Ultrasound Findings in Three Complex Cystic Breast Carcinomas: Correlation with Intrinsic Tumor Subtype.

In ultrasound examinations, mixed mammary gland masses are divided into either intracystic masses that contain a solid component in the cyst or solid masses that contain a fluid component in the mass. The histological types and subtypes of three complex cystic masses that showed different internal compositions in ultrasound were determined using the ultrasound findings of three patients. Case 1: The mass showed a large cystic component (bleeding) inside and a broad-based solid lesion at the margin in the ultrasound finding. The histological type was encapsulated papillary carcinoma and the subtype was luminal A. Case 2: The mass was lobulated with a small cystic component at the margin. The histological type was solid papillary carcinoma and the subtype was luminal A. Case 3: The mass was lobulated with a circumscribed margin. Cystic components suspected of being hemorrhagic necrosis were observed at the margin and within the solid component. The histological type was squamous cell carcinoma and the subtype was triple negative. Case 2 was a solid mass in appearance, but a cystic component noted at the margin was possibly an intracystic mass. For Case 3, findings suggestive of necrosis were observed both at the margin and in the solid component and this suggested a mass with fluid degeneration. Complex cystic masses are usually examined with a focus on the solid component seen on ultrasound images; however, it is also important to observe the cystic composition. This can help determine the subtypes in addition to the histological types.

[The value of ultrasound classification in BI-RADS category 4 of breast complex cystic masses].

Objective: To analyze the feature of breast complex cystic masses and to classify it at ultrasonography (US), which applied to the Breast Imaging Reporting and Data System (BI-RADS) categories 4a to 4c with pathological results as the golden standards. Methods: The ultrasonographic data and clinical features of 78 patients with complex cystic masses confirmed by pathology in Cancer Hospital from July 2014 to June 2017 were retrospectively reviewed. The complex cystic breast masses were divided into four classes on the basis of their US features: type 1 [thick wall and (or) thick septa (> 0.5 mm)], type 2 (one or more intra-cystic masses), type 3 (mixed cystic and solid components with cystic components more than 50%) and type 4 (mixed cystic and solid components with solid components more than 50%). Positive values (PPVs) were calculated for each type. Multiple linear regression analysis was used to analyze the ultrasonographic features of the masses (lesion size, margins, blood flow resistance index, calcification, and axillary lymph nodes, etc.) with malignant correlation. Results: There were 81 lesions in 78 patients. Among the 81 masses based on US appearance, 14 (17.3%) were classified as type Ⅰ, 18 (22.2%) as type Ⅱ, 18 (22.2%) as type Ⅲ, and 31 (38.3%) as type Ⅳ. The positive predictive values of the malignant lesions of type Ⅰ, type Ⅱ, Ⅲ and Ⅳ were 7.1%, 16.7%, 61.1% and 48.3%, respectively (P=0.040). In all the 81 masses, 14 were BI-RADS categories 4a, 18 were BI-RADS categories 4b and 49 were BI-RADS categories 4c. Masses with maximum diameter equal to or larger than 2.0 cm, unclear margins, RI≥0.7 and presence of abnormal axillary nodes assessment had a high probability of malignancy (P=0.030, 0.038, <0.001 and 0.025, respectively). Conclusion: Ultrasound typing is helpful for differentiating benign and malignant breast complex cysts and classifying BI-AIDS 4a to 4c, thus providing clearer treatment for clinical practice.

Influence of age on PPV of sonographic BI-RADS categories 3, 4, and 5.

PURPOSE: The purpose of this retrospective study was to calculate the positive predictive value (PPV) of sonographic Breast Imaging Reporting and Data System (BI-RADS) categories 3, 4, and 5 in different age groups to investigate whether age influences the PPV of the BI-RADS category in breast ultrasound. MATERIALS AND METHODS: From our sonography-guided core biopsy database of breasts between 2006 and 2008, we identified 2817 BI-RADS category 3, 4, and 5 lesions with known pathological diagnosis in 2587 women, all of whom underwent the earlier breast assessment via ultrasound with a sonographic BI-RADS lexicon and later sonography-guided core biopsy. All lesions were classified into three age groups (< 45, 45 - 59, and > 59 years). The age-related PPVs of each BI-RADS category among three age groups were calculated on the basis of pathological diagnoses and were compared using a χ(2)-test. RESULTS: The overall PPV of each BI-RADS category was 2.2 % in category 3, 6.5 % in category 4a, 35.2 % in category 4b, 79.6 % in category 4c, and 99.6 % in category 5. The age-related PPVs of category 3 varied significantly among the three age groups (0.9 % versus 3.9 % versus 2.0 % p = 0.048), and notably, the age-related PPV in group 2 was higher than the others. Additionally, there was a significant positive association between the age-related PPVs and increasing age in categories 4a and 4b (4a, p < 0.0001 and 4b, p = 0.0139), but not in categories 4c and 5 (4c, p = 0.1853 and 5, p = 0.2871). CONCLUSION: The incidence of female breast cancer differs not only in different sonographic BI-RADS categories, but also in different age groups. Therefore, more attention should be paid to the special age group that we found for sonographic BI-RADS categories 3, 4a, and 4b.

