Evaluation Of Breast Lesions Research Articles

Evaluation of breast lesions based on modified BI-RADS using high-resolution readout-segmented diffusion-weighted echo-planar imaging and T2/T1-weighted image

PurposeTo evaluate the diagnostic performance of a non-contrast magnetic resonance imaging (MRI) protocol combining high-resolution diffusion-weighted images (HR-DWI) using readout-segmented echo planar imaging, T1-weighted imaging (T1WI), and T2-weighted imaging (T2WI), using our modified Breast Imaging-Reporting and Data System (modified BI-RADS). MethodsTwo experienced radiologists, blinded to the final pathological diagnosis, categorized a total of 108 breast lesions (61 malignant and 47 benign) acquired with the above protocol using the modified BI-RADS with a diagnostic decision tree. The decision tree included subcategories of category 4, as in mammography (categories 2, 3, 4A, 4B, 4C, and 5). These results were compared with the pathological diagnoses. ResultsThe area under the ROC curve (AUC) was 0.89 (95% confidence interval [CI]: 0.83–0.95) for reader 1, and 0.89 (95% CI: 0.82–0.96) for reader 2. When categories 4C and above were classified as malignant, the sensitivity, specificity, and accuracy were 73.8%, 93.6%, and 82.4%, for reader 1; and 82.0%, 89.4%, and 85.2% for reader 2, respectively. ConclusionOur results suggest that using HR-DWI, T1WI/T2WI analyzed with a modified BI-RADS and a decision tree showed promising diagnostic performance in breast lesions, and is worthy of further study.

Assessment of Enhancement Kinetics Improves the Specificity of Abbreviated Breast MRI: Performance in an Enriched Cohort

Objective: To investigate the added value of kinetic information for breast lesion evaluation on abbreviated breast MRI (AB-MRI). Methods: This retrospective study analyzed 207 breast lesions with Breast Imaging Reporting and Data System categories 3, 4, or 5 on AB-MRI in 198 consecutive patients who had breast MRI for screening after breast cancer surgery between January 2017 and December 2019. All lesions were pathologically confirmed or stable on follow-up images for 2 years or more. Kinetic information of the lesions regarding the degree and rate of enhancement on the first post-contrast-enhanced image and the enhancement curve type from two post-contrast-enhanced images were analyzed on a commercially available computer-assisted diagnosis system. The diagnostic performances of AB-MRI with morphological analysis alone and with the addition of kinetic information were compared using the McNemar test. Results: Of 207 lesions, 59 (28.5%) were malignant and 148 (71.5%) were benign. The addition of an enhancement degree of ≥90% to the morphological analysis significantly increased the specificity of AB-MRI (29.7% vs. 52.7%, p < 0.001) without significantly reducing the sensitivity (94.9% vs. 89.8%, p = 0.083) compared to morphological analysis alone. Unnecessary biopsy could have been avoided in 34 benign lesions, although three malignant lesions could have been missed. For detecting invasive cancer, adding an enhancement degree ≥107% to the morphological analysis significantly increased the specificity (26.5% vs. 57.6%, p < 0.001) without significantly decreasing the sensitivity (94.6% vs. 86.5%, p = 0.083). Conclusion: Adding the degree of enhancement on the first post-contrast-enhanced image to the morphological analysis resulted in higher AB-MRI specificity without compromising its sensitivity.

Deep learning-based multimodal fusion network for segmentation and classification of breast cancers using B-mode and elastography ultrasound images.

Ultrasonography is one of the key medical imaging modalities for evaluating breast lesions. For differentiating benign from malignant lesions, computer-aided diagnosis (CAD) systems have greatly assisted radiologists by automatically segmenting and identifying features of lesions. Here, we present deep learning (DL)-based methods to segment the lesions and then classify benign from malignant, utilizing both B-mode and strain elastography (SE-mode) images. We propose a weighted multimodal U-Net (W-MM-U-Net) model for segmenting lesions where optimum weight is assigned on different imaging modalities using a weighted-skip connection method to emphasize its importance. We design a multimodal fusion framework (MFF) on cropped B-mode and SE-mode ultrasound (US) lesion images to classify benign and malignant lesions. The MFF consists of an integrated feature network (IFN) and a decision network (DN). Unlike other recent fusion methods, the proposed MFF method can simultaneously learn complementary information from convolutional neural networks (CNNs) trained using B-mode and SE-mode US images. The features from the CNNs are ensembled using the multimodal EmbraceNet model and DN classifies the images using those features. The experimental results (sensitivity of 100 ± 0.00% and specificity of 94.28 ± 7.00%) on the real-world clinical data showed that the proposed method outperforms the existing single- and multimodal methods. The proposed method predicts seven benign patients as benign three times out of five trials and six malignant patients as malignant five out of five trials. The proposed method would potentially enhance the classification accuracy of radiologists for breast cancer detection in US images.

