristics has improved lesion type identification accuracy. The objective of this study was to reassess the findings on an independent patient group by changing the magnetic field from 1.5-Tesla to 3.0-Tesla.MethodsThis retrospective study consisted of a training group of 234 female patients, including 85 benign and 149 malignant lesions, imaged using 1.5-Tesla MRI, and a test group of 95 female patients, including 19 benign and 85 malignant lesions, imaged using 3.0-Tesla MRI. The lesion of interest was segmented from the raw image and four sets of measurements describing the morphology, kinetics, DW-MRI, and texture of the pictorial properties of each lesion were obtained. Each lesion was characterized by 28 features in total. Three classical machine-learning algorithms were used to build prediction models on the training group, which evaluated the prognostic performance of the multi-sided features in three scenarios. To reduce information redundancy, five highly diagnostic factors were selected to obtain a compact yet informative characterization of the lesion status.ResultsThree classification models were built on the training of 1.5-Tesla patients and were tested on the independent 3.0-Tesla test group. The following results were found. i) Characterization of breast masses in a multi-sided way dramatically increased prediction performance. The usage of all features gave a higher performance in both sensitivity and specificity than any individual feature groups or their combinations. ii) ADC was a highly effective factor in improving the sensitivity in discriminating malignant from benign masses. iii) Five features, namely ADC, Sum Average, Entropy, Elongation, and Sum Variance, were selected to achieve the highest performance in diagnosis of the 3.0-Tesla patient group.ConclusionsThe combination of ADC and other multi-sided characteristics can increase the capability of discriminating malignant and benign breast lesions, even under different imaging protocols. The selected compact feature subsets achieved a high diagnostic performance and thus are promising in clinical applications for discriminating lesion type and for personalized treatment planning.
Highlights
The apparent diffusion coefficient (ADC) is a highly diagnostic factor in discriminating malignant and benign breast masses in diffusion-weighted magnetic resonance imaging (DW-Magnetic resonance imaging (MRI))
Preclinical and clinical reports show that ADC reflects regional cellularity, which results in significantly lower values in malignant tumors than in benign breast lesions or normal tissue due to an increasing restriction on the extracellular matrix and a higher fraction of signal from intracellular water [7,8,9]
We focus on evaluating the potential discriminatory power by integrating dynamic contrast-enhanced (DCE)-MRI with diffusion-weighted magnetic resonance imaging (DW-MRI)
Summary
The apparent diffusion coefficient (ADC) is a highly diagnostic factor in discriminating malignant and benign breast masses in diffusion-weighted magnetic resonance imaging (DW-MRI). Magnetic resonance imaging (MRI) methods such as dynamic contrast-enhanced (DCE) and diffusion-weighted (DW) methods are among those of interest, as they provide noninvasive digital biomarker measurements of tissue properties that are highly relevant to the assessment of tumor progression and/or responses [3]. Preclinical and clinical reports show that ADC reflects regional cellularity, which results in significantly lower values in malignant tumors than in benign breast lesions or normal tissue due to an increasing restriction on the extracellular matrix and a higher fraction of signal from intracellular water [7,8,9]. False negatives and underestimation of cancer spread were observed owing to artifacts based on bleeding and tumor structure [11]
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