Imaging features of granulomatous mastitis in 36 patients with new sonographic signs

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.

Investigation of multi-infections and breast disease comorbidities in granulomatous mastitis

Introduction

Granulomatous mastitis (GM) is an inflammatory breast disease typically caused by infection, posing diagnostic challenges. It can coexist with other breast disorders or multiple infections, which have been vaguely discussed. This study investigates the incidence of multi-infection and breast disease comorbidities in GM.

Method

The study enroled 63 females who had a confirmed diagnosis of GM. Laboratory investigations and bacterial cultures had been conducted for all the cases. The patients had undergone ultrasonography examination utilizing the LOGIQ E9 system. Core needle biopsy had been done to procure tissue samples for histopathological examination. Thorough scrutiny and assessment of patients' records were performed. The variables encompassed age at presentation, breastfeeding data, parity, smoking status, seasonal affliction, hair-washing agents, exposure to radiation, comorbidities, and clinical, ultrasound and histopathological findings.

Results

The patients' ages ranged from 24 to 50. Breastfeeding history was positive in nearly all cases (97%). The majority of cases exhibited multiparity (81%). In total, 63.5% were passive smokers. Multi-infections were detected in six cases (9.5%). Among them, B. cepacia complex and Toxoplasma gondii were identified in two cases (3.16%). Other multi-infections involved Staphylococcus epidermidis and Toxoplasma gondii, Burkholderia cepacia and S. kloosii and Toxoplasma gondii, Staphylococcus epidermis and Brucella spp., Candida spp. and Brucella spp. Histopathological analysis revealed GM comorbidities with other breast diseases in 35% of the cases.

Conclusion

Multi-infections and breast disease comorbidities may further complicate diagnosis and management of GM. The findings of this study may raise additional questions about the nature of the disease or potential complications associated with it.

A sonogram radiomics model for differentiating granulomatous lobular mastitis from invasive breast cancer: a multicenter study

PURPOSE

To construct a nomogram based on sonogram features and radiomics features to differentiate granulomatous lobular mastitis (GLM) from invasive breast cancer (IBC).

MATERIALS AND METHODS

A retrospective collection of 213 GLMs and 472 IBCs from three centers was divided into a training set, an internal validation set, and an external validation set. A radiomics model was built based on radiomics features, and the RAD score of the lesion was calculated. The sonogram radiomics model was constructed using ultrasound features and RAD scores. Finally, the diagnostic efficacy of the three sonographers with different levels of experience before and after combining the RAD score was assessed in the external validation set.

RESULTS

The RAD score, lesion diameter, orientation, echogenicity, and tubular extension showed significant differences in GLM and IBC (p < 0.05). The sonogram radiomics model based on these factors achieved optimal performance, and its area under the curve (AUC) was 0.907, 0.872, and 0.888 in the training, internal, and external validation sets, respectively. The AUCs before and after combining the RAD scores were 0.714, 0.750, and 0.830 and 0.834, 0.853, and 0.878, respectively, for sonographers with different levels of experience. The diagnostic efficacy was comparable for all sonographers when combined with the RAD score (p > 0.05).

CONCLUSION

Radiomics features effectively enhance the ability of sonographers to discriminate between GLM and IBC and reduce interobserver variation. The nomogram combining ultrasound features and radiomics features show promising diagnostic efficacy and can be used to identify GLM and IBC in a noninvasive approach.

Management of granulomatous lobular mastitis: an international multidisciplinary consensus (2021 edition)

Granulomatous lobular mastitis (GLM) is a rare and chronic benign inflammatory disease of the breast. Difficulties exist in the management of GLM for many front-line surgeons and medical specialists who care for patients with inflammatory disorders of the breast. This consensus is summarized to establish evidence-based recommendations for the management of GLM. Literature was reviewed using PubMed from January 1, 1971 to July 31, 2020. Sixty-six international experienced multidisciplinary experts from 11 countries or regions were invited to review the evidence. Levels of evidence were determined using the American College of Physicians grading system, and recommendations were discussed until consensus. Experts discussed and concluded 30 recommendations on historical definitions, etiology and predisposing factors, diagnosis criteria, treatment, clinical stages, relapse and recurrence of GLM. GLM was recommended as a widely accepted definition. In addition, this consensus introduced a new clinical stages and management algorithm for GLM to provide individual treatment strategies. In conclusion, diagnosis of GLM depends on a combination of history, clinical manifestations, imaging examinations, laboratory examinations and pathology. The approach to treatment of GLM should be applied according to the different clinical stage of GLM. This evidence-based consensus would be valuable to assist front-line surgeons and medical specialists in the optimal management of GLM.

