Mucinous versus medullary breast carcinoma: mammography, ultrasound, and MRI findings

Deep Learning for Distinguishing Mucinous Breast Carcinoma From Fibroadenoma on Ultrasound

PURPOSE

Mucinous breast carcinoma (MBC) tends to be misdiagnosed as fibroadenomas (FA) due to its benign imaging characteristics. We aimed to develop a deep learning (DL) model to differentiate MBC and FA based on ultrasound (US) images. The model could contribute to the diagnosis of MBC for radiologists.

METHODS

In this retrospective study, 884 eligible patients (700 FA patients and 184 MBC patients) with 2257 US images were enrolled. The images were randomly divided into a training set (n = 1805 images) and a test set (n = 452 images) in a ratio of 8:2. First, we used the training set to establish DL model, DL+ age-cutoff model and DL+ age-tree model. Then, we compared the diagnostic performance of three models to get the optimal model. Finally, we evaluated the diagnostic performance of radiologists (4 junior and 4 senior radiologists) with and without the assistance of the optimal model in the test set.

RESULTS

The DL+ age-tree model yielded higher areas under the receiver operating characteristic curve (AUC) than DL model and DL+ age-cutoff model (0.945 vs. 0.835, P < .001; 0.945 vs. 0.931, P < .001, respectively). With the assistance of DL+ age-tree model, both junior and senior radiologists' AUC had significant improvement (0.746-0.818, P = .010, 0.827-0.860, P = .005, respectively).

CONCLUSIONS

The DL+ age-tree model based on US images and age showed excellent performance in the differentiation of MBC and FA. Moreover, it can effectively improve the performance of radiologists with different degrees of experience that may contribute to reducing the misdiagnosis of MBC.

Special Types of Breast Cancer: Clinical Behavior and Radiological Appearance

Breast cancer is a complex disease that includes entities with different characteristics, behaviors, and responses to treatment. Breast cancers are categorized into subgroups based on histological type and grade, and these subgroups affect clinical presentation and oncological outcomes. The subgroup of "special types" encompasses all those breast cancers with insufficient features to belong to the subgroup "invasive ductal carcinoma not otherwise specified". These cancers account for around 25% of all cases, some of them having a relatively good prognosis despite high histological grade. The purpose of this paper is to review and illustrate the radiological appearance of each special type, highlighting insights and pitfalls to guide breast radiologists in their routine work.

Artificial intelligence in breast imaging: potentials and challenges

Breast cancer, which is the most common type of malignant tumor among humans, is a leading cause of death in females. Standard treatment strategies, including neoadjuvant chemotherapy, surgery, postoperative chemotherapy, targeted therapy, endocrine therapy, and radiotherapy, are tailored for individual patients. Such personalized therapies have tremendously reduced the threat of breast cancer in females. Furthermore, early imaging screening plays an important role in reducing the treatment cycle and improving breast cancer prognosis. The recent innovative revolution in artificial intelligence (AI) has aided radiologists in the early and accurate diagnosis of breast cancer. In this review, we introduce the necessity of incorporating AI into breast imaging and the applications of AI in mammography, ultrasonography, magnetic resonance imaging, and positron emission tomography/computed tomography based on published articles since 1994. Moreover, the challenges of AI in breast imaging are discussed.

On Ultrasonographic Features of Mucinous Carcinoma with Micropapillary Pattern

Objective

To describe the sonographic features of pure mucinous carcinoma with micropapillary pattern (MUMPC) and compare with different pathological type of mucinous breast carcinoma.

Methods

Subjects were retrospectively reviewed at Suzhou Municipal Hospital from January 2015 to June 2019. Sonographic features of 49 cases (9 MUMPC, 19cPMBC, and 21 MMBC) pathologically confirmed MBC were recorded according to the Breast Imaging Reporting and Data System (BI-RADS) lexicon. The differences in sonographic features among different type of mucinous breast carcinoma were discussed, including clinical features and sonographic features, shape, lesion boundary, peripheral hyperechoic ring, echo pattern, posterior acoustic feature, thickness peripheral hyperechoic area, and blood flow.

Results

All MUMPC had no lymph node metastasis (88.9%, 8/9), and most of the MUMPC showed no thickness peripheral hyperechoic area (88.9%, 8/9) and blood flow (55.6%, 5/9) within the tumor. Furthermore, MUMPC had mixed cystic and solid components (33.3%, 3/9) and solid echoic (66.7%, 6/9) structures, with regular shape (66.7%, 6/9) and peripheral hyperechoic ring (66.7%, 6/9). Seven cases of the MUMPC showed circumscribed margin (77.8%, 7/9), and there was significant difference among the three groups (p < 0.05). In addition, there were 7 cases (77.8%, 7/9) of MUMPC tumor ≤2cm, which was significantly different from cPMBC (26.3%, 5/19) and MMBC (28.6%, 6/21) (p < 0.05). There was no significant difference in ultrasonographic features of MBC with different sizes when stratified by tumor size (p > 0.05).