Correlation of Mammography, Ultrasound and Sonoelastographic Findings With Histopathological Diagnosis in Breast Lesions.

Introduction Breast masses range from inflammatory and benign to malignant lesions, varying in different age groups and clinical presentations. Breast imaging techniques leading to prompt and specific diagnosis have been a lifesaver for millions around the globe saving them undue mental stress in inflammatory lesions and preventing early death in case of neoplasms. Here, we compare mammography, ultrasonography (US) and ultrasound elastography in the screening and diagnosis of breast lesions and the usefulness of strain ratio as a non-invasive tool in diagnosing breast malignancies. Aims and objectives Determining the characteristics ofbreast lesions on imaging by mammogram, ultrasonography, elastography, calculating strain ratio andcorrelating them with histopathology. To further estimate the advantages and limitations of one modality over the other in the evaluation of breast lesions. Methods The study was done over a duration of 18 months from November 2019 to June 2021 at JSS Medical College and Hospital, Mysuru. In this prospective study, 73 female patients with palpable breast lesions were evaluated using mammography, US and sonoelastography and were co-related with histopathological findings. Results This study has proved that the use of ultrasound elastography has higher sensitivity (91.67% with strain ratio kept at 3) in detecting malignant lesions when compared with x-ray mammography and ultrasonography having a sensitivity of 87.88% and 90.91%, respectively. Our study confirmed that there is a correlation between strain ratio and histopathological findings and that the strain ratio has high sensitivity in diagnosing and differentiating malignant breast lesions. Conclusions Mammography, US and sonoelastography when combined together are helpful in characterization and management of breast lesions. This helps to avoid unnecessary invasive interventions. The specificity of the study in detecting malignant lesions was comparable with that of histopathological analysis.

Diagnostic performance of mono-exponential DWI versus diffusion kurtosis imaging in breast lesions: A meta-analysis.

This meta-analysis aimed to explore the diagnostic value of diffusion kurtosis imaging (DKI) compared to mono-exponential diffusion weighted imaging (DWI) in the diagnosis of breast cancer. A systematic electronic literature search (up to September 2020) was conducted for published English-language studies comparing the diagnostic values of DKI and DWI for the detection of breast cancer. The data of mean kurtosis (MK), mean diffusivity (MD), and apparent diffusion coefficient (ADC) were extracted to construct 2 × 2 contingency tables. The pooled sensitivities, specificities, and areas under the receiver operating characteristic curve (AUCs) were compared between DKI and DWI in the diagnosis of breast cancer. Eight studies were finally included, with a total of 771 patients in the same population. Pooled sensitivities were 82.0% [95% confidence interval (95% CI), 78.2-85.3%] for ADC, 87.3% (95% CI, 83.9-90.1%) for MK, and 83.9% (95% CI, 80.2-87.1%) for MD. Pooled specificities were 81.1% (95% CI, 76.7-84.9%) for ADC, 85.1% (95% CI, 81.1-88.5%) for MK, and 83.2% (95% CI, 79.0-86.8%) for MD. According to the summary receiver operator characteristic curve analyses, the AUCwas 0.901 for ADC, 0.930 for MK, and 0.918 for MD (ADC vs MK, P = .353; ADC vs MD, P = .611). No notable publication bias was found, while significant heterogeneity was observed. Although DKI is feasible for identifying breast cancer, MD and MK offer similar diagnostic performance to ADC values. Thus, we recommend that DKI should not be included in the routine evaluation of breast lesions now.

