Differentiation Of Breast Lesions Research Articles

Diagnostic role of dynamic contrast-enhanced magnetic resonance imaging in differentiating breast lesions.

This study aimed to assess the diagnostic role of perfusion weighted image (DCE-PWI) to differentiate benign from malignant breast lesions. The study comprised 32 women who had mammography and/or breast ultrasonography findings that were clinically questionable. All patients were fasting during the magnetic resonance imaging (MRI) test to avoid nausea or dynamic contrast-enhanced vomiting from the contrast medium. In this study, we observed the form of the dynamic curve (time and signal intensity curve) type I (persistent curve) was noted in 12 lesions (37.5%): 10 lesions were benign and two lesions were malignant; type II (plateau curve) was noted in eight lesions (25%): three lesions were benign and five lesions were malignant, and type III (washout curve) noted in 12 lesions (37.5%): one lesion was benign and 11 lesions were malignant. The dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) perfusion technique plays an important role in differentiating benign and malignant tumors in breast cancer.

Cystic (including atypical) and solid breast lesion classification using the different features of quantitative ultrasound parametric images.

The amount of ultrasound (US) breast examinations continues to grow rapidly because of the wider endorsement of breast cancer screening programs. Cysts are the most commonly diagnosed breast lesions. Atypical breast cysts can be a serious differentiation problem in the US. Our goal was to develop noninvasive automated US grayscale image analysis for the cystic and solid breast lesion differentiation based on mathematical image post-processing. We used a set of 217 ultrasound images of proven 107 cystic (including 53 atypical) and 110 solid lesions. Empirical statistical and morphological models of the lesions were used to obtain features. The AUC indicator and Student's t test were used to assess the quality of the individual features. The Pearson correlation matrix was used to calculate the correlation between various features. The LASSO and stepwise regression methods were used to determine the most significant features. Finally, the lesion classification was carried out by the various methods. The use of LASSO regression for the feature selection made it possible to select the most significant features for classification. The sensitivity increased from 87.1% to 89.2% and the specificity-from 92.2 to 94.8%. After the correlation matrix construction, it was found that features with a high value of the correlation coefficient (0.72; 0.75) can also be used to improve the quality of the classification. The construction of the empirical model of the lesion pixels brightness behavior can provide parameters that are important for the correct classification of ultrasound images. The optimal set of features with the maximum discriminant characteristics may not be consistent with the correlation of features and the value of the AUC index. Features with a low AUC index (in our case 0.72) can also be important for improving the quality of the classification.

Breast DCE-MRI segmentation for lesion detection by multi-level thresholding using student psychological based optimization

In recent years, the high prevalence of breast cancer in women has risen dramatically. Therefore, segmentation of breast Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) is a necessary task to assist the radiologist inaccurate diagnosis and detection of breast cancer in breast DCE-MRI. For image segmentation, thresholding is a simple and effective method. In breast DCE-MRI analysis for lesion detection and segmentation, radiologists agree that optimization via multi-level thresholding technique is important to differentiate breast lesions from dynamic DCE-MRI. In this paper, multi-level thresholding using Student Psychology-Based Optimizer (SPBO) is proposed to segment the breast DCE-MR images for lesion detection. First, MR images are denoised using the anisotropic diffusion filter and then, Intensity Inhomogeneities (IIHs) are corrected in the preprocessing step. The preprocessed MR images are segmented using the SPBO algorithm. Finally, the lesions are extracted from the segmented images and localized in the original MR images. The proposed method is applied on 300 Sagittal T2-Weighted DCE-MRI slices of 50 patients, histologically proven, and analyzed. The proposed method is compared with algorithms such as Particle Swarm Optimizer (PSO), Dragonfly Optimization (DA), Slime Mould Optimization (SMA), Multi-Verse Optimization (MVO), Grasshopper Optimization Algorithm (GOA), Hidden Markov Random Field (HMRF), Improved Markov Random Field (IMRF), and Conventional Markov Random Field (CMRF) methods. The high accuracy level of 99.44%, sensitivity 96.84%, and Dice Similarity Coefficient (DSC) 93.41% are achieved using the proposed automatic segmentation method. Both quantitative and qualitative results demonstrate that the proposed method performs better than the eight compared methods.

Whole Volume Apparent Diffusion Coefficient (ADC) Histogram as a Quantitative Imaging Biomarker to Differentiate Breast Lesions: Correlation with the Ki-67 Proliferation Index.