Conclusion

Most of the MUMPC showed a circumscribed margin, peripheral hyperechoic ring, and without lymph node metastasis and thickness peripheral hyperechoic area. However, it is challenging to distinguish MUMPC from PMBC and MMBC on ultrasound. Future research should focus on developing novel analysis methods for ultrasound imaging, conducting studies with larger sample sizes and diverse population groups.

Multiparametric MRI Features of Breast Cancer Molecular Subtypes

Background and Objectives: Breast cancer (BC) molecular subtypes have unique incidence, survival and response to therapy. There are five BC subtypes described by immunohistochemistry: luminal A, luminal B HER2 positive and HER2 negative, triple negative (TNBC) and HER2-enriched. Multiparametric breast MRI (magnetic resonance imaging) provides morphological and functional characteristics of breast tumours and is nowadays recommended in the preoperative setting. Aim: To evaluate the multiparametric MRI features (T2-WI, ADC values and DCE) of breast tumours along with breast density and background parenchymal enhancement (BPE) features among different BC molecular subtypes. Materials and Methods: This was a retrospective study which included 344 patients. All underwent multiparametric breast MRI (T2WI, ADC and DCE sequences) and features were extracted according to the latest BIRADS lexicon. The inter-reader agreement was assessed using the intraclass coefficient (ICC) between the ROI of ADC obtained from the two breast imagers (experienced and moderately experienced). Results: The study population was divided as follows: 89 (26%) with luminal A, 39 (11.5%) luminal B HER2 positive, 168 (48.5%) luminal B HER2 negative, 41 (12%) triple negative (TNBC) and 7 (2%) with HER2 enriched. Luminal A tumours were associated with special histology type, smallest tumour size and persistent kinetic curve (all p-values < 0.05). Luminal B HER2 negative tumours were associated with lowest ADC value (0.77 × 10−3 mm2/s2), which predicts the BC molecular subtype with an accuracy of 0.583. TNBC were associated with asymmetric and moderate/marked BPE, round/oval masses with circumscribed margins and rim enhancement (all p-values < 0.05). HER2 enriched BC were associated with the largest tumour size (mean 37.28 mm, p-value = 0.02). Conclusions: BC molecular subtypes can be associated with T2WI, ADC and DCE MRI features. ADC can help predict the luminal B HER2 negative cases.

Rare Case of Intravascular Myopericytoma-Imaging Characteristics and Review of the Literature

Myopericytoma is a rare vessel wall tumor, a subtype of hemangiopericytoma that usually develops subcutaneously. Intravascular myopericytoma is a rarer subtype, with only few cases reported in the literature and even fewer with imaging modalities included. We report the case of a 36-year-old man who was referred to our institution with a painless, palpable mass in the right arm and was evaluated with MRI, grey-scale and Doppler-mode ultrasound. Tumor histopathology and imaging characteristics are presented together with the role that each imaging modality played in the management of the patient.

Mucinous carcinoma of the breast: distinctive histopathologic and genetic characteristics

Mucinous carcinoma is a rare histologic type of breast cancer that, when classified with favorable histology, can be treated with different therapeutic options. This study reviews the histologic findings of mucinous carcinoma that support or exclude favorable histology and emphasizes the necessity of an appropriate gross examination with radiologic findings for an accurate diagnosis. In addition, unusual findings such as micropapillary arrangements and lobular differentiation in mucinous carcinoma and their implications for prognosis and treatment are reviewed. Mucinous carcinoma involves upregulation of MUC2, a mucus-associated gene common in mucinous carcinoma of the breast as well as various other organs. In mucinous carcinoma, the fraction of genome altered and tumor mutation burden are lower than those of invasive carcinoma of no special type, the most common histology of breast cancer. In addition, the genetic alterations found in mucinous carcinoma are diverse, unlike the pathognomonic genetic alterations observed in other histologic types of breast cancer. These genetic features support the importance of conventional microscopic evaluations for the pathologic differential diagnosis of mucinous carcinoma of the breast in routine practice. A variety of breast lesions, including mucinous cystadenocarcinoma and mucocele-like lesions, as well as mucinous carcinoma from other organs, can mimic mucinous carcinoma of the breast. In order to obtain an accurate pathologic diagnosis, careful evaluation of the overall histopathologic characteristics and ancillary testing are required to provide information on appropriate treatment and prognosis.