Diagnostic performance of the Kaiser score in the evaluation of breast lesions on contrast-enhanced mammography

ObjectivesWe aimed to investigate whether the Kaiser score (KS) could improve the diagnostic performance of breast imaging reporting and data system (BI-RADS) in evaluating breast enhancing lesions on contrast-enhanced mammography (CEM). MethodsThree hundred fifty-nine patients with 375 lesions (231 malignant and 144 benign) were included in this retrospective study from April 2019 to December 2021.Two readers with different levels of experience in breast imaging were asked to give a BI-RADS assessment category according to the CEM BI-RADS and final score based on the KS. The diagnostic performance of all lesions, mass and non-mass enhancement (NME) were assessed by receiver operating characteristic (ROC) analysis, and the areas under the ROC curve (AUCs) were measured. The weighted kappa coefficients were calculated to investigate the interreader agreement. ResultsThe AUCs of the KS for all lesions were 0.915 (95 %CI: 0.884–0.947) and 0.876 (95 %CI: 0.838–0.914) for two readers. When mass and NME were evaluated separately, the AUCs of the KS for mass were higher than those for NME (p < 0.001). The AUCs of BI-RADS for all lesion diagnoses ranged between 0.821 (95 %CI: 0.778–0.864) and 0.842(95 %CI: 0.801–0.883) for two readers. The AUCs of the KS were higher than those of BI-RADS (p < 0.001, p = 0.016). There were no significant differences in the sensitivity between the KS (97.4 %) and BI-RADS (99.6 %) for all lesions (p = 0.130). The specificity of the KS was significantly higher than that of BI-RADS (p < 0.001). Compared with BI-RADS, the application of the KS could have potentially obviated 41.7 % to 47.9 % unnecessary biopsies in 144 benign lesions. Interreader agreement between the two readers of the KS was almost perfect (k = 0.883 [95 % CI: 0.842–0.924]). ConclusionThe use of the KS provided a high diagnostic performance in distinguishing malignant and benign breast lesions on CEM and outperformed BI-RADS. The application of the KS can downgrade up to 47.9% of unnecessary biopsies of benign breast lesions.

Ultrasonic breast tumor extraction based on adversarial mechanism and active contour

Background and objectiveBreast cancer is a high incidence of gynecological diseases; breast ultrasound screening can effectively reduce the mortality rate of breast cancer. In breast ultrasound images, the localization and segmentation of tumor lesions are important steps for the extraction of lesions, which helps clinicians evaluate breast lesions quantitatively and makes better clinical diagnosis of the disease. However, the segmentation of breast lesions is difficult due to the blurred and uneven edges of some lesions. In this paper, we propose a segmentation framework combining active contour module and deep learning adversarial mechanism and apply it for the segmentation of breast tumor lesions. MethodWe use a conditional adversarial network as the main framework. The generator is a segmentation network consisting of a Deformed U-Net and an active contour module. Here, the Deformed U-Net performs pixel-level segmentation for breast ultrasound images. The active contour module refines the tumor lesion edges, and the refined result provides loss information for Deformed U-Net. Therefore, the Deformed U-Net can better classify the edge pixels. The discriminator is the Markov discriminator; this discriminator provides loss feedback for the segmentation network. We cross-train the discriminator and segmentation network to implement Adversarial Mechanism for getting a more optimized segmentation network. ResultsThe segmentation performance of the segmentation network for breast ultrasound images is improved by adding a Markov discriminator to provide discriminant loss training. The proposed method for segmenting the tumor lesions in breast ultrasound image obtains dice coefficient: 89.7%, accuracy: 98.1%, precision: 86.3%, mean-intersection-over-union: 82.2%, recall: 94.7%, specificity: 98.5% and F1score: 89.7%. ConclusionComparing with traditional methods, the proposed method gives better performance. The experimental results show that the proposed method can effectively segment the lesions in breast ultrasound images, and then assist doctors to realize the diagnosis of breast lesions.

Evaluation of Breast Lesions Using Mammography

The second leading cause of death is breast cancer and annual mammograms have been found to incite the early identification of breast cancer.The diagnosis of cancer at initial stage is helpful in lowering the mortality rate. The most prevalent pathologies of breast cancer are swelling, cysts, and adenosis, and benign or malignant tumors. Objective: To evaluate Breast Lesions Using Mammography.Method: It was a cross-sectional study comprised of a sample size of 47 female patients, calculated via a convenient sampling approach. Women with age of 29 years and above were included in this study. The study was performed in Faisalabad, Pakistan from December 2021 to March 2022. The digital mammographic equipment was used in this study and data was entered and analyzed by SPSS version 20. Results: The majority 33(70.2%) patients were included in 30-49 years age group while least were 2(4.3%) in 70 and above. The ill-defined shape tumors were 18 (38.3%) and the the microlobulated shaped tumors were 1(2.1%). The tumors with well-defined margin were 17(36.2%) and tumors with ill-defined margin were 1(2.1%). The tumors without calcification were 31(66%) and the calcified tumors were 16(34%). The tumors with involvement of lymph nodes were 30(63.8) and tumors with single lymph node involved were 1( 2.1%) Conclusion: A mammogram is necessary for determining the stage of breast cancer. A mammogram can be very useful to determine the location and shape of the lesion for demonstrating a lesion's radiographic features.

