The uncertainty of boundary can improve the classification accuracy of BI-RADS 4A ultrasound image.

PURPOSE: The Breast Imaging-Reporting and Data System (BI-RADS) for ultrasound imaging provides a widely used reporting schema for breast imaging. Previous studies have shown that in ultrasound imaging, 90% of BI-RADS 4A tumors are benign lesions after biopsies. Unnecessary biopsy procedures can be avoided by accurate classification of BI-RADS 4A tumors. However, the classification task is challenging and has not been fully investigated by existing studies. For benign and malignant tumors of BI-RADS 4A, the appearances of intra-class tumors are highly variable, the characteristics of inter-class tumors is overall-similar. Discriminative features need to be found to improve classification accuracy of BI-RADS 4A tumors. METHODS: In this study, we designed the network using the clinical features of BI-RADS 4A tumors to improve the discrimination ability of network. The boundary information is embedded into the input of the network using the uncertainty. A fine-grained data augmentation method is used to find discriminative features in tumor information embedded with boundary information. Two mathematical methods, voting-based and variance-based, are used to define the uncertainty of boundary, and the differences of these two definitions are compared in a classification network. RESULTS: The dataset we used to evaluate our method had 1155 2D grayscale images. Each image represented a unique BI-RADS 4A tumor. Among them, 248 tumors were proven to be malignant by biopsy, and the remaining 907 were benign. A weakly supervised data augmentation network (WS-DAN) was used as the backbone classification network, which showed competitive performance in finding discriminative features. Using the auxiliary input of the uncertain boundaries defined by the voting method, the area under the curve (AUC) value of our method was 0.8347 (sensitivity = 0.7774, specificity = 0.7459). The AUC value of the variance-based uncertainty was 0.7789. The voting-based uncertainty was higher than the baseline (AUC = 0.803), which only inputs the original image. Compared with the classic classification network, our method had a significant effect improvement (p < 0.01). CONCLUSIONS: Using the uncertain boundaries defined by the voting methods as auxiliary information, we obtained a better performance in the classification of BI-RADS 4A ultrasound images, while variance-based uncertain boundaries had no effect on improving classification performance. Additionally, fine-grained network helped find discriminative features comparing with the commonly used classification networks.

Intraradiological pathology-calibrated electrical impedance spectroscopy in the evaluation of excision-required breast lesions.

PURPOSE: Evaluating a real-time complementary bioelectrical diagnostic device based on electrical impedance spectroscopy (EIS) for improving breast imaging-reporting and data system (BI-RADS) scoring accuracy, especially in high-risk or borderline breast diseases. The primary purpose is to characterize breast tumors based on their dielectric properties. Early detection of high-risk lesions and increasing the accuracy of tumor sampling and pathological diagnosis are secondary objectives of the study. METHODS: The tumor detection probe (TDP) was first applied to the mouse model for electrical safety evaluations by electrical current measurement. Then it was utilized for characterization of 138 human palpable breast lesions that were to undergo core needle biopsy (CNB), vacuum-assisted biopsy (VAB), or fine needle aspiration (FNA) on the surgeon's requests. Impedance phase slope (IPS) in frequency ranges of 100- 500 kHz and impedance magnitude in f = 1 kHz were extracted as the classification parameters. Consistency of radiological and pathological declarations for the excisional recommendation was then compared with the IPS values. RESULTS: Considering pathological results as the gold standard, meaningful correlations between IPS and pathophysiological status of lesions recommended for excision (such as atypical ductal hyperplasia, papillary lesions, complex sclerosing adenosis, and fibroadenoma) were observed (p < 0.0001). These pathophysiological properties may include cell size, membrane permeability, packing density, adenosis, cytoplasm structure, etc. Benign breast lesions showed IPS values greater than 0, while high-risk proliferative, precancerous, or cancerous lesions had negative IPS values. Statistical analysis showed 95% sensitivity with area under the curve (AUC) equal to 0.92. CONCLUSION: Borderline breast diseases and high-risk lesions that should be excised according to standard guidelines can be diagnosed with TDP before any sampling process. It is an important outcome for high-risk lesions that are radiologically underestimated to BI-RADS3, specifically in younger patients with dense breast masses that present challenges in mammographic and sonographic evaluations. Also, the lowest IPS value detects the most pathologic portions of the tumor for increasing sampling accuracy in large tumors. SIGNIFICANCE: Precise detection of high-risk breast masses, which may be declared BI-RADS3 instead of BI-RADS4a.