Are Mutation Carrier Patients Different from Non-Carrier Patients? Genetic, Pathology, and US Features of Patients with Breast Cancer

The purpose of this study is to evaluate the relationship between the pathogenic/likely pathogenic mutations, US features, and histopathologic findings of breast cancer in mutation carriers compared to non-carrier patients. Methods: In this retrospective study, we identified 264 patients with breast cancer and multigene panel testing admitted to our clinic from January 2018 to December 2020. Patient data US findings, US assessment of the axilla, multigene panel tests, histopathology, and immunochemistry reports were reviewed according to the BI-RADS lexicon. Results: The study population was comprised of 40% pathogenic mutation carriers (BRCA1, BRCA2, CHEK2, ATM, PALB, TP 53, NBN, MSH, BRIP 1 genes) and 60% mutation-negative patients. The mean patient age was 43.5 years in the carrier group and 44 years in the negative group. Carrier patients developed breast cancer with benign morphology (acoustic enhancement, soft elastography appearance) compared to non-carriers (p < 0.05). A tendency towards specific US features was observed for each mutation. BRCA1 carriers were associated with BC with microlobulated margins, hyperechoic rim, and soft elastography appearance (p < 0.05). Estrogen receptor (ER)-negative tumors were associated with BRCA1, TP53, and RAD mutations, while BRCA2 and CHEK2 were associated with ER-positive tumors. Conclusions: Patients with pathogenic mutations may exhibit BC with benign US features compared to negative, non-carrier patients. BRCA1, TP53, and RAD carriers account for up to one third of the ER tumors from the carrier group. Axillary US performed worse in depicting involved lymph nodes in carrier patients, compared to negative patients.

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.

Mucinous carcinoma in a male patient: Diagnosis and management of breast cancer in male patients

Male breast cancer is a rare but serious condition that impacts an increasing number of men each year. Due to low incidence rate, there is a current lack of established diagnostic and management practices. Here, we provide a review of the current epidemiology, classification, diagnosis, and treatment of male breast cancer. We present a rare case of mucinous breast cancer in a 74-year-old male patient detected after he presented with a retroareolar mass. The patient underwent mammography, targeted ultrasound, and ultrasound-guided core needle biopsy, which established the diagnosis. He was treated surgically with left mastectomy and sentinel lymph node biopsy with axillary lymph node dissection, followed by post-adjuvant tamoxifen and has remained free of recurrence and metastasis since.

A Case of Secretory Carcinoma in a Patient With a History of Contralateral Medullary Carcinoma

BACKGROUND

Secretory and medullary carcinomas of the breast are rare subtypes of infiltrating ductal carcinoma. The different histological behavior of medullary and secretory carcinomas is correlated with different imaging features on mammography, ultrasound, and magnetic resonance imaging.

CASE REPORT

We report the case of a Caucasian woman in which both subtypes of tumors were diagnosed in an 8-year time interval and evaluate, in antithesis, histopathological and imaging aspects of medullary and secretory carcinoma.

CONCLUSION

To our knowledge, this is the first case reported in literature of secretory carcinoma with a complete imaging tumor evaluation in a patient with a previous contralateral medullary cancer.

Superb Microvascular Imaging Technology Can Improve the Diagnostic Efficiency of the BI-RADS System

BACKGROUND

To explore whether superb microvascular imaging (SMI)SMI can improve the diagnostic efficiency by evaluating the vascular index (VI) and vascular architecture (VA) in breast lesions.

METHODS

This is a retrospective study of data collected prospectively for research use. Taking 225 consecutive cases of breast lesions from November 2016 to December 2017 as a training set, the VI values and VA types of benign and malignant lesions were calculated based on the pathological results. Taking 238 consecutive cases of breast lesions from January 2018 to October 2018 as the verification set, the diagnostic sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV) were calculated to compare the diagnostic efficacy.

RESULTS

The training set included 225 breast lesions and the validation set 238 breast lesions. The VI value in the malignant group (10.3 ± 8.0) was significantly higher than that in the benign group (4.3 ± 5.0)(P<0.001). A VI value of 4.05 was used as the diagnostic threshold for differentiating benign from malignant lesions, with a sensitivity of 80.5%, a specificity of 61.9%, an accuracy of 71.1%, a PPV of 62.9%, a NPV of 76.9%, and an area under the curve of 0.758 (0.696-0.819). There was a significant difference in the types of benign and malignant VA (P < 0.001), and the PPV of the root hair-like and crab claw-like VAs were 93.9% and 100.0%, respectively. The diagnostic sensitivity, specificity, accuracy, PPV, NPV and area under the AUC curve were 58.0%, 98.2%, 97.0%, 70.3% and 0.781, respectively (95%CI: 0.719-0.844). SMI combined with conventional ultrasound improved the diagnostic specificity (70.0% vs. 90.0%), accuracy (87.4% vs. 96.6%), and PPV (82.5% vs. 93.2%) without decreasing the diagnostic sensitivity (99.3%), yielded higher diagnostic performance with the area under the ROC curve was 0.941 (95%CI: 0904-0.979) compared with conventional US alone (P < 0.001).

CONCLUSION

A VI value 4.05 is a cut-off value with good diagnostic efficacy. The residual root-like and crab claw-like VAs are the characteristic VAs of malignant lesions. Conventional ultrasound combined with the VI and VA can improve the diagnostic specificity, accuracy and PPV without reducing the diagnostic sensitivity.