“DIFFERENTIATING BENIGN FROM MALIGNANT BREAST LESIONS- CLINICAL, HISTOPATHOLOGICAL AND IMAGING CORRELATION”

Introduction: Breast cancer is the second most common cancer in Indian women. Breast cancer is a signicant cause of worldwide morbidity and mortality. Mammography and ultrasound are used as the rst line of investigation for the early detection and localization of breast tumors. Mammography has high sensitivity in case of patients with fatty parenchyma however low sensitivity in case of patients with dense breasts, implants, and post-surgical scar, thus breast MRI with higher sensitivity and specicity due to its ability to depict excellent soft-tissue contrast has become increasingly important in the detection of breast cancer. DCE-MRI has improved specicity in characterizing breast lesions by analysis of time-intensity curves. Diffusion-weighted imaging can improve the sensitivity and specicity of MRI in the evaluation of breast lesions by calculating the ADC values a quantitative measure that is a useful tool for tumor detection and differentiating between benign and malignant breast lesions. To evaluate the role of diffusion-weighted MRI andObjective: dynamic contrast-enhanced MRI in differentiating benign from malignant breast lesions and to compare the ndings of diffusion-weighted MRI and dynamic enhanced MRI with histopathological or FNAC ndings. A totalMaterial And Methods: of 30 patients with palpable breast lumps with either positive or negative ndings on mammography and ultrasound were included in this study. All patients included in this study rst underwent lm-screen mammography. Ultrasonography was done with convex and linear probes. This was followed by MRI. T1W axial, T2W axial, SPAIR/ Fat Saturated T2 weighted axial images were acquired in appropriate imaging planes. Diffusion-weighted images were obtained using b values of 0 and 1000 and ADC values were calculated. Dynamic contrast-enhanced MR was performed using fat-suppressed 3D T1 weighted images after injection of gadolinium and time-intensity curves were generated. A single precontrast scan was followed by 4 post- contrast scans which were obtained for a total duration of 4 min 24 seconds. Findings of the MRI (Diffusion-weighted and dynamic contrast enhancement) were analyzed and correlated with histopathological and FNAC ndings to evaluate their use as a diagnostic modality. A total of 30 female patients presenting with palpable breast lumps were included in theResults: study. Out of 30 patients, 20 cases were malignant (66.67%) and 10 cases were benign (33.3%). Fibroadenoma accounted for a majority of benign lesions (4 out of 10 benign lesions) while IDC accounted for the majority of malignant lesions (15 out of 20 malignant lesions). Dynamic CE-MR is a reliable tool for differentiating between benign and malignant lesions based on kinetic curves. 7 out of 10 benign lesions showed a type I curve while the rest showed a type II curve while the majority (15/20) of malignant lesions showed a type III curve. 8 out of 10 benign breast lesions did not show restricted diffusion on DWI while all malignant lesions showed restricted diffusion on DWI. In our study, the mean ADC value for benign lesions was 1.59 x 10 mm/s while the mean ADC value for malignant lesions was 0.88 x 10 mm/s. Using the ROC curve, the cut-off value of ADC was calculated to be 1.19 x 10 mm/s which gives sensitivity and specicity of 95% and 90% respectively. The individual sensitivity for DCE-MRI and DWI was calculated to be 95% and 95% while the individual specicity for DCE-MRI and DWI was calculated to be 70% and 90% respectively. After a combined analysis of DCE-MRI and DWI using a positive result from any of the two techniques as malignancy, the sensitivity and specicity were 95% and 80% respectively.

