Posterior breast cancer: Mammographic and ultrasonographic features

Background/Aim. Posterior breast cancers are located in the prepectoral region of the breast. Owing to this distinctive anatomical localization, physical examination and mammographic or ultrasonographic evaluation can be difficult. The purpose of the study was to assess possibilities of diagnostic mammography and breast ultrasonography in detection and differentiation of posterior breast cancers. Methods. The study included 40 women with palpable, histopathological confirmed posterior breast cancer. Mammographic and ultrasonographic features were defined according to Breast Imaging Reporting and Data System (BI-RADS) lexicon. Results. Based on standard two-view mammography 87.5%, of the cases were classified as BI-RADS 4 and 5 categories, while after additional mammographic views all the cases were defined as BIRADS 4 and 5 categories. Among 96 mammographic descriptors, the most frequent were: spiculated mass (24.0%), architectural distortion (16.7%), clustered microcalcifications (12.6%) and focal asymmetric density (12.6%). The differentiation of the spiculated mass was significantly associated with the possibility to visualize the lesion at two-view mammography (p = 0.009), without the association with lesion diameter (p = 0.083) or histopathological type (p = 0.055). Mammographic signs of invasive lobular carcinoma were significantly different from other histopathological types (architectural distortion, p = 0.003; focal asymmetric density, p = 0.019; association of four or five subtle signs of malignancy, p = 0.006). All cancers were detectable by ultrasonography. Mass lesions were found in 82.0% of the cases. Among 153 ultrasonographic descriptors, the most frequent were: irregular mass (15.7%), lobulated mass (7.2%), abnormal color Doppler signals (20.3%), posterior acoustic attenuation (18.3%). Ultrasonographic BI-RADS 4 and 5 categories were defined in 72.5% of the cases, without a significant difference among various histopathological types (p = 0.109). Conclusion. Standard two-view mammography followed by additional mammographic projections is an effective way to demonstrate the spiculated mass and to classify the prepectoral lesion as category BI-RADS 4 or 5. Additional ultrasonography can overcome the mimicry of invasive lobular breast carcinoma at mammography.

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Automatic detection of various patterns of calcification distribution on mammograms: a graph convolution network approach (Preprint)

10.2196/preprints.32188 ◽

2021 ◽

Author(s):

Melissa Min-Szu Yao ◽

Hao Du ◽

Mikael Hartman ◽

Wing P. Chan ◽

Mengling Feng

Keyword(s):

Breast Cancer ◽

Breast Imaging ◽

Characteristic Curve ◽

Area Under The Curve ◽

Automatic Detection ◽

Ground Truth ◽

Architectural Distortion ◽

Breast Cancers ◽

Convolutional Network ◽

Mammographic Images

UNSTRUCTURED Purpose: To develop a novel artificial intelligence (AI) model algorithm focusing on automatic detection and classification of various patterns of calcification distribution in mammographic images using a unique graph convolution approach. Materials and methods: Images from 200 patients classified as Category 4 or 5 according to the American College of Radiology Breast Imaging Reporting and Database System, which showed calcifications according to the mammographic reports and diagnosed breast cancers. The calcification distributions were classified as either diffuse, segmental, regional, grouped, or linear. Excluded were mammograms with (1) breast cancer as a single or combined characterization such as a mass, asymmetry, or architectural distortion with or without calcifications; (2) hidden calcifications that were difficult to mark; or (3) incomplete medical records. Results: A graph convolutional network-based model was developed. 401 mammographic images from 200 cases of breast cancer were divided based on calcification distribution pattern: diffuse (n = 24), regional (n = 111), group (n = 201), linear (n = 8) or segmental (n = 57). The classification performances were measured using metrics including precision, recall, F1 score, accuracy and multi-class area under receiver operating characteristic curve. The proposed achieved precision of 0.483 ± 0.015, sensitivity of 0.606 (0.030), specificity of 0.862 ± 0.018, F1 score of 0.527 ± 0.035, accuracy of 60.642% ± 3.040% and area under the curve of 0.754 ± 0.019, finding method to be superior compared to all baseline models. The predicted linear and diffuse classifications were highly similar to the ground truth, and the predicted grouped and regional classifications were also superior compared to baseline models. Conclusion: The proposed deep neural network framework is an AI solution to automatically detect and classify calcification distribution patterns on mammographic images highly suspected of showing breast cancers. Further study of the AI model in an actual clinical setting and additional data collection will improve its performance.