An Improved Nomogram to Reduce False-Positive Biopsy Rates of Breast Imaging Reporting and Data System Ultrasonography Category 4A Lesions.

BACKGROUND: The NCCN clinical guidelines recommended core needle biopsy for breast lesions classified as Breast Imaging Reporting and Data System (BI-RADS) 4, while category 4A lesions are only 2-10% likely to be malignant. Thus, a large number of biopsies of BI-RADS 4A lesions were ultimately determined to be benign, and those unnecessary biopsies may incur additional costs and pains. However, it is important to emphasize that the current risk prediction model focuses primarily on the details and complex risk features of US or MG findings, which may be difficult to apply in order to benefit from the model. To stratify and manage BI-RADS 4A lesions effectively and efficiently, a more effective and practical predictive model must be developed. METHODS: We retrospectively analyzed 465 patients with BI-RADS ultrasonography (US) category 4A lesions, diagnosed between January 2019 and July 2019 in Tianjin Medical University Cancer Institute and Hospital and National Clinical Research Center for Cancer. Univariate and multivariate logistic regression analyses were conducted to identify risk factors. To stratify and predict the malignancy of BI-RADS 4A lesions, a nomogram combining the risk factors was constructed based on the multivariate logistic regression results. In order to determine the predictive performance of our predictive model, we used the concordance index (C-index), calibration curve, and receiver operating characteristic (ROC), and the decision curve analysis (DCA) to assess the clinical benefits. RESULTS: Based on our analysis, 16.3% (76 out of 465) of patients were pathologically diagnosed with malignant lesions, while 83.6% (389 out of 465) were diagnosed with benign lesions. According to univariate and multivariate logistic regression analysis, age (OR = 3.414, 95%CI:1.849-6.303), nipple discharge (OR = .326, 95%CI:0.157-.835), palpable lesions (OR = 1.907, 95%CI:1.004-3.621), uncircumscribed margin (US) (OR = 1.732, 95%CI:1.033-2.905), calcification (mammography, MG) (OR = 2.384, 95%CI:1.366-4.161), BI-RADS(MG) (OR = 5.345, 95%CI:2.934-9.736) were incorporated into the predictive nomogram (C-index = .773). There was good agreement between the predicted risk and the observed probability of recurrence. Furthermore, we determined that 153 was the best cutoff score for distinguishing between patients in the low- and high-risk groups. Malignant lesions were significantly more prevalent in high-risk patients than in low-risk patients. CONCLUSION: Based on clinical, US, and MG features, we present a predictive nomogram to reliably predict the malignancy risk of BI-RADS(US) 4A lesions, which may assist clinicians in the selection of patients at low risk of malignancy and reduce the number of false-positive biopsies.

Effect of Quantitative and Semi-quantitative Elastography Methods for the Management of Borderline Lesions on Ultrasonography.

BACKGROUND: Elastography (strain or shear-wave) is a method that estimates tissue stiffness. INTRODUCTION: The aim of this study is to evaluate the quantitative and semi-quantitative ultrasound elastography methods for the diagnosis of BI-RADS 4a and BI-RADS 3 lesions, which are borderline for biopsy and follow-up. MATERIALS AND METHODS: 175 consecutive women with 193 ultrasound-visible breast lesions were classified on Conventional B-mode Ultrasonography (CUS) according to the BI-RADS scoring system. Quantitative and semiquantitative values from ultrasound elastography in the form of strain Elastography Ratio (SER), shear Wave Elastography (SWE) and Shear Wave Elastography Ratio (SWER) were obtained. The lesions categorized as BI-RADS 4a and BI-RADS 3 on ultrasound were subsequently re-categorized according to the elastography values. RESULTS: Except for the 13 BI-RADS 2 lesions, the remaining 180 lesions were biopsied. Pathology showed 83 lesions to be benign and 97 to be malignant. The sensitivity and specificity of the CUS were 96.9% and 75.0%, respectively with an accuracy of 86.0%. Cut-off points calculated based on ROC curves were 56.8 kPa for SWE, 3.53 for SWER and 3.81 for SER. When we downgraded BIRADS 4a lesions based on elastography results, the specificity (CUS+SER 96.9%, CUS+SWE 91.7%, and CUS+SWER 90.6%) and the accuracy (CUS+SER 95.3%, CUS+SWE 92.7%, and CUS+SWER 92.2%) were shown to be better than CUS. When we upgraded BI-RADS 3 lesions based on elastography results, the sensitivity of combined sets of SWE (99,0%) and SWER (100,0%) was better than CUS. CONCLUSION: The rate of false-negative biopsies can be decreased with the combined use of elastography and ultrasonography.