MENAGERIE OF BREAST LESIONS ON TRUCUT BIOPSIES AND THEIR CORRELATION WITH BIRADS SCORE-3 YEARS INSTITUTIONAL STUDY

Breast ultrasound is a primary diagnostic modality for evaluating suspected breast lesions. Histopathological examination of trucut biopsies helps in accurate typing and grading the tumor. In the present study we compared histopathological ndings of trucut breast biopsies with BIRADS score. It was a retrospective, 3 years study with 183 cases that had under gone ultrasound examination and trucut biopsy for histopathological examination. Among 183 cases, 8 were inconclusive, 93 were benign and 82 were malignant. In 183 cases, 62 cases were classied as BIRADS-II, 24 cases as BIRADS-III, 66 cases as BIRADS IV and 31 cases as BIRADS V. Sensitivity calculated was 98.78%, specicity was 86.02%, positive predictive value was 86.17% and negative predictive value was 98.77%. The disease prevalence was 46.86% and accuracy was 92%. P value was statistically signicant. Therefore, we conclude that BIRADS score is non-invasive and a useful test for evaluating breast lesions preoperatively. However, we cannot rely on BIRADS score in cases of sampling error, nature of the lesion or unusual radiological presentation that may lead to interpretation error and thus histopathology will be considered as gold standard in diagnosis of breast lumps.

A Concise Review on the Utilization of Abbreviated Protocol Breast MRI over Full Diagnostic Protocol in Breast Cancer Detection.

Breast MRI possesses high sensitivity for detecting breast cancer among the current clinical modalities and is an indispensable imaging practice. Breast MRI comprises diffusion-weighted imaging, ultrafast, and T2 weighted and T1 weighted CE (contrast-enhanced) imaging that may be utilized for improving the characterization of the lesions. This multimodal evaluation of breast lesions enables outstanding discrimination between the malignant and benign and malignant lesions. The expanding indications of breast MRI confirm the far superiority of MRI in preoperative staging, especially in the estimation of tumour size and identifying tumour foci in the contralateral and ipsilateral breast. Recent studies depicted that experts can meritoriously utilize this tool for improving breast cancer surgery despite their existence of no significant long term outcomes. For managing the, directly and indirectly, associated screening cost, abbreviated protocols are found to be more beneficial. Further, in some of the patients who were treated with neoadjuvant chemotherapy, breast MRI is utilized for documenting response. It is therefore essential to realise that oncological screening must be easily available, cost-effective, and time-consuming. Earlier detection of this short sequence protocol leads to prior and early breast cancer disease in high risky female populations like women with dense breasts, prehistoric evidence, etc. This proper utilization of AP reduces unnecessary mastectomies. Hence, this review focused on the explorative information for strongly suggesting the benefits of AP breast MRI compared to full diagnostic protocol MRI.

Can DWI provide additional value to Kaiser score in evaluation of breast lesions.

To explore added value of diffusion-weighted imaging (DWI) as an adjunct to Kaiser score (KS) for differentiation of benign from malignant lesions on breast magnetic resonance imaging (MRI). Two hundred forty-six patients with 273 lesions (155 malignancies) were included in this retrospective study from January 2015 to December 2019. All lesions were proved by pathology. Two radiologists blind to pathological results evaluated lesions according to KS. Lesions with score > 4 were considered malignant. Four thresholds of ADC values -1.3 × 10-3mm2/s, 1.4 × 10-3mm2/s, 1.53 × 10-3mm2/s, and 1.6 × 10-3mm2/s were used to distinguish benign from malignant lesions. For combined diagnosis, a lesion with KS > 4 and ADC values below the preset cutoffs was considered as malignant; otherwise, it was benign. Sensitivity, specificity, and area under the curve (AUC) were compared between KS, DWI, and combined diagnosis. The AUC of KS was significantly higher than that of DWI alone (0.941 vs 0.901, p = 0.04). The sensitivity of KS (96.8%) and DWI (97.4 - 99.4%) was comparable (p > 0.05) while the specificity of KS (83.9%) was significantly higher than that of DWI (19.5-56.8%) (p < 0.05). Adding DWI as an adjunct to KS resulted in a 0-2.5% increase of specificity and a 0.1-1.3% decrease of sensitivity; however, the difference did not reach statistical significance (p > 0.05). KS showed higher diagnostic performance than DWI alone for discrimination of breast benign and malignant lesions. DWI showed no additional value to KS for characterizing breast lesions. • KS showed higher diagnostic performance than DWI alone for differentiation of benign from breast malignant lesions. • DWI alone showed a high sensitivity but a low specificity for characterizing breast lesions. • Diagnostic performance did not improve using DWI as an adjunct to KS.

