Are Irregular Hypoechoic Breast Masses on Ultrasound Always Malignancies?: A Pictorial Essay

Diagnostic Performance of Grayscale Ultrasonography in the Evaluation of Mass-forming Breast Lesions

In our study, we evaluated the diagnostic performance of grayscale ultrasonography (USG) in risk stratification of mass-forming breast lesions. Our study included 90 cases, in which 88 were females and 2 cases were male with age ranging from 16 to 73 years. Out of 90 lesions, 51 (58%) lesions were benign and 39 (39%) lesions were malignant. High-resolution USG done in all 90 lesions revealed sensitivity (90.2%), specificity (74.36%), positive predictive value (PPV) (82.14%), negative predictive value (NPV) (85.29%), and accuracy (83.33%). Calculated weighted kappa value 0.665, indicating better level of agreement in predicting malignant lesions compared to gold standard. Our study revealed that USG is sensitive and specific test in detecting malignant lesions with high PPV and NPV; the calculated weighted kappa value was 0.665, indicating better level of agreement in predicting malignant lesions compared to gold standard.

Approach to Nonmass Lesions on Breast Ultrasound

Nonmass lesions in breast ultrasound (US) are areas of altered echogenicity without definite margins or mass effect. However, these lesions may show calcifications, associated architectural distortion, or shadowing just like masses. They vary in their echogenicity, distribution, ductal or nonductal appearance and the associated features that can be seen in variety of benign and malignant pathologies. With no uniform definition or classification system, there is no standardized approach in further risk categorization and management strategies of these lesions. Malignant nonmass lesions are not uncommon and few sonographic features can help in differentiating benign and malignant pathologies. US-guided tissue sampling or lesion localization can be preferred in the nonmass lesions identified on second look US after magnetic resonance imaging or mammography. This article aims to describe various imaging patterns and attempts to provide an algorithmic approach to nonmass findings on breast US.

Correlation Analysis of Digital Mammography, Ultrasonography, and Pathologic Features in Pure Invasive Micropapillary Carcinoma of the Breast (PIMPC)

Purpose

This study determined the digital mammography and ultrasonography imaging features of pure invasive micropapillary carcinoma of the breast (PIMPC) and the correlation with pathologic features.

Patients Methods

Nineteen patients diagnosed with PIMPC at Yantaishan Hospital from October 2015 to February 2022 were included in the study group. Forty patients with breast masses diagnosed as nonspecific invasive ductal carcinoma of the breast (NIDC) from July to December 2021 were included in the control group. Digital mammography and ultrasonography features were compared between the two groups.

Results

Patients with PIMPC had a younger age profile compared to patients with NIDC (P=0.017). Moreover, PIMPC masses were smaller than NIDC masses (P=0.040). Imaging features analysis revealed significant differences in age groups (<45 years: χ²=5.971, P=0.044) and the presence of spiculations or the crab claw sign (χ²=8.583, P=0.004) between patients with PIMPC and NIDC. However, there were no statistically significant differences in the presence of calcifications, blood flow grading, pathologic molecular subtypes between the study and control groups. The Ki-67 proliferative index (χ²=1.052, P=0.389), vascular invasion (χ²=2.263, P=0.197), and lymph node metastasis (χ²=1.968, P=0.386) showed no significant differences between PIMPC and NIDC patients.

Conclusion

PIMPC imaging features show specificity, such as tiny breast masses, spiculated edges, or crab claw-like patterns, and malignant signs appeared when the lesion was <2 cm in diameter. PIMPC mainly occurs in middle-aged women 45-59 y of age. Patients with PIMPC and NIDC of the breast are frequently associated with lymph node metastases and greater than one-half of the cases (74%) were shown to have a Ki-67 index >30%, suggesting a significant risk of recurrence and metastasis. Early therapeutic care for these patients is crucial. These results warrant further validation with additional samples from several centers due to the limited single-center sample size in the current study.

Breast Imaging Reporting and Data System evaluation of breast lesions improved with virtual touch tissue imaging average grayscale values

BACKGROUND

Early breast cancer diagnosis is of great clinical importance for selecting treatment options, improving prognosis, and enhancing the quality of patients' survival.