Application of ultrasound artificial intelligence in the differential diagnosis between benign and malignant breast lesions of BI-RADS 4A.

BACKGROUND: The classification of Breast Imaging Reporting and Data System 4A (BI-RADS 4A) lesions is mostly based on the personal experience of doctors and lacks specific and clear classification standards. The development of artificial intelligence (AI) provides a new method for BI-RADS categorisation. We analysed the ultrasonic morphological and texture characteristics of BI-RADS 4A benign and malignant lesions using AI, and these ultrasonic characteristics of BI-RADS 4A benign and malignant lesions were compared to examine the value of AI in the differential diagnosis of BI-RADS 4A benign and malignant lesions. METHODS: A total of 206 lesions of BI-RADS 4A examined using ultrasonography were analysed retrospectively, including 174 benign lesions and 32 malignant lesions. All of the lesions were contoured manually, and the ultrasonic morphological and texture features of the lesions, such as circularity, height-to-width ratio, margin spicules, margin coarseness, margin indistinctness, margin lobulation, energy, entropy, grey mean, internal calcification and angle between the long axis of the lesion and skin, were calculated using grey level gradient co-occurrence matrix analysis. Differences between benign and malignant lesions of BI-RADS 4A were analysed. RESULTS: Significant differences in margin lobulation, entropy, internal calcification and ALS were noted between the benign group and malignant group (P = 0.013, 0.045, 0.045, and 0.002, respectively). The malignant group had more margin lobulations and lower entropy compared with the benign group, and the benign group had more internal calcifications and a greater angle between the long axis of the lesion and skin compared with the malignant group. No significant differences in circularity, height-to-width ratio, margin spicules, margin coarseness, margin indistinctness, energy, and grey mean were noted between benign and malignant lesions. CONCLUSIONS: Compared with the naked eye, AI can reveal more subtle differences between benign and malignant BI-RADS 4A lesions. These results remind us carefully observation of the margin and the internal echo is of great significance. With the help of morphological and texture information provided by AI, doctors can make a more accurate judgment on such atypical benign and malignant lesions.

Frecuencia de cáncer en pacientes con ecografía mamaria BI-RADS 4A / Rate of breast cancer diagnosis in patients categorized as bi-rads 4A on screening ultrasound

Las imágenes son fundamentales en la evaluación de la patología mamaria. El Colegio Americano de Radiología estandarizó los reportes con el Breast Imaging Reporting and Data System (BI-RADS), que permite predecir según las características morfológicas la probabilidad de malignidad y la conducta a seguir. En la cuarta edición de 2003 se amplió para incluir el primer lexicón de ecografía. Los BI-RADS 4 y 5 corresponden a lesiones sospechosas de malignidad y en la 4 fue necesario crear tres subgrupos, de los cuales el 4A son lesiones con posibilidad baja de cáncer (entre 2% y 10%). Como en la primera publicación presentamos los resultados del valor predictivo positivo del reporte BI-RADS 4A mamográfico, el objetivo del presente estudio es determinar la tasa de malignidad en BI-RADS 4A ecográfico en los hospitales de San José e Infantil Universitario de San José de Bogotá DC, Colombia, incluyendo casos de BI-RADS 4A mamográfico para contrastar con el estudio inicial. Cuatro de 72 pacientes con ecografía mamaria BI-RADS 4A fueron diagnosticadas con cáncer de mama (VPP del 5.5%), todos en nódulos sólidos. Algunos pudieron catalogarse como BI-RADS 3 por los radiólogos...