Comparison of Linear and Convex-Array Transducers in Assessing the Enhancement Characteristics of Suspicious Breast Lesions at Contrast-Enhanced Ultrasound (CEUS).

The purpose of this study was to determine the observer agreement in assessing the enhancement pattern of suspicious breast lesions with contrast-enhanced ultrasound (CEUS) using high and low frequency transducers. Methods: This prospective study included 70 patients with suspicious breast lesions detected at mammography and/or ultrasound and classified according to the American College of Radiology (ACR) Breast Imaging-Reporting and Data System (BI-RADS) in 4A, 4B, 4C, or 5, who underwent CEUS examinations between October 2020 and August 2021. Results: Participants’ ages ranged from 28 to 83 years (48.5 + 6.36, mean age + SD). We obtained a substantial agreement for the first reader (kappa = 0.614, p < 0.001) and a perfect agreement for the second and third reader (kappa = 1, p < 0.001) between the two transducers for the uptake pattern. A moderate agreement for the second and third reader (kappa = 0.517 and 0.538, respectively, p < 0.001) and only a fair agreement (kappa = 0.320, p < 0.001) in the case of the first reader for the perilesional enhancement was observed. We obtained an excellent inter-observer agreement (Cronbach’s Alpha coefficient = 0.960, p < 0.001) for the degree of enhancement, a good inter-observer agreement for the uptake pattern and perilesional enhancement (Cronbach’s Alpha coefficient = 0.831 and 0.853, respectively, p < 0.001), and a good and acceptable inter-observer agreement for internal homogeneity, perfusion defects and margins of the lesions (Cronbach’s Alpha coefficient = 0.703, 0.703 and 0.792, respectively, p < 0.001) concerning the evaluation of breast lesions with the linear-array transducer. Conclusions: The evaluation of suspicious breast lesions by three experts with high-frequency linear-array transducer and low-frequency convex-array transducer was comparable in terms of uptake pattern and perilesional enhancement. The agreement regarding the evaluation of the degree of enhancement, the internal homogeneity, and the perfusion defects varied between fair and substantial. For all CEUS characteristics, the inter-observer agreement was superior for linear-array transducer, which leads to more a homogeneous and reproducible interpretation.

Differentiation of Benign and Malignant Breast Lesions Using ADC Values and ADC Ratio in Breast MRI.

Magnetic resonance imaging (MRI) of the breast has been increasingly used for the detailed evaluation of breast lesions. Diffusion-weighted imaging (DWI) gives additional information for the lesions based on tissue cellularity. The aim of our study was to evaluate the possibilities of DWI, apparent diffusion coefficient (ADC) value and ADC ratio (the ratio between the ADC of the lesion and the ADC of normal glandular tissue) to differentiate benign from malignant breast lesions. Materials and methods: Eighty-seven patients with solid breast lesions (52 malignant and 35 benign) were examined on a 1.5 T MR scanner before histopathological evaluation. ADC values and ADC ratios were calculated. Results: The ADC values in the group with malignant tumors were significantly lower (mean 0.88 ± 0.15 × 10−3 mm2/s) in comparison with the group with benign lesions (mean 1.52 ± 0.23 × 10−3 mm2/s). A significantly lower ADC ratio was observed in the patients with malignant tumors (mean 0.66 ± 0.13) versus the patients with benign lesions (mean 1.12 ± 0.23). The cut-off point of the ADC value for differentiating malignant from benign breast tumors was 1.11 × 10−3 mm2/s with a sensitivity of 94.23%, specificity of 94.29%, and diagnostic accuracy of 98%, and an ADC ratio of ≤0.87 with a sensitivity of 94.23%, specificity of 91.43%, and a diagnostic accuracy of 95%. Conclusion: According to the results from our study DWI, ADC values and ADC ratio proved to be valuable additional techniques with high sensitivity and specificity for distinguishing benign from malignant breast lesions.