OBJECTIVE

We investigated the value of virtual touch tissue imaging average grayscale values (VAGV) helper Breast Imaging Reporting and Data System (BI-RADS) in diagnosing breast malignancy.

METHODS

We retrospectively analyzed 141 breast tumors in 134 patients. All breast lesions were diagnosed pathologically by biopsy or surgical excision. All patients first underwent conventional ultrasound (US) followed by virtual touch tissue imaging (VTI). The measurement of the VAGV of the lesion was performed by Image J software. BI-RADS classification was performed for each lesion according to the US. We performed a two-by-two comparison of the diagnostic values of VAGV, BI-RADS, and BI-RADS+VAGV.

RESULTS

VAGV was lower in malignant tumors than in benign ones (35.82 ± 13.39 versus 73.58 ± 42.69, P< 0.001). The area under the receiver operating characteristic curve (AUC) value, sensitivity, and specificity of VAGV was 0.834, 84.09%, and 69.07%, respectively. Among BI-RADS, VAGV, and BI-RADS+VAGV, BI-RADS+VAGV had the highest AUC (0.926 versus 0.882, P= 0.0066; 0.926 versus 0.834, P= 0.0012). There was perfect agreement between the two radiologists using VAGV (ICC= 0.9796) and substantial agreement using BI-RADS (Kappa= 0.725).

CONCLUSION

Our study shows that VAGV can accurately diagnose breast cancer. VAGV effectively improves the diagnostic performance of BI-RADS.

Multi-Instance Classification of Breast Tumor Ultrasound Images Using Convolutional Neural Networks and Transfer Learning

BACKGROUND

Breast cancer is arguably one of the leading causes of death among women around the world. The automation of the early detection process and classification of breast masses has been a prominent focus for researchers in the past decade. The utilization of ultrasound imaging is prevalent in the diagnostic evaluation of breast cancer, with its predictive accuracy being dependent on the expertise of the specialist. Therefore, there is an urgent need to create fast and reliable ultrasound image detection algorithms to address this issue.

METHODS

This paper aims to compare the efficiency of six state-of-the-art, fine-tuned deep learning models that can classify breast tissue from ultrasound images into three classes: benign, malignant, and normal, using transfer learning. Additionally, the architecture of a custom model is introduced and trained from the ground up on a public dataset containing 780 images, which was further augmented to 3900 and 7800 images, respectively. What is more, the custom model is further validated on another private dataset containing 163 ultrasound images divided into two classes: benign and malignant. The pre-trained architectures used in this work are ResNet-50, Inception-V3, Inception-ResNet-V2, MobileNet-V2, VGG-16, and DenseNet-121. The performance evaluation metrics that are used in this study are as follows: Precision, Recall, F1-Score and Specificity.

RESULTS

The experimental results show that the models trained on the augmented dataset with 7800 images obtained the best performance on the test set, having 94.95 ± 0.64%, 97.69 ± 0.52%, 97.69 ± 0.13%, 97.77 ± 0.29%, 95.07 ± 0.41%, 98.11 ± 0.10%, and 96.75 ± 0.26% accuracy for the ResNet-50, MobileNet-V2, InceptionResNet-V2, VGG-16, Inception-V3, DenseNet-121, and our model, respectively.

CONCLUSION

Our proposed model obtains competitive results, outperforming some state-of-the-art models in terms of accuracy and training time.