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The Adjunctive Digital Breast Tomosynthesis in Diagnosis of Breast Cancer

BioMed Research International ◽

10.1155/2013/597253 ◽

2013 ◽

Vol 2013 ◽

pp. 1-7 ◽

Cited By ~ 24

Author(s):

Tsung-Lung Yang ◽

Huei-Lung Liang ◽

Chen-Pin Chou ◽

Jer-Shyung Huang ◽

Huay-Ben Pan

Keyword(s):

Breast Cancer ◽

Diagnostic Performance ◽

Digital Breast Tomosynthesis ◽

Subjective Assessment ◽

Architectural Distortion ◽

Breast Cancers ◽

Chi Square ◽

Breast Tomosynthesis ◽

Chi Square Test ◽

Mass Lesions

Purpose. To compare the diagnostic performance of digital breast tomosynthesis (DBT) and digital mammography (DM) for breast cancers.Materials and Methods. Fifty-seven female patients with pathologically proved breast cancer were enrolled. Three readers gave a subjective assessment superiority of the index lesions (mass, focal asymmetry, architectural distortion, or calcifications) and a forced BIRADS score, based on DM reading alone and with additional DBT information. The relevance between BIRADS category and index lesions of breast cancer was compared by chi-square test.Result. A total of 59 breast cancers were reviewed, including 17 (28.8%) mass lesions, 12 (20.3%) focal asymmetry/density, 6 (10.2%) architecture distortion, 23 (39.0%) calcifications, and 1 (1.7%) intracystic tumor. Combo DBT was perceived to be more informative in 58.8% mass lesions, 83.3% density, 94.4% architecture distortion, and only 11.6% calcifications. As to the forced BIRADS score, 84.4% BIRADS 0 on DM was upgraded to BIRADS 4 or 5 on DBT, whereas only 27.3% BIRADS 4A on DM was upgraded on DBT, as BIRADS 4A lesions were mostly calcifications. A significantPvalue (<0.001) between the BIRADS category and index lesions was noted.Conclusion. Adjunctive DBT gives exquisite information for mass lesion, focal asymmetry, and/or architecture distortion to improve the diagnostic performance in mammography.

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O29 Aberrant expression of BMP8A and the BMPRs involves in the progression of breast cancer

British Journal of Surgery ◽

10.1093/bjs/znab282.034 ◽

2021 ◽

Vol 108 (Supplement_5) ◽

Author(s):

L J Sui ◽

A Sanders ◽

W G Jiang ◽

L Ye

Keyword(s):

Breast Cancer ◽

Poor Prognosis ◽

Gene Array ◽

Breast Cancers ◽

Sequencing Data ◽

Aberrant Expression ◽

Bmp Receptors ◽

Significant Difference ◽

Highly Correlated ◽

Spearman Test

Abstract Introduction Role of Bone morphogenetic protein 8A (BMP8A) and BMP receptors (BMPRs) in the tumourigenesis and progression of breast cancer remains elusive. Present study aims to investigate the expression of BMP8A and related BMPRs in breast cancer and their clinical implication. Method Expression of BMP8A and BMPRs was analysed using the RNA sequencing data of the TCGA breast cancer cohort. Findings were further validated in a meta gene array dataset (E-MDTA6703, n = 2302). STRING dataset was applied to explore the predicted receptors of BMP8A. Clinical relevance of deregulated BMP8A and BMPRs in breast cancer was assessed using both ANOVA and Kaplan-Meier tests. Correlation with markers of proliferation and invasion was evaluated using Spearman test. Result Analysis of datasets revealed that BMP8A and BMPR1B were highly expressed in breast cancer while ACVRL1, ACVR1, BMPR1A, ACVR1C, TGFBR2, TGFBR3, BMPR2 and ACVR2A were lower-expressed compared with normal controls. Expressions of BMPR1B, BMPR1A, BMPR2, ACVR2A and ACVR2B were highly correlated with BMP8A in the breast cancers. Overall survival in the group with higher BMP8A expression was shorter(median= 122.3 months), P = 0.012 compared with lower-expressed group(median = 215.2 months). No significant difference was observed in BMP8A and BMPRs in tumours according to their staging and lymph node involvement. Positive correlations were found between BMP8A and tumour proliferation, EMT, angiogenic markers. Conclusion BMP8A is increased in breast cancer and correlates with poor prognosis. The highly correlated BMPRs might be involved in the signal transduction of BMP8A to co-regulate BMP responsive genes and cellular functions which is yet to be investigated. Take-home Message BMP8A is increased in breast cancer and correlates with poor prognosis.

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Breast Cancer Survival in the Mammography Era in Brazil: A Population-Based Analysis of 2,715 Cases

10.21203/rs.3.rs-625485/v1 ◽

2021 ◽

Author(s):

Juliana Fernandes ◽

Beatriz Machado ◽

Cassio Cardoso-Filho ◽

Juliana Nativio ◽

Cesar Cabello ◽

...

Keyword(s):

Breast Cancer ◽

Cancer Survival ◽

Survival Rates ◽

Population Based ◽

Breast Cancer Survival ◽

Stage I ◽

Current Status ◽

Breast Cancers ◽

Risk Of Death ◽

Significant Difference

Abstract Background This study aims to assess breast cancer survival rates after one decade of mammography in a large urban area of Brazil. Methods It is a population-based retrospective cohort of women with breast cancer in Campinas, São Paulo, from 2010 to 2014. Age, vital status and stage were accessed through the cancer and mortality registry, and patients records. Statistics used Kaplan-Meier, log-rank and Cox's regression. Results Out of the 2,715 cases, 665 deaths (24.5%) were confirmed until early 2020. The mean age at diagnosis was 58.6 years. Women 50-69 years were 48.0%, and stage I the most frequent (25.0%). The overall mean survival was 8.4 years (8.2-8.5). The 5-year survival (5yOS) for overall, 40-49, 50-59, 60-69, 70-79 years was respectively 80.5%, 87.7%, 83.7%, 83.8% and 75.5%. The 5yOS for stages 0, I, II, III and IV was 95.2%, 92.6%, 89.4%, 71.1% and 47.1%. There was no significant difference in survival in stage I or II (p=0.058). Compared to women 50-59 years, death's risk was 2.3 times higher for women 70-79 years and 26% lower for women 40-49 years. Concerning stage I, the risk of death was 1.5, 4.1 and 8.6 times higher, and 34% lower, respectively, for stage II, III, IV and 0. Conclusions In Brazil, breast cancers are currently diagnosed in the early stages, although advanced cases persist. Survival rates may reflect improvements in screening, early detection and treatment. The results can reflect the current status of other regions or countries with similar health care conditions.

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Breast Imaging During Pregnancy and Lactation

Journal of Breast Imaging ◽

10.1093/jbi/wbz065 ◽

2019 ◽

Vol 1 (4) ◽

pp. 342-351

Author(s):

Lisa Abramson ◽

Lindsey Massaro ◽

J Jaime Alberty-Oller ◽

Amy Melsaether

Keyword(s):

Breast Cancer ◽

Breast Imaging ◽

Treatment Options ◽

Nipple Discharge ◽

Cancer Staging ◽

Imaging Features ◽

Breast Cancers ◽

Bloody Nipple Discharge ◽

Breast Cancer Staging ◽

Pregnancy And Lactation

Abstract Breast imaging during pregnancy and lactation is important in order to avoid delays in the diagnosis and treatment of pregnancy-associated breast cancers. Radiologists have an opportunity to improve breast cancer detection by becoming familiar with appropriate breast imaging and providing recommendations to women and their referring physicians. Importantly, during pregnancy and lactation, both screening and diagnostic breast imaging can be safely performed. Here we describe when and how to screen, how to work up palpable masses, and evaluate bloody nipple discharge. The imaging features of common findings in the breasts of pregnant and lactating women are also reviewed. Finally, we address breast cancer staging and provide a brief primer on treatment options for pregnancy-associated breast cancers.

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Report of clinico-pathological features of breast cancer in HIV-infected and uninfected women in Botswana

Infectious Agents and Cancer ◽

10.1186/s13027-019-0245-6 ◽

2019 ◽

Vol 14 (1) ◽

Cited By ~ 1

Author(s):

Rohini K. Bhatia ◽

Mohan Narasimhamurthy ◽

Yehoda M. Martei ◽

Pooja Prabhakar ◽

Jeré Hutson ◽

...

Keyword(s):

Breast Cancer ◽

Cancer Patients ◽

Stage Iii ◽

Disease Stage ◽

Breast Cancers ◽

Pathological Features ◽

Breast Cancer Patients ◽

Hiv Status ◽

Receptor Status ◽

Significant Difference

Abstract Background To characterize the clinico-pathological features including