Does ARFI elastography complement B-mode ultrasonography in the radiological diagnosis of idiopathic granulomatous mastitis and invasive ductal carcinoma?

BACKGROUND

Idiopathic granulomatous mastitis (IGM) is a chronic, unpleasant autoimmune inflammatory condition and is clinically and radiologically often confused with breast malignancy.

PURPOSE

To investigate the contributions of qualitative and quantitative aspects of acoustic radiation force impulse (ARFI) elastography to the differential diagnosis between IGM and invasive ductal carcinoma (IDC) in the breast.

MATERIAL AND METHODS

Ninety-four women with IDC and 39 with IGM were included in the study. Shear wave velocity (SWV) was calculated for all lesions using quantitative elastography. Next, each lesion's correspondence on qualitative elastographic images to those on the B-mode images was evaluated: pattern 1, no findings on elastography images; pattern 2, lesions that were bright inside; pattern 3, lesions that contained both bright and dark areas; and pattern 4, lesions that were dark inside. Pattern 4 was subdivided into 4a (dark area same size as lesion) and 4b (dark area larger than lesion size). Sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy were calculated.

RESULTS

The mean SWV based on ARFI elastography was 3.78 ± 1.26 m/s for IGM and 5.34 ± 1.43 m/s for IDC lesions (P < 0.05). Based on qualitative ARFI elastography, IDC lesions were mostly classified as pattern 4b, while IGM lesions were mostly classified as pattern 1 or 2 (P = 0.01). Evaluation of both the qualitative and quantitative aspects of ARFI elastography yielded a sensitivity of 89% and specificity of 84%.

CONCLUSION

ARFI elastography may facilitate the differential diagnosis between IGM and IDC.

A quantization assisted U-Net study with ICA and deep features fusion for breast cancer identification using ultrasonic data

Breast cancer is one of the leading causes of death in women worldwide-the rapid increase in breast cancer has brought about more accessible diagnosis resources. The ultrasonic breast cancer modality for diagnosis is relatively cost-effective and valuable. Lesion isolation in ultrasonic images is a challenging task due to its robustness and intensity similarity. Accurate detection of breast lesions using ultrasonic breast cancer images can reduce death rates. In this research, a quantization-assisted U-Net approach for segmentation of breast lesions is proposed. It contains two step for segmentation: (1) U-Net and (2) quantization. The quantization assists to U-Net-based segmentation in order to isolate exact lesion areas from sonography images. The Independent Component Analysis (ICA) method then uses the isolated lesions to extract features and are then fused with deep automatic features. Public ultrasonic-modality-based datasets such as the Breast Ultrasound Images Dataset (BUSI) and the Open Access Database of Raw Ultrasonic Signals (OASBUD) are used for evaluation comparison. The OASBUD data extracted the same features. However, classification was done after feature regularization using the lasso method. The obtained results allow us to propose a computer-aided design (CAD) system for breast cancer identification using ultrasonic modalities.

Methods for the segmentation and classification of breast ultrasound images: a review

PURPOSE

Breast ultrasound (BUS) is one of the imaging modalities for the diagnosis and treatment of breast cancer. However, the segmentation and classification of BUS images is a challenging task. In recent years, several methods for segmenting and classifying BUS images have been studied. These methods use BUS datasets for evaluation. In addition, semantic segmentation algorithms have gained prominence for segmenting medical images.

METHODS

In this paper, we examined different methods for segmenting and classifying BUS images. Popular datasets used to evaluate BUS images and semantic segmentation algorithms were examined. Several segmentation and classification papers were selected for analysis and review. Both conventional and semantic methods for BUS segmentation were reviewed.

RESULTS

Commonly used methods for BUS segmentation were depicted in a graphical representation, while other conventional methods for segmentation were equally elucidated.

CONCLUSIONS

We presented a review of the segmentation and classification methods for tumours detected in BUS images. This review paper selected old and recent studies on segmenting and classifying tumours in BUS images.