Utility of Ultrasound Imaging Features in Diagnosis of Breast Cancer

Background Currently, breast cancer (BC) is considered one of the most prevalent cancer worldwide in women and represents a global health challenge. Early diagnosis is the keystone in the management of BC patients. This study aims to assess the utility of ultrasonography (US) features of malignancy in the diagnosis of BC. Methods This retrospective cross-sectional study involved the electronic records of 326 female patients who were diagnosed with BC. A cross-tabulation test was performed to identify the association between the presence of each US feature (yes/no), and the final US diagnosis (benign/malignant). The strength of association of each feature was measured using the odds ratio (OR) which was assumed to be significant when > 1, with a 95% confidence interval (CI). Results The mean age of the female patients involved in this study was 45.36 ±12.16 years old (range, 17-90 years). Cross-tabulation test showed a significant association between the malignancy tumor and the irregular shape of the lesion (p < 0.001, OR=7.162, CI 2.726-18.814), non-circumscribed margins (p < 0.001, OR = 9.031, CI 3.200-25.489), tissue distortion (p < 0.001, OR = 18.095, CI 5.944-55.091), and the lymph node enlargement (p < 0.001, OR = 5.705, CI 2.332-13.960). Conclusion US imaging features of malignancy have a high sensitivity and positive predictive value for detection of the BC. However, the specificity of breast US imaging features is much lower because of the overlapping features in benign and malignant breast lesions. Breast lesions with an irregular shape, not circumscribed irregular or spiculated margins, hypo-echogenicity, tissue distortion, and those with lymphadenopathy have the highest likelihood of malignancy despite the low specificity. US is a highly valuable, safe, and affordable imaging modality with high diagnostic accuracy for BC.

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

OBJECTIVE

This study aimed to assess the diagnostic role of perfusion weighted image (DCE-PWI) to differentiate benign from malignant breast lesions.

PATIENTS AND METHODS

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.

RESULT

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.

CONCLUSIONS

The dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) perfusion technique plays an important role in differentiating benign and malignant tumors in breast cancer.

Development and External Validation of a Simple-To-Use Dynamic Nomogram for Predicting Breast Malignancy Based on Ultrasound Morphometric Features: A Retrospective Multicenter Study

Background

With advances in high-throughput computational mining techniques, various quantitative predictive models that are based on ultrasound have been developed. However, the lack of reproducibility and interpretability have hampered clinical use. In this study, we aimed at developing and validating an interpretable and simple-to-use US nomogram that is based on quantitative morphometric features for the prediction of breast malignancy.

Methods

Successive 917 patients with histologically confirmed breast lesions were included in this retrospective multicentric study and assigned to one training cohort and two external validation cohorts. Morphometric features were extracted from grayscale US images. After feature selection and validation of regression assumptions, a dynamic nomogram with a web-based calculator was developed. The performance of the nomogram was assessed with respect to calibration, discrimination, and clinical usefulness.

Results

Through feature selection, three morphometric features were identified as being the most optimal for predicting malignancy, and all regression assumptions of the prediction model were met. Combining all these predictors, the nomogram demonstrated a good discriminative performance in the training cohort and in the two external validation cohorts with AUCs of 0.885, 0.907, and 0.927, respectively. In addition, calibration and decision curves analyses showed good calibration and clinical usefulness.

Conclusions

By incorporating US morphometric features, we constructed an interpretable and easy-to-use dynamic nomogram for quantifying the probability of breast malignancy. The developed nomogram has good generalization abilities, which may fit into clinical practice and serve as a potential tool to guide personalized treatment. Our findings show that quantitative morphometric features from different ultrasound machines and systems can be used as imaging surrogate biomarkers for the development of robust and reproducible quantitative ultrasound dynamic models in breast cancer research.

Correlation Between Ultrasound BI-RADS 4 Breast Lesions and Fine Needle Cytology Categories in a Sample of Iraqi Female Patients

Background: Breast cancer is the most common malignancy in female and the most registered cause of women’s mortality worldwide. BI-RADS 4 breast lesions are associated with an exceptionally high rate of benign breast pathology and breast cancer, so BI-RADS 4 is subdivided into 4A, 4B and 4C to standardize the risk estimation of breast lesions.

The aim of the study: to evaluate the correlation between BI-RADS 4 subdivisions 4A, 4B & 4C and the categories of reporting FNA cytology results.

Patients and Methods: A case series study was conducted in the Oncology Teaching Hospital in Baghdad from September 2018 to September 2019. Included patients had suspicious breast findings and given BI-RADS 4 (4A, 4B, or 4C) in the radiological report accordingly. Fine needle aspiration was performed under the ultrasound guide and the results were classified into five categories. The biopsy was performed for suspicious, malignant or equivocal FNA findings.