Discrimination of breast tumors in ultrasonic images using an ensemble classifier based on the AdaBoost algorithm with feature selection.

This paper proposes a novel algorithm to estimate a log-compressed K distribution parameter and presents an algorithm to discriminate breast tumors in ultrasonic images. We computed a total of 208 features for discrimination, including those based on a parameter of a log-compressed K-distribution, which quantifies the homogeneity of the echo pattern in the tumor, but is influenced by compression parameters in the ultrasonic device. The proposed algorithm estimates the parameter of the log-compressed K-distribution in a manner free from this influence. To quantify irregularities in tumor shape, pattern-spectrum-based features were newly developed in this paper. The discrimination process uses an ensemble classifier trained by a multiclass AdaBoost learning algorithm (AdaBoost.M2), combined with a sequential feature-selection process. A 10-fold cross-validation test validated the performance, and the results were compared with those of a Mahalanobis distance-based classifier and a multiclass support vector machine. A total of 200 carcinomas, 50 fibroadenomas, and 50 cysts were used in the experiments. This paper demonstrates that the combination of a classifier trained by AdaBoost.M2 and features based on the estimated parameter of a log-compressed K-distribution, as well as those of the pattern spectrum, are useful for the discrimination of tumors.

Evaluación de los quistes complejos de mama / Assessment of the complex breast cysts

Detalles que deben tenerse en cuenta al realizar estudios ecográficos de las lesiones quísticas, especialmente en su visualización y en la interpretación diagnóstica: forma, pared, contenido, tabiques internos, formaciones intraquísticas, y artefactos...

Cistos mamários - qual o seu significado? / Breast cysts - what do they mean?

Os cistos fazem parte de uma variedade de alterações benignas da mama, designadas como mudanças fibrocísticas, e constituem uma das causas mais freqüentes de tumores mamários. O exame clínico, isoladamente, é incapaz de estabelecer o diagnóstico de um cisto mamário. Geralmente é uma lesão assintomática, sendo diagnosticada por métodos de imagem. A acurácia do ultra-som para a identificação de cistos é próxima dos 100 porcento quanto presente massa anecóica oval, redonda ou lobulada, de contorno circunscrito, com reforço acústico posterior. Segundo o sistema BI-RADS para ultra-sonografia, os cistos podem ser divididos em simples, microcistos agrupados, complicados e complexos, sendo classificados em : categoria 2 (benigna) os cistos simples, categoria 3 (provavelmente benigna) para os microcistos agrupados e os cistos complicados e categoria 4 (suspeita) para os cistos complexos. A abordagem terapêutica dos cistos mamários deve ser individualizada de acordo com sua apresentação e o perfil psicológico de cada paciente. Atualmente, não se justificam medidas radicais na abordagem terapêutica destas lesões, visto que sua natureza é eminentemente benigna. É mister que os ginecologistas e mastologistas estejam informados e atualizados para utilizar racionalmente os recursos propedêuticos, otimizando tanto o benefício psíquico e clínico das pacientes como os custos decorrentes de exames e terapêuticas desnecessárias na abordagem dos diferentes tipos de cistos mamários.

Sonographic differentiation of benign and malignant cystic lesions of the breast.

OBJECTIVE: The purpose of this study was to subdivide the types of sonographic findings of benign versus malignant cystic masses and to determine appropriate patient care according to the sonographic findings with pathologic correlation. METHODS: The sonographic findings of 175 symptomatic cystic breast lesions were pathologically proven and reviewed retrospectively. Cystic lesions were classified as 6 types: simple cysts (type I), clustered cysts (type II), cysts with thin septa (type III), complicated cysts (type IV), cystic masses with a thick wall/septa or nodules (type V), and complex solid and cystic masses (type VI). Sonographic findings were compared with the pathologic results and were evaluated according to the incidence of benign and malignant masses. RESULTS: All 23 type I, 15 type II, 22 type III, and 35 type IV cases were pathologically proven to be benign. Seven (25.9%) of the 27 type V cases and 33 (62.3%) of the 53 type VI cases were proven to be malignant. We analyzed the shapes and margins of 80 cases of cystic masses with a solid component (types V and VI); 16 (44%) of 36 sonographically circumscribed masses were malignant. CONCLUSIONS: Because the sonographically detected simple cysts (type I), clustered cysts (type II), and cysts with thin septa (type III) were all benign, annual routine follow-up appears reasonable. Symptomatic complicated cysts (type IV) should be aspirated and appropriately treated according to clinical symptoms. Cystic masses with a solid component (types V and VI) should be examined by biopsy with pathologic confirmation.