Magnetic resonance imaging and shear wave elastography comparison in the evaluation of breast lesions

This study aims to compare the Magnetic Resonance Imaging (MRI) and Shear Wave Elastography (SWE) data and investigate their contribution to diagnosis when both are individually used and combined. This study included the consent of 46 diagnostic biopsy patients after their breast MRI examination was performed. Mass appearance patients in the sonographic evaluation were SWE examined before the biopsy. In MR examination, lesion localization, size, contrast curves, contrast enhancement features, intensity in the fat-suppressed T2A sequence, Emean value in SWE examination, and histopathological results were examined. Individual and combined use of MRI and SWE findings were evaluated with histopathological results. The diagnosis consistency was compared according to the histopathological results of the malignant-benign defined lesions. Higher sensitivity in MRI; higher specificity and accuracy in SWE were acquired when both methods were compared. The accuracy of MRI improved when MRI use is combined with SWE. The combined use of SWE with MRI increases the diagnostic accuracy in breast lesion characterization. We observed that lesions showing a type 3 enhancement curve and iso-hypointense on the T2W sequence in breast MRI could be predicted to show a stiff elasticity pattern on SWE due to higher elastography values.

Utility of MRI in diagnosis of molecular subtypes of breast cancer

Background: Breast cancer is a collection of diseases defined by distinct pathological (e.g., ductal, lobular, mucinous) and molecular characteristics (e.g., ER and PR, HER2 amplification, and more recently transcriptome-based classifications such as luminal and basal cancers). Molecular subtyping is beneficial for the diagnosis and individualized treatment of breast cancer.MRI is a supplemental technique to mammography and ultrasonography for the evaluation of breast lesions and to predict molecular subtypes of breast cancer. Studies have highlighted the value of DCE-MRI in reflecting the anatomic and functional properties of tumors and facilitating treatment. Objectives: The aim of this work was to assess the utility of MRI as an accurate method for detection of molecular subtypes of breast cancer. Patients and Methods: a retrospective clinical study of 2-years enrollment duration. The study was conducted at Radiology Department, Assuit University Hospitals on 50 lesions, patient age ranging from 23 to 66 years old with mean age was (46.9 ± SD) years. MR imaging studies were performed using a 1.5 T Magnetom Vision scanner with dedicated bilateral phased-array breast coil (Siemens, Erlangen, Germany). MRI sequences were 1. Axial T1WI. 2. Axial T2WI. 3. Axial STIR. 4. Axial DWI and ADC. 5. DCE-MRI. Results: The study included 50 lesions; 21 lesions were Luminal A, 15 lesions were Luminal B, 10 lesions were HER2+, and 4 lesions were TN. Histopathology 39 lesions were IDC-NOS, 1 lesion was IDC (medullary type), 7 lesions were mixed pathology IDC + DCIS, and 3 lesions were ILC. As regard the correlation between the histopathological type and grade, both were specific in differentiation between the molecular subtypes. According to the MRI findings it was found that T2 Intratumoral signal intensity, STIR, and the margin of the lesion were highly specific in differentiation, both L.N status and number were found that they were highly specific in differentiation (p <0.001), while size of the lesion, T1WI signal, ADC values were found that they were non-specific in differentiation between the molecular subtypes of breast cancer. Conclusion: Breast MRI may help in assessing different molecular subtypes of breast cancer.

Reproducibility of the "International Academy of Cytology Yokohama System for Reporting Breast Cytology" - A Retrospective Analysis of 70 Cases.

Fine-needle aspiration cytology (FNAC) is the most practiced initial method for evaluation of breast lesions. The International Academy of Cytology Yokohama System for Reporting Breast (IAC YSRB) Fine-Needle Aspiration Biopsy Cytopathology has been developed to standardize the reporting system. However, literature available on the inter-observer reproducibility of 5 IAC YSRB categories is limited. We investigated the inter-observer reproducibility of the IAC YSRB system. A total of 70 consecutive specimens obtained from FNAC of breast lesions were reviewed retrospectively by 3 experienced cytopathologists who allotted 1 to 5 IAC YSRB categories. The percent overall agreement between observers was 70.48% and the free marginal kappa was 0.63, which signifies substantial agreement. After combining "Suspicious" and "Malignant" categories, overall agreement was 80.95% and free marginal kappa became 0.75. Inter-observer agreement of three (70.48%) cytopathologists was substantial. Agreement can be improved by combining certain categories, especially "Suspicious" and "Malignant". Technical quality limitation plays a significant role in a proportion of cases, mainly the "Atypical" and "Suspicious of malignancy" categories. Application of the IAC YSRB system in day-to-day practice will increase the inter-observer agreement.