Results: This study included 158 women with BIRADS 4 breast lesions with the mean age of (44.6 years); There was a highly significant association between BI-RADS 4 breast lesion and FNA results (p<0.001); 51.9% of BI-RADS IV-C had C5 FNA results. There was a highly significant association between BI-RADS 4 lesion and the final diagnosis (p<0.001); 41.2% of BI-RADS 4 B had a malignant breast lesion, while 37.3% of BIRADS 4 C had a malignant lesion.

Conclusion: A clear relationship was observed between BI-RADS 4 subcategories and the fine needle aspiration cytology subgroups. BI-RADS 4-B is helpful in the discrimination between benign and malignant breast lesions; furthermore BI-RADS 4C has more acceptable validity in the diagnosis of breast malignancy. Therefore, BI-RADS subcategories are encouraged to be included and mentioned in the ultrasound report for more accurate estimation of the lesion nature.

Ultrasound-Based Nomogram for Distinguishing Malignant Tumors from Nodular Sclerosing Adenoses in Solid Breast Lesions

OBJECTIVES

Nodular sclerosing adenoses (NSAs) and malignant tumors (MTs) may coexist and are often classified into the same Breast Imaging Reporting and Data System (BI-RADS) category. We aimed to build and validate an ultrasound-based nomogram to distinguish MT from NSA for building a precise sequence of biopsies.

MATERIALS AND METHODS

The training cohort included 156 patients (156 masses) with NSA or MT at one study institution. We used best subset regression to determine the predictors for building a nomogram from ultrasonic characteristics and patients' age. Model performance and clinical utility were evaluated using Brier score, concordance (C)-index, calibration curve, and decision curve analysis. The independent validation cohort consisted of 162 patients (162 masses) from a separate institution.

RESULTS

Through best subset regression, we selected 6 predictors to develop nomogram: age, calcification, echogenic rim, vascularity distribution, tumor size, and thickness of breast parenchyma. Brier score and C-index of the nomogram in the training cohort were 0.068 and 0.967 (95% confidence interval [CI]: 0.941-0.993), respectively. In addition, calibration curve demonstrated good agreement between prediction and pathological result. In the validation cohort, the nomogram still obtained a favorable C-index score of 0.951 (95% CI: 0.919-0.983) and fine calibration. Decision curve analysis showed that the model was clinically useful.

CONCLUSIONS

If multiple NSA and MT masses are present in the same patient and are classified into the same BI-RADS category, our nomogram can be used as a supplement to the BI-RADS category for accurate biopsy of the mass most likely to be MT.

Stromal Fibrosis of the Breast and the Associated Radiological Findings

This case report follows a 42-year-old female patient who underwent a routine screening mammogram. The patient was found to have a 50 mm benign subareolar mass in the right breast. However, because benign imaging findings do not preclude malignancy, the patient was recommended to undergo a biopsy for confirmation. Subsequent imaging and core needle biopsy established a benign lesion consistent with stromal fibrosis with underlying fibroadenomatous changes. The benign imaging and histological findings of the breast mass were concordant. The patient was recommended yearly mammograms and continued observation. This case report highlights the importance of radiopathological concordance in patients found to have benign imaging findings on screening mammograms.

Sonographic Evaluation of Incidental Synchronous Masses in Patients with Breast Cancer: Clinical Significance and Diagnostic Workup

Objective

This study aimed to evaluate the diagnostic accuracy of breast ultrasonography in classifying incidental satellite masses as benign or malignant in patients with breast cancer and to assess its effect on their surgical management.

Materials and Methods

Ultrasound-guided fine needle aspiration/biopsy was performed in 288 satellite masses of 225 patients with breast cancer. Two radiologists retrospectively reviewed the sonograms of these masses and classified them as benign or malignant and compared this feature with the results of the histopathological examination. The location of the satellite mass and type of surgery were also documented.

Results

Of the 288 satellite masses, 139 were located in the same quadrant, 95 in different quadrants, while 54 were in the contralateral breast. Of the 123 sonographically benign masses, 106 showed benign pathological outcome, and from 165 sonographically malignant masses, 127 were found malignant on histopathology/cytology. McNemar's chi-square was 7.27 (p-value=0.007), showing statistically significant association between sonographic features and pathological outcome of satellite masses. The sensitivity, specificity, positive and negative predictive values, and accuracy were 88.2%, 73.6%, 77%, 86.1%, and 80.9% respectively. Based on these findings, 61 patients underwent lumpectomy limited to a single tumor, 52 underwent extended resection, 78 underwent mastectomy, four underwent lumpectomy for the contralateral breast, and bilateral mastectomies were performed in another four patients. Surgery was not performed in 26 patients.