Objectives To evaluate the value of the whole volume apparent diffusion coefficient (ADC) histogram in distinguishing between benign and malignant breast lesions and differentiating different molecular subtypes of breast cancers and to assess the correlation between ADC histogram parameters and Ki-67 expression in breast cancers. Methods The institutional review board approved this retrospective study. Between September 2016 and February 2019, 189 patients with 84 benign lesions and 105 breast cancers underwent magnetic resonance imaging (MRI). Volumetric ADC histograms were created by placing regions of interest (ROIs) on the whole lesion. The relationships between the ADC parameters and Ki-67 were analysed using Spearman's correlation analysis. Results Of the 189 breast lesions included, there were significant differences in patient age (P < 0.001) and lesion size (P = 0.006) between the benign and malignant lesions. The results also demonstrated significant differences in all ADC histogram parameters between benign and malignant lesions (all P < 0.001). The median and mean ADC histogram parameters performed better than the other ADC histogram parameters (AUCs were 0.943 and 0.930, respectively). The receiver operating characteristic (ROC) analysis revealed that the 10th percentile ADC value and entropy could determine the human epidermal growth factor receptor 2 (HER-2) status (both P = 0.001) and estrogen receptor (ER)/progesterone receptor (PR) status (P = 0.020 and P = 0.041, respectively). Among all breast cancer lesions, 35 tumours in the low-proliferation group (Ki − 67 < 14%) and 70 tumours in the high-proliferation group (Ki − 67 ≥ 14) were analysed with ROC curves and correlation analyses. The ROC analysis revealed that entropy and skewness could determine the Ki-67 status (P = 0.007 and P < 0.001, respectively), and there were weak correlations between ADC entropy (r = 0.383) and skewness (r = 0.209) and the Ki-67 index. Conclusion The volumetric ADC histogram could serve as an imaging marker to determine breast lesion characteristics and may be a supplemental method in predicting tumour proliferation in breast cancer.

Investigation of Synthetic Relaxometry and Diffusion Measures in the Differentiation of Benign and Malignant Breast Lesions as Compared to BI-RADS.

Breast cancer is the most common malignant tumor in women and a quantitative contrast-free method is highly desirable for its diagnosis. To investigate the performance of quantitative MRI in differentiating malignant from benign breast lesions and to compare with the Breast Imaging Reporting and Data System (BI-RADS). Retrospective. Eighty patients (56 with malignant lesions and 24 with benign lesions). Diffusion-weighted imaging (DWI) with a single-shot echo planar sequence and synthetic MRI with magnetic resonance image compilation (MAGiC) were performed at 3T. T1 relaxation time (T1 ), T2 relaxation time (T2 ), and proton density (PD) from synthetic MRI and apparent diffusion coefficient (ADC) from DWI were analyzed by two radiologists (Reader A, Reader B). Univariable and multivariable models were developed to optimize differentiation between malignant and benign lesions and their performances compared to BI-RADS. The diagnostic performance was evaluated using multivariate logistic regression analysis and area under the receiver operating characteristic (ROC) curves (AUC). T2 , PD, and ADC values for malignant lesions were significantly lower than those in benign breast lesions for both radiologists (all P < 0.05). The combined T2 , PD, and ADC model had the best performance for differentiating malignant and benign lesions with AUC, sensitivity, specificity, positive predictive value, and negative predictive values of 0.904, 94.6%, 87.5%, 94.6%, and 87.5%, respectively. The corresponding results for BI-RADS were no AUC, 94.6%, 75.0%, 89.8%, and 85.7%, respectively. The approach that combined synthetic MRI and DWI outperformed BI-RADS in the differential diagnosis of malignant and benign breast lesions and was achieved without contrast agents. This approach may serve as an alternative and effective strategy for the improvement of breast lesion differentiation. 3. 3.