The role of the IAC Yokohama System for Reporting Breast Fine Needle Aspiration Biopsy and the ACR Breast Imaging-Reporting and Data System in the evaluation of breast lesions.

Stratification of breast lesions for appropriate management is achieved through an integration of clinical examination, imaging, and fine needle aspiration biopsy (FNAB). The current study aimed to evaluate the combined effectiveness of the widely used Breast Imaging-Reporting and Data System (BI-RADS) with the recently proposed International Academy of Cytology (IAC) Yokohama System for Reporting Breast Fine Needle Aspiration Biopsy Cytopathology. A retrospective analysis was done on all breast FNABs from 2016 through 2020. The cases were categorised according to the IAC Yokohama System. Histopathological correlation of the BI-RADS and IAC Yokohama System was performed. The rate of malignancy (ROM) for each category of the BI-RADS and IAC Yokohama System was calculated. The ROM values for categories I to V were 38%, 0.6%, 21.9%, 100%, and 97%, respectively. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of FNAB with category III assumed as malignant were 98.9%, 85%, 76.1%, 99.3%, and 89.5%, respectively. With category III assumed as benign, these indices were 90.8%, 98.9%, 97.5%, 95.7%, and 96.2%, respectively. The sensitivity, specificity, PPV, NPV and accuracy of BI-RADS were 91.5%, 81.9%, 72%, 95%, and 85.1%, respectively. FNAB is still an indispensable test in the evaluation of breast lesions. The utilisation of the IAC Yokohama reporting system for breast cytology in conjunction with ACR BI-RADS aids in better stratification of lesions.

RADIOLOGICAL TESTS VERSUS PATHOLOGICAL DIAGNOSTICS: COMPLIMENTARY OR ANTAGONISTIC RELATIONSHIP- AN INSTITUTIONAL EXPERIENCE

Aims &amp; Objective: To determine the yield, reliability and diagnostic accuracy of Breast Imaging Reporting and Data System in evaluation of breast lesions taking histopathology as gold standard. Material and Methods: This cross-sectional, analytical study was conducted at Pathology dept. Of SMSMC Jaipur from June 2019 to June 2020. The data of the core needle biopsies of breast lesions received at the Pathology department which had been also categorized on mammogram, according to the Breast Imaging Reporting and Data System (BI-RADS), were selected for the study. All clinical parameters, the site of the biopsy, the radiological findings, the pathological diagnoses were studied. The concordance between the radiological and pathological results was studied . According to BI-RADS, categories II and III were classified as benign and BI-RADs IV and V as malignant. The breast core biopsies were classified as benign and malignant, according to the diagnosis. Results: A total of 115 patients were included in the study. BI-RADS system for reporting whencompared with histopathology had sensitivity of 93.55%, specificity 68.18%, positive predictive value92.55%, negative predictive value 71.43% and diagnostic accuracy 88.70%. Conclusion: The findings of this study report a high diagnostic accuracy of BI-RADS in the diagnosis ofbreast carcinoma.

Role of conventional magnetic resonance imaging and diffusion-weighted imaging in evaluation of breast lesions

Background and Aim This study aimed to evaluate the role of diffusion-weighted imaging (DWI) and apparent diffusion coefficient in diagnosis of the breast lesions. Patients and methods This study included 100 patients referred to the Radiology Department of Al-Azhar Assiut University Hospital from February 2018 to May 2020 for DWI MRI examination with a provisional diagnosis of breast lesions. All patients were recruited after meeting the inclusion criteria. They underwent clinical examination, mammography, ultrasonography, and contrast-enhanced MRI with DWI. Results Our study included 100 cases. The combined MRI protocol of dynamic contrast-enhanced (DCE)-MRI and DWI was true positive in 60 and true negative in 30 patients. The combined MRI protocol of DCE-MRI and DWI provided a sensitivity of 92%, a specificity of 90%, a positive predictive value of 95.8%, a negative predictive value of 81.8% and accuracy of 91.43%. In this study, the addition of DWI to standard DCE-MRI provided a 10% increase in the specificity of breast MRI, with a 4% decrease in the sensitivity. Conclusion DWI is a short unenhanced scan and had the highest specificity compared with other imaging modalities, as it reduced the false-positive results. It should be added to conventional breast MRI to increase the sensitivity and specificity of MRI.