You'll see it when you know it: granulomatous mastitis

Granulomatous mastitis (GM) is an under-recognized and under-diagnosed disease. Patients with GM often present to the emergency room with a painful breast mass, nipple retraction, and skin changes. This pictorial essay will review the clinical presentation and imaging appearance of GM, BI-RADS reporting parameters, differential diagnoses, and diagnostic challenges posed by this disease. Early and accurate diagnosis is essential, as misdiagnosis can result in repeated core biopsies, leading to fistulae and sinus tract formation. A classic history and typical sonographic appearance allow the emergency radiologist to confidently make this diagnosis.

Apparent diffusion coefficient magnetic resonance imaging (ADC-MRI) in the axillary breast cancer lymph node metastasis detection: a narrative review

The presence of axillary lymph nodes metastases in breast cancer is the most significant prognostic factor, with a great impact on morbidity, disease-related survival and management of oncological therapies; for this reason, adequate imaging evaluation is strictly necessary. Physical examination is not enough sensitive to assess breast cancer nodal status; axillary ultrasonography (US) is commonly used to detect suspected or occult nodal metastasis, providing exclusively morphological evaluation, with low sensitivity and positive predictive value. Currently, sentinel lymph node biopsy (SLNB) and/or axillary dissection are the milestone for the diagnostic assessment of axillary lymph node metastases, although its related morbidity. The impact of magnetic resonance imaging (MRI) in the detection of nodal metastases has been widely investigated, as it continues to represent the most promising imaging modality in the breast cancer management. In particular, diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) values represent additional reliable non-contrast sequences, able to improve the diagnostic accuracy of breast cancer MRI evaluation. Several studies largely demonstrated the usefulness of implementing DWI/ADC MRI in the characterization of breast lesions. Herein, in the light of our clinical experience, we perform a review of the literature regarding the diagnostic performance and accuracy of ADC value as potential pre-operative tool to define metastatic involvement of nodal structures in breast cancer patients. For the purpose of this review, PubMed, Web of Science, and SCOPUS electronic databases were searched with different combinations of "axillary lymph node", "breast cancer", "MRI/ADC", "breast MRI" keywords. All original articles, reviews and metanalyses were included.

Diagnostic performance of shear wave elastography in discriminating malignant and benign breast lesions : Our experience with QelaXtoTM software

STUDY AIMS

We sought to evaluate the diagnostic performance of quantitative elastography (shear wave elastography) and to establish the optimal cutoff value to differentiate malignant and benign breast lesions using QelaXtoTM software.

METHODS

We conducted a retrospective observational study of adult women with suspicious breast lesions (BIRADS 3, 4 or 5) who underwent programmed ultrasound-guided core biopsies. Breast lesions were assessed using quantitative elastography combined with B-mode ultrasound. Histopathology was used as reference standard. Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were estimated, and a ROC curve analysis was conducted. Three elastography cutoff values were considered: 36, 50 and 80 kPa.

RESULTS

We included 143 women (mean age of 56 years) with a total of 145 breast lesions: 68 benign tumors (47.26%) and 77 malignancies (52.74%). Mean elasticity measurements of benign and malignant lesions were significantly different (24.6 kPa, SD 28.47, vs. 101.49 kPa, SD 47.38, [Formula: see text]). Using the 50 kPa cutoff, elastography showed a global sensitivity of 87% to discriminate malignant lesions (AUC = 0.897). Moreover, sensitivity was 90.7% when lesions were located 5-40 mm below the skin surface (optimal elastographic field of view). Our false positive rate was 17.65%, comprised mainly of fibroepithelial neoplasms, fibroadenomas and fibrosis.

CONCLUSIONS

Quantitative elastography can differentiate malignant and benign breast lesions with acceptable to excellent performance. In our sample, the QelaXtoTM software showed a lower optimal cutoff than other ultrasound systems.

S-Detect characterization of focal breast lesions according to the US BI RADS lexicon: a pictorial essay

High-resolution ultrasonography (US) is a valuable tool in breast imaging. Nevertheless, US is an operator-dependent technique: to overcome this issue, the American College of Radiology (ACR) has developed the breast imaging-reporting and data system (BI-RADS) US lexicon. Despite this effort, the variability in the assessment of focal breast lesions (FBLs) with the use of BI-RADS US lexicon is still an issue. Within this framework, evidence shows that computer-aided image analysis may be effective in improving the radiologist's assessment of FBLs. In particular, S-Detect is a newly developed image-analytic computer program that provides assistance in morphologic analysis of FBLs seen on US according to the BI-RADS US lexicon. This pictorial essay describes state-of-the-art of sonographic characterization of FBLs by using S-Detect.