The role of histogram analysis in diffusion-weighted imaging in the differential diagnosis of benign and malignant breast lesions

The incidence of breast cancer is continuously increasing and tends to occur more in younger patients [1]. In China, the number of new cases of and deaths from breast cancer account for 12.2 and 9.6%, respectively, of cases in the world, and the incidence of breast cancer has increased twice as fast as that in the rest of the world. Furthermore, the average age of patients diagnosed with breast cancer in China is in the 45–55 age range, which is younger than that of Western women [2]. Therefore, the early detection and diagnosis of breast cancer are critical. Although punch biopsy remains the gold standard [3] for the diagnosis of breast cancer, some studies have shown that only 53.1% of patients are diagnosed with breast cancer by punch biopsy, indicating that there are some defects and missed diagnoses in the diagnosis of breast cancer [4]. Therefore, the significance of imaging in the diagnosis of breast cancer is growing. Heterogeneity is an important feature of malignant breast tumors that greatly affects the choice of treatment methods and the efficacy of chemotherapy drugs [5]. Due to its high soft-tissue resolution, magnetic resonance imaging (MRI) has increasingly been used in the screening and diagnosis of breast lesions [6]. Some studies have shown that the MRI results for breast cancer are consistent with pathological sections. Therefore, its sensitivity is very high. In addition, since MRI is free of radiation and can be performed repeatedly, it has become one of the safest and most effective diagnostic modalities for breast cancer [7]. However, there is some overlap in the examination of benign and malignant breast lesions by MRI. For example, breast cancer shows a high signal intensity on diffusion-weighted imaging (DWI), and some benign breast lesions also show a high signal intensity, which limits the clinical application of MRI [7].

In recent years, DWI, as a functional imaging method that can reflect tissue microenvironments, has been increasingly used in clinical diagnosis [8]. However, DWI based on intravoxel incoherent motion (IVIM) can more accurately describe the diffusion motion characteristics of water molecules in the tissue microenvironment, thereby more accurately reflecting the heterogeneity of breast lesions [8]. Gray-scale histogram analysis can quantify the signal intensity distribution in the lesions to more objectively reflect the tissue heterogeneity in the lesion [9]. Gray-scale histogram analysis is an intuitionistic texture analysis based on statistics. The commonly used first-order statistical parameters include the average, maximum, minimum, variance, skewness, and kurtosis of the signal strength in the pixel [9]. This can more objectively analyze the image information, and provide a more reliable basis for the identification, classification, efficacy, and prognosis evaluation of benign and malignant tumors. Using the gray-scale histogram of DWI may enhance the sensitivity of the MRI, thereby improving the diagnostic efficiency of MRI for breast cancer [9].

Based on the above assumptions, the present study retrospectively analyzed the MRI images and clinical data of 55 patients (63 lesions). The difference in comparative data in benign and malignant breast lesions was analyzed to investigate the role of the histogram analysis of DWI in the differential diagnosis of benign and malignant breast lesions.

Diffusion-weighted imaging (DWI) can noninvasively detect the brownian motion of water molecules inside and outside cells, which is affected by a variety of factors, including the density of cells, the structure of cell membranes, and macromolecular substances inside and outside cells. In addition, DWI can quantitatively reflect the microenvironment in tissues by measuring the distance that water molecules are allowed to move in a certain period of time. The quantitative index can be expressed through the ADC value, but the ADC value can be easily affected by the perfusion of microcirculatory blood flow in the tissue.

In previous studies, T1WI, T2WI, contrast enhanced T1WI sequence and ADC map have been mostly used for texture analysis. The present study revealed that f (minimum, mean, kurtosis, the 10th percentile and 50th percentile) and ADC-slow (mean and the 50th percentile) in malignant breast lesions were significantly lower than in the benign lesions, while f (variance, skewness) and ADC-fast (kurtosis) in malignant breast lesions were significantly higher than in the benign lesions, indicating that the above parameters can be used for the differential diagnosis of benign and malignant breast lesions due to the different manifestations in benign and malignant breast lesions.