Imaging is essential for breast pathology evaluation. The American College of Radiology provides a standardized classification system the Breast Imaging-Reporting and Data System (BI-RADS), allowing prediction of malignancy probability based on appearance and providing guidelines to be followed. The 2003 fourth edition was extended to include the first ultrasound lexicon. BI-RADS 4 and 5 categories are lesions with suspicious changes of malignancy, and category 4 was divided into three sub groups, 4A lesions have low possibility of cancer (between 2% and 10%). As our first publication presented the results of the positive predictive value of BI-RADS 4A mammographic reports, the objective of the present study is to determine the malignancy rate in BI-RADS 4A ultrasound reports at San José and Infantil Universitario de San José hospitals in Bogotá DC, Colombia, including BI-RADS 4A mammographic exam cases to compare with the initial study. Of the 72 BI-RADS 4a lesions on ultrasound screening, four were malignant (VPP 5.5%) all detected as solid nodules. Some could have been categorized as BI-RADS 3 by radiologists...

Caracterización de lesiones asociadas a microcalcificaciones BI-RADS 4A, en 11 años de biopsias estereotáxicas / Characterization of lesions associated with microcalcifications BI-RADS 4A over a 11-year period of stereotactic breast biopsies

Substantial advances in breast imaging techniques, especially developments in digital mammography, have led to early detection of breast cancer. It is well-known that microcalcifications are present in approximately 55 percent of nonpalpable breast malignancies and are responsible for the detection of 85-90 percent of cases of ductal carcinoma in situ (DCIS) through mammographic screening. We evaluated the types of associated lesions and the percentage of malignancy in BI-RADS 4A subcategory (low suspicion of malignancy), by performing a database review of stereotactic biopsies of microcalcifications categorized as BI-RADS 4A, between September 1999 and January 2011, which accounted for 21.4 percent of biopsied microcalcifications in a total of 159 women. Histological findings corresponded to benign lesions in 43.5 percent, high-risk lesions in 46.5 percent, and malignant tumors in 10 percent. Concerning the latter (16 biopsies), 81.3 percent were DCIS and 18.7 percent corresponded to infiltrating ductal carcinoma (IDC). The PPV of BI-RADS 4 A category was 13 percent, a value consistent with that described in the literature. Microcalcifications BI-RADS 4A exhibit low suspicion of malignancy, since they mostly correspond to benign lesions (90 percent). Subcategory 4A constitutes an important ancillary diagnostic tool for a more accurate assessment of lesions suspicious for malignancy; therefore, we strongly recommend its use.

El continuo avance en las técnicas de imágenes mamarias, especialmente el desarrollo de la mamografía digital, ha permitido detectar cáncer mamario en etapa precoz. Se sabe que las microcalcificaciones están presentes en el 55 por ciento de los cánceres no palpables y corresponden al 85-90 por ciento de los carcinomas ductales in situ (CDIS) que se detectan con mamografía de screening. Hemos evaluado el tipo de lesiones asociadas y el porcentaje de malignidad de la subcategoría BI-RADS 4A (baja sospecha de malignidad), realizando una revisión de la base de datos de las biopsias estereotáxicas por microcalcificaciones categorizadas BI-RADS 4A entre septiembre 1999 y enero 2011 y que alcanzaron al 21,4 por ciento del total de las microcalcificaciones biopsiadas, en un total de 159 mujeres. Los resultados histológicos correspondieron a lesiones benignas en el 43,5 por ciento, lesiones de alto riesgo en el 46,5 por ciento y malignas en 10 por ciento. De las lesiones malignas (16 biopsias), el 81,3 por ciento fue CDIS y el 18,7 por ciento carcinoma ductal infiltrante (CDI). El VPP de la categoría BI-RADS 4 A fue de 13 por ciento, concordante con la literatura. Las microcalcificaciones BI-RADS 4A son de baja sospecha de malignidad, correspondiendo en su gran mayoría (90 por ciento) a lesiones benignas. La subdivisión en 4 A representa una herramienta que facilita un mejor manejo clínico de las pacientes, por lo que recomendamos su utilización.