Conclusion

Although ultrasound is an effective tool for the detection and characterization of incidental satellite masses in patients with breast cancer, biopsy is imperative to ascertain the pathological diagnosis and, therefore, select the most appropriate surgical plan.

Efficacy of breast MRI for surgical decision in patients with breast cancer: ductal carcinoma in situ versus invasive ductal carcinoma

Background

Preoperative breast magnetic resonance imaging (MRI) provides more information than mammography and ultrasonography for determining the surgical plan for patients with breast cancer. This study aimed to determine whether breast MRI is more useful for patients with ductal carcinoma in situ (DCIS) lesions than for those with invasive ductal carcinoma (IDC).

Methods

A total of 1113 patients with breast cancer underwent mammography, ultrasonography, and additional breast MRI before surgery. The patients were divided into 2 groups: DCIS (n = 199) and IDC (n = 914), and their clinicopathological characteristics and oncological outcomes were compared. Breast surgery was classified as follows: conventional breast-conserving surgery (Group 1), partial mastectomy with volume displacement (Group 2), partial mastectomy with volume replacement (Group 3), and total mastectomy with or without reconstruction (Group 4). The initial surgical plan (based on routine mammography and ultrasonography) and final surgical plan (after additional breast MRI) were compared between the 2 groups. The change in surgical plan was defined as group shifting between the initial and final surgical plans.

Results

Changes (both increasing and decreasing) in surgical plans were more common in the DCIS group than in the IDC group (P <  0.001). These changes may be attributed to the increased extent of suspicious lesions on breast MRI, detection of additional daughter nodules, multifocality or multicentricity, and suspicious findings on mammography or ultrasonography but benign findings on breast MRI. Furthermore, the positive margin incidence in frozen biopsy was not different (P = 0.138).

Conclusions

Preoperative breast MRI may provide more information for determining the surgical plan for patients with DCIS than for those with IDC.

Imaging Features of Inflammatory Breast Disorders: A Pictorial Essay

Inflammatory breast disorders include a wide array of underlying causes, ranging from common benign infection, non-infectious inflammation and inflammation resulting from underlying breast malignancy. Because it is at times difficult to distinguish mastitis and breast cancer based on clinical features, awareness of detailed imaging features may be helpful for better management of inflammatory breast disorders. Therefore, this pictorial essay intends to demonstrate radiologic findings of a variety of inflammatory breast disorders, using selected cases with mammography, ultrasound and magnetic resonance images.

Color Doppler ultrasound assessment of morphology and types of fistulous tracts in hidradenitis suppurativa (HS)

BACKGROUND

Fistulous tracts in hidradenitis suppurativa (HS) are key signs of severity and their clinical evaluation alone may be limited for assessing their presence and morphology. There is also a need to determine the factors that allow reversibility of the anatomic changes in HS.

OBJECTIVE

We sought to categorize fistulous tracts in HS.

METHODS

A retrospective study of color Doppler ultrasound images of cases with positive clinical and sonographic criteria of HS with fistulous tracts was performed. The sonographic staging of HS, location, and anatomic characteristics of the tracts were registered and graded. Statistical analysis for correlating variables was performed using bivariate and multivariate studies.

RESULTS

In all, 52 patients presenting 96 fistulous tracts met the criteria. Morphology was defined and a sonographic classification into 3 types of fistulae was developed. Type 3 concentrated 71% of the cases presenting communicating tracts, and type 2, 29%. Types 2 and 3 represented 63% of patients with multiple fistulous tracts. Fistulous tracts types 2 and 3 were significantly correlated with age 35 years or older and groin location.

LIMITATIONS

Ultrasound cannot detect lesions less than 0.1 mm.

CONCLUSION

Fistulous tracts in HS can be categorized using ultrasound, which may support earlier and more precise management.