The average gray value of gray histogram reflects the data concentration trend and average level. The higher the gray value means more bright areas in the region of interest. ADC slow represents the true diffusion degree of water molecules in tissues, which can reflect the heterogeneity of tissues. In the present study, the mean ADC-slow and 50th percentile of malignant breast lesions were significantly lower than those of benign breast lesions. The causes were as follows: the cell density of malignant lesions was high and the ratio of the intracellular nucleus to the cytoplasm increased, which led to a decrease in intracellular and extracellular spaces. Furthermore, the heterogeneity of tissues was higher, the limited diffusion of water molecules was more significant [10], and the gray value was lower.

Previous studies have revealed [11] that ADC-fast has limited applications in disease diagnosis due to its poor stability and reproducibility, but it was found that the kurtosis of ADC-fast was significantly different in the benign and malignant lesions in this study. Kurtosis could reflect the peak value of the gray-scale histogram by calculating the distribution of pixels in issues. Furthermore, it is the position of the peak height, which indicates the size of the ADC-fast value under the maximum frequency [12]. In the present study, the kurtosis of ADC-fast in malignant lesions was significantly higher than that in benign lesions, which indicates that the maximum frequency of ADC-fast value in malignant lesions is significantly higher than that in benign lesions. This reflected that the microvascular perfusion area in malignant lesions is significantly higher than that in benign lesions.

In the present study, the histogram parameters of the perfusion fraction f which represented the proportion of the fast diffusion component, include the minimum, mean, variance, skewness, kurtosis, the 10th percentile and the 50th percentile were significantly different between benign and malignant breast lesions. The perfusion fraction f of the tissue represents the proportion of the stroma at different vascular levels to the diseased tissue, which is correlated to the microstructure of the tissue. Tamura et al. [13] considered that the f value of non-invasive breast cancer is significantly higher than that of invasive breast cancer. The present study revealed that the minimum value, mean, kurtosis, the 10th percentile and the 50th percentile of malignant lesions are significantly lower than those of benign breast lesions. By analyzing these reasons, on one hand, the increased neovascularization and solid components of malignant lesions may lead to the compression of microvessels, and the decrease of the signal attenuation ratio is caused by microperfusion; on the other hand, the necrotic cysts of malignant lesions increases, while the rapid diffusion components of the necrotic cysts are almost zero. In the present study, the possible necrotic cystic area was not avoided when selected the ROI. To some extent, it reduceed the f value of malignant lesions.

The variance mainly describes the mean degree of dispersion of the lesion’s characteristic value. The larger the variance was, the more data representing the deviation of the mean value in the lesion, and the higher non-uniformity of the lesion was [14]. The degree of skewness can reflect the asymmetry of the gray-scale signal distribution of the pixel in the particular ROI. The higher the degree is, the higher the complexity of the texture feature in the ROI would be. This indicates an increase in heterogeneity within the lesion [15]. The variance, skewness of the f value of breast malignant lesions in the present study were significantly higher than those of benign lesions, which indicated that the heterogeneity of malignant lesions is significantly higher than that of benign lesions, and was consistent with the pathological characteristics of the lesions.

The present study has some limitations. First, the histology of benign lesions included in the present study was quite different, which reduced the specificity of the differential diagnosis, to a certain extent. As the next step, the investigator will increase the sample size and further study the differences between benign and malignant lesions. Second, the delineation of the ROI is only an analysis of the two-dimensional characteristics of the largest dimension of the lesion, and not an analysis of the texture features of the whole volume of the tumor. Furthermore, the IVIM parameters had many influencing factors. Bokacheva et al. [10] considered that the interval and size of the b value can affect the IVIM parameters, especially on perfusion-related parameters. Therefore, the reasonable selection and optimization of the b value also needs further analysis and research. In addition, proton density, echo time and T2 can also affect these IVIM parameters. These factors would also be controlled as variables in future research, thereby determiming the appropriate diagnostic conditions.

The histogram analysis of ADC-slow (mean and the 50th percentile), ADC-fast (kurtosis) and f (minimum, mean, kurtosis, the 10th percentile and 50th percentile. Variance, skewness) can provide a more objective and accurate basis for the differential diagnosis of benign and malignant breast lesions.