Breast Elasticity Imaging Techniques: Comparison of Strain Elastography and Shear-Wave Elastography in the Same Population

Our purpose was to compare the diagnostic performances of strain elastography (SE) and shear-wave elastography (SWE) in differentiating breast lesions by combining with conventional ultrasound (US). A total of 198 patients with 203 breast lesions underwent conventional US, SE and SWE examination using MyLab 90 and Aixplorer US systems. The SE parameters were SEscore, fat-to-lesion ratio, gland-to-lesion ratio, muscle-to-lesion ratio and SEmean, and the SWE parameters were Emax, Emean, Emin and Esd. Conventional US had the best diagnostic performance, with an area under the curve (AUC) of 0.896. Among all SE parameters, the AUCs of SEscore, fat-to-lesion ratio and SEmean were 0.802, 0.810 and 0.833. For SWE parameters, they were 0.845, 0.746 and 0.845, respectively, for Emax, Emean and Esd. When combined with US, the sensitivity and AUC of SWE seemed to be better than those of SE (96.55% vs. 93.10%, 0.958 vs. 0.947), but no statistically significant difference existed between them.

Peripheral blood transcriptome identifies high-risk benign and malignant breast lesions

Peripheral blood transcriptome profiling is a potentially important tool for disease detection. We utilize this technique in a case-control study to identify candidate transcriptomic biomarkers able to differentiate women with breast lesions from normal controls. Whole blood samples were collected from 50 women with high-risk breast lesions, 57 with breast cancers and 44 controls (151 samples). Blood gene expression profiling was carried out using microarray hybridization. We identified blood gene expression signatures using AdaBoost, and constructed a predictive model differentiating breast lesions from controls. Model performance was then characterized by AUC sensitivity, specificity and accuracy. Biomarker biological processes and functions were analyzed for clues to the pathogenesis of breast lesions. Ten gene biomarkers were identified (YWHAQ, BCLAF1, WSB1, PBX2, DDIT4, LUC7L3, FKBP1A, APP, HERC2P2, FAM126B). A ten-gene panel predictive model showed discriminatory power in the test set (sensitivity: 100%, specificity: 84.2%, accuracy: 93.5%, AUC: 0.99). These biomarkers were involved in apoptosis, TGF-beta signaling, adaptive immune system regulation, gene transcription and post-transcriptional protein modification. A promising method for the detection of breast lesions is reported. This study also sheds light on breast cancer/immune system interactions, providing clues to new targets for breast cancer immune therapy.

High-Spatial-Resolution Multishot Multiplexed Sensitivity-encoding Diffusion-weighted Imaging for Improved Quality of Breast Images and Differentiation of Breast Lesions: A Feasibility Study.

Multishot multiplexed sensitivity-encoding diffusion-weighted imaging is a feasible and easily implementable routine breast MRI protocol that yields high-quality diffusion-weighted breast images.Purpose: To compare multiplexed sensitivity-encoding (MUSE) diffusion-weighted imaging (DWI) and single-shot DWI for lesion visibility and differentiation of malignant and benign lesions within the breast.Materials and Methods: In this prospective institutional review board-approved study, both MUSE DWI and single-shot DWI sequences were first optimized in breast phantoms and then performed in a group of patients. Thirty women (mean age, 51.1 years ± 10.1 [standard deviation]; age range, 27-70 years) with 37 lesions were included in this study and underwent scanning using both techniques. Visual qualitative analysis of diffusion-weighted images was accomplished by two independent readers; images were assessed for lesion visibility, adequate fat suppression, and the presence of artifacts. Quantitative analysis was performed by calculating apparent diffusion coefficient (ADC) values and image quality parameters (signal-to-noise ratio [SNR] for lesions and fibroglandular tissue; contrast-to-noise ratio) by manually drawing regions of interest within the phantoms and breast tumor tissue. Interreader variability was determined using the Cohen κ coefficient, and quantitative differences between MUSE DWI and single-shot DWI were assessed using the Mann-Whitney U test; significance was defined at P < .05.Results: MUSE DWI yielded significantly improved image quality compared with single-shot DWI in phantoms (SNR, P = .001) and participants (lesion SNR, P = .009; fibroglandular tissue SNR, P = .05; contrast-to-noise ratio, P = .008). MUSE DWI ADC values showed a significant difference between malignant and benign lesions (P < .001). No significant differences were found between MUSE DWI and single-shot DWI in the mean, maximum, and minimum ADC values (P = .96, P = .28, and P = .49, respectively). Visual qualitative analysis resulted in better lesion visibility for MUSE DWI over single-shot DWI (κ = 0.70).Conclusion: MUSE DWI is a promising high-spatial-resolution technique that may enhance breast MRI protocols without the need for contrast material administration in breast screening.Keywords: Breast, MR-Diffusion Weighted Imaging, OncologySupplemental material is available for this article.© RSNA, 2020.

Monoexponential, Biexponential, and stretched‐exponential models using diffusion‐weighted imaging: A quantitative differentiation of breast lesions at 3.0T

Monoexponential, Biexponential, and stretched‐exponential models using diffusion‐weighted imaging: A quantitative differentiation of breast lesions at 3.0T

Characterization of Breast Lesions Using a Novel Combined Approach of Ultrasonography and Elastography Prior to Invasive Procedures: Are New Age Modalities Set to Replace the Diagnostic Giant?

ObjectiveThe goal of this study was to evaluate the diagnostic yield of B-mode ultrasound and ultrasound elastography used alone and in combination for differentiating breast lesions into benign and malignant.Materials and methodsEighty-five patients were investigated with B-mode ultrasonography and elastography and provided a Breast Imaging Reporting, and Data System (BI-RADS) score based on ultrasonography, strain ratio, and elastography score (ES) based on elastography. Each lesion was then evaluated by a combination method, combining BI-RADS with strain ratio and BI-RADS with elastography score. Each modality was assessed for the successful detection and characterization of the lesion and whether combining ultrasonography B-mode imaging with strain elastography improves diagnosis and is reliable enough to replace invasive procedures such as biopsy that have been the mainstay of diagnosis.ResultsOf 85 lesions, 23 lesions (27%) were found to be malignant, and 62 lesions (72.9%) were benign. When used alone, BI-RADS had 100.0% sensitivity, 13% specificity, 50% and 100% positive and negative predictive values (respectively), and 72.9% accuracy. BI-RADS results were then combined with strain ratio (SR) and ES. BI-RADS with SR had 91.3% sensitivity, 95.2% specificity, 87.5% and 96.7% positive and negative predictive values (respectively), and 94.1% accuracy. Similarly, BI-RADS with ES had 91.3% sensitivity, 93.5% specificity, 84.0% and 96.7% positive and negative predictive values, and 92.9% accuracy.ConclusionsThe combination method performs better at diagnosing breast lesions than BI-RADS alone and can be used as an early and preliminary basis for diagnosis and in settings where invasive procedures cannot be performed. Combining strain elastography and BI-RADS also help characterize which lesions are better suited for biopsy, leading to a decline in unnecessary invasive procedures.

Preliminary results of computer-aided diagnosis for magnetic resonance imaging of solid breast lesions.

The present study aimed to determine suitable optimal classifiers and investigate the general applicability of computer-aided diagnosis (CAD) to compare magnetic resonance (MR)-CAD with MR imaging (MRI) in distinguishing benign from malignant solid breast masses. We analyzed a total of 251 patients (mean age: 44.8 ± 12.3years; range: 21-81years) with 274 breast masses (154 benign masses, 120 malignant masses) using a Gaussian mixture model and a random forest machine model for segmentation and classification. The diagnostic performance of MRI alone and MRI plus CAD were compared with respect to sensitivity, specificity, and area under the curve (AUC), using receiver operating characteristic curve analysis. The discriminating power to detect malignancy using MR-CAD with an AUC of 0.955 (sensitivity was 95.8% and the specificity was 92.9%) was significantly higher than that of MRI alone with an AUC of 0.785 (sensitivity was 71.7% and the specificity was 85.7%). CAD is feasible to differentiate breast lesions, and it can complement MRI, thereby making it easier to diagnose breast lesions and obviating the need for unnecessary biopsies.

Three-dimensional shear wave elastography for differentiation of breast lesions: An initial study with quantitative analysis using three orthogonal planes.

To prospectively evaluate the diagnostic performance of three-dimensional (3D) shear wave elastography (SWE) for breast lesions with quantitative stiffness information from transverse, sagittal and coronal planes. Conventional ultrasound (US), two-dimensional (2D)-SWE and 3D-SWE were performed for 122 consecutive patients with 122 breast lesions before biopsy or surgical excision. Maximum elasticity values of Young's modulus (Emax) were recorded on 2D-SWE and three planes of 3D-SWE. Area under the receiver operating characteristic curve (AUC), sensitivity and specificity of US, 2D-SWE and 3D-SWE were evaluated. Two combined sets (i.e., BI-RADS and 2D-SWE; BI-RADS and 3D-SWE) were compared in AUC. Observer consistency was also evaluated. On 3D-SWE, the AUC and sensitivity of sagittal plane were significantly higher than those of transverse and coronal planes (both P < 0.05). Compared with BI-RADS alone, both combined sets had significantly (P < 0.05) higher AUCs and specificities, whereas, the two combined sets showed no significant difference in AUC (P > 0.05). However, the combined set of BI-RADS and sagittal plane of 3D-SWE had significantly higher sensitivity than the combined set of BI-RADS and 2D-SWE. The sagittal plane shows the best diagnostic performance among 3D-SWE. The combination of BI-RADS and 3D-SWE is a useful tool for predicting breast malignant lesions in comparison with BI-RADS alone.

Monoexponential, Biexponential, and stretched-exponential models using diffusion-weighted imaging: A quantitative differentiation of breast lesions at 3.0T.

Diffusion-weighted imaging (DWI) plays an important role in the differentiation of malignant and benign breast lesions. To investigate the utility of various diffusion parameters obtained from monoexponential, biexponential, and stretched-exponential DWI models in the differential diagnosis of breast lesions. Prospective. Sixty-one patients (age range: 25-68 years old; mean age: 46 years old) with 31 malignant lesions, 42 benign lesions, and 28 normal breast tissues diagnosed initially by clinical palpation, ultrasonography, or conventional mammography were enrolled in the study from January to September 2016. 3.0T MR scanner, T1 WI, T2 WI, DWI (conventional and multi-b values), dynamic contrast-enhanced. The apparent diffusion coefficient (ADC) was calculated by monoexponential analysis. The diffusion coefficient (ADCslow ), pseudodiffusion coefficient (ADCfast ), and perfusion fraction (f) were calculated using the biexponential model. The distributed diffusion coefficient (DDC) and water molecular diffusion heterogeneity index (α) were obtained using a stretched-exponential model. All parameters were compared for malignant tumors, benign tumors, and normal breast tissues. A receiver operating characteristic curve was used to compare the ability of these parameters, in order to differentiate benign and malignant breast lesions. All statistical analyses were performed using statistical software (SPSS). ADC, ADCslow , f, DDC, and α values were significantly lower in malignant tumors when compared with normal breast tissues and benign tumors (P < 0.05). However, ADC and f had higher area under the receiver operating characteristic curve (AUC) values (0.889 and 0.919, respectively). The parameters derived from the biexponential and stretched-exponential DWI could provide additional information for differentiating between benign and malignant breast tumors when compared with conventional diffusion parameters. 4 Technical Efficacy: Stage 4 J. Magn. Reson. Imaging 2019;50:1461-1467.

Shear Wave Elastography as an Early Indicator of Breast Cancer in a Breastfeeding Patient: A Case Report and Literature Review

Shear wave elastography (SWE) is a relatively new and highly effective method to reveal mechanical features of tissue by demonstrating quantitative elasticity value. The morphological features including margin of the lesion, orientation, shape and border are considered in differentiation of breast lesions on USG. It is a known fact that malignant lesions are usually palpated as a hard mass in the physical examination. A qualitative broad information can obtain about the tissue elasticity by integrating SWE examination into the classical USG evaluation. We reported the finding of SWE examination in breastfeeding patient with breast malignancy in addition to magnetic resonance imaging (MRI) and ultrasonography (USG) findings.

Diagnostic performance of quantitative diffusion tensor imaging for the differentiation of breast lesions at 3 T MRI

BackgroundConventional breast magnetic resonance imaging (MRI), including dynamic contrast-enhanced MR mammography, may lead to ambiguous diagnosis and unnecessary biopsies. PurposeTo investigate the contribution of quantitative diffusion tensor imaging (DTI) in the discrimination between benign and malignant breast lesions at 3 T MRI. Material and methodsThe study included a total of 86 lesions (44 benign and 42 malignant) in 58 women (34 with malignant lesions, 23 with benign lesions and 1 with both types of lesions). All patients were examined on a 3 T MRI scanner. Fractional Anisotropy (FA), Mean Diffusivity (MD), Apparent Diffusion Coefficient (ADC), as well as eigenvalues (λ1, λ2, λ3) were calculated and compared between benign and malignant lesions using two different software packages (GE Functool and ExploreDTI). ResultsMalignant lesions exhibited significantly lower ADC values compared to benign ones (ADCmal = 1.06 × 10−3 mm2/s, ADCben = 1.54 × 10−3 mm2/s, p-value < 0.0001). FA measurements in carcinomas indicated slightly higher values than those in benign lesions (FAmal = 0.20 ± 0.07, FAben = 0.15 ± 0.05, p-value = 0.0003). Eigenvalues λ1, λ2, λ3, showed significantly lower values in malignant tumors compared to benign lesions and normal breast tissue. ROC curve analysis of ADC and DTI metrics demonstrated that ADC provides high diagnostic performance (AUC = 0.944) while, MD and λ1 showed best discriminative results (AUC = 0.906) for the differentiation of malignant and benign lesions in contrast to other DTI parameters. ConclusionThe addition of eigenvalue analysis improves DTI's ability to differentiate between benign and malignant breast lesions.

Comparison of strain and acoustic radiation force impulse elastography of breast lesions by qualitative evaluation.

To compare the diagnostic performance of conventional strain elastography (CSE) and acoustic radiation force impulse (ARFI) induced SE for qualitative assessment of breast lesions and evaluate the additional value of the two techniques combined with Breast Imaging Reporting and Data System (BI-RADS) respectively for the differentiation of benign and malignant breast lesions. In a cohort of 110 women, the conventional ultrasound (US) features and the elasticity scores of CSE and ARFI induced SE were recorded. The diagnostic performances of BI-RADS, elastography and BI-RADS plus elastography were evaluated, including the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity and accuracy. Pathologically, there were forty-eight malignant and sixty-two benign breast lesions in the final analysis. The AUCs for CSE and ARFI induced SE are similar (CSE, 0.807; ARFI induced SE, 0.846; p > 0.05), however, the specificity of the latter method was significantly higher than that of CSE (83.9% vs. 58.1%, p = 0.004) in differentiating breast lesions. The accuracy and specificity of BI-RADS plus ARFI induced SE (84.5%, 80.6%, respectively) were significantly higher than BI-RADS alone (73.6%, 54.8%, respectively) and BI-RADS plus conventional SE (72.7%, 56.5%, respectively), respectively (p < 0.05) without loss of sensitivity. Our study showed that BI-RADS plus ARFI induced SE had a better diagnostic performance in the diagnosis of breast lesions in comparison with BI-RADS alone or BI-RADS plus CSE.

F-18 fluoride uptake in primary breast cancer.

Bone-specific radiotracers are known to accumulate in breast lesions. Tc-99m diphosphonates have been widely studied in differentiating breast lesions. In this retrospective study, we aimed to assess the uptake of the bone-specific PET radiotracer, F-18 fluoride (NaF), in primary breast cancers to determine its sensitivity and to identify any differences in NaF uptake between calcified and non-calcified tumors, histological subtypes, and patients with or without axillary lymphadenopathy. NaF positron emission tomography/computed tomography (PET/CT) images of 69 newly diagnosed breast cancer patients were reviewed. F-18 fluoride uptake as maximum standardized uptake value (NaF SUVmax) was measured in the primary tumor, enlarged axillary lymph nodes and contralateral normal/non-tumoral breast tissue. Low-dose CT images were reviewed to locate the primary tumor and grossly assess its calcification and check for ipsilateral axillary lymphadenopathy. Whole body NaF PET/CT images were reviewed to search for bone metastases. Eighteen patients also underwent F-18 fluorodeoxyglucose (FDG) PET/CT study. The primary breast tumor was clearly seen as focal or diffuse uptake on NaF PET images in 27 of 69 patients (39%) (mean NaF SUVmax: 2.0 ± 1.0). In the rest, there was only mild bilateral diffuse breast uptake. When analyzing images per histological subtype (42 patients, 43 tumors), 14 of 31 invasive ductal carcinomas (IDC) (45%) and 3 of 4 ductal carcinoma in situ (DCIS) were visible on PET. Five invasive lobular carcinomas, 2 invasive mammary carcinomas, and 1 mucinous carcinoma were not visible on PET. Mean NaF SUVmax of contralateral normal/non-tumoral breast tissue was 1.0 ± 0.4. There was no significant difference in mean NaF SUVmax of primary tumor in cases with and without calcification or with and without axillary lymphadenopathy (p 0.892 and 0.957). There was no correlation between NaF SUVmax and FDG SUVmax values of the primary tumors (r 0.072, p 0.797, Pearson correlation). NaF PET has relatively low sensitivity in detecting breast cancer. However, abnormal breast uptake on NaF PET requires further evaluation. F-18 fluoride uptake in the primary breast tumor does not seem to be correlated with axillary lymphadenopathy (metastasis potential), gross tumor calcification or metabolic activity of the tumor.

Evaluation of shear wave elastography for differential diagnosis of breast lesions: A new qualitative analysis versus conventional quantitative analysis.

To evaluate a special kind of ultrasound (US) shear wave elastography for differential diagnosis of breast lesions, using a new qualitative analysis (i.e. the elasticity score in the travel time map) compared with conventional quantitative analysis. From June 2014 to July 2015, 266 pathologically proven breast lesions were enrolled in this study. The maximum, mean, median, minimum, and standard deviation of shear wave speed (SWS) values (m/s) were assessed. The elasticity score, a new qualitative feature, was evaluated in the travel time map. The area under the receiver operating characteristic (AUROC) curves were plotted to evaluate the diagnostic performance of both qualitative and quantitative analyses for differentiation of breast lesions. Among all quantitative parameters, SWS-max showed the highest AUROC (0.805; 95% CI: 0.752, 0.851) compared with SWS-mean (0.786; 95% CI:0.732, 0.834; P = 0.094), SWS-median (0.775; 95% CI:0.720, 0.824; P = 0.046), SWS-min (0.675; 95% CI:0.615, 0.731; P = 0.000), and SWS-SD (0.768; 95% CI:0.712, 0.817; P = 0.074). The AUROC of qualitative analysis in this study obtained the best diagnostic performance (0.871; 95% CI: 0.825, 0.909, compared with the best parameter of SWS-max in quantitative analysis, P = 0.011). The new qualitative analysis of shear wave travel time showed the superior diagnostic performance in the differentiation of breast lesions in comparison with conventional quantitative analysis.

Breast cancer diagnosis based on mammary thermography and extreme learning machines

Abstract Introduction Breast cancer is the most common cancer in women and one of the major causes of death from cancer among female around the world. The early detection and treatment are the major way to healing. The use of mammary thermography in Mastology is increasing as a complementary imaging technique to early detect lesions. Its use as a screening exam to identify breast disorders has been investigated. The aim of this study is to investigate the behavior of different classification methods while grouping the thermographic images into specific types of lesions. Methods To evaluate our proposal, we built classifiers based on artificial neural networks, decision trees, Bayesian classifiers, and Haralick and Zernike attributes. The image database is composed by thermographic images acquired at the University Hospital of the Federal University of Pernambuco. These images are clinically classified into the classes cyst, malignant and benign. Moments of Zernike and Haralick were used as attributes. Results Extreme Learning Machines (ELM) and Multilayer Perceptron networks (MLP) proved to be quite efficient classifiers for classification of breast lesions in thermographic images. Using 75% of the database for training, the maximum value obtained for accuracy was 73.38%, with a Kappa index of 0.6007. This result indicated to a sensitivity of 78% and specificity of 88%. The overall efficiency of the system was 83%. Conclusion ELM showed to be a promising classifier to be used in the differentiation of breast lesions in thermographic images, due to its low computational cost and robustness.

Ultrasound elastography: How can it help in differentiating breast lesions?

IntroductionElastography is considered a non-invasive imaging modality which determines the tumors according to their stiffness. Strain images representing the stiffness of the lesions compared to that of the surrounding normal tissue. PurposeTo prospectively evaluate the sensitivity and specificity of the real-time sonoelastography together with B-mode US for distinguishing benign from malignant breast lesions. MethodsThe study was conducted on 80 patients, each patient was subjected to complete history taking, thorough clinical examination. All patients had conventional US and elastography using Hitachi 7.5 MHz linear probe (Hitachi hi vision avirus ultrasound), while only in 68 patients mammography was done. ResultsAmong the 80 patients, sensitivity and specificity of the elastography test of breast lesions according to the elastography score were 80% and 80.95% respectively, while sensitivity and specificity of conventional B mode US were 80% and 76% respectively and the combined B mode US and US elastography showed higher sensitivity and specificity of 86.6% and 90.4% respectively. ConclusionElastography is a non-invasive imaging technique which is done in the same session of ultrasound in an attempt to increase and improve the accuracy of diagnostic efficiency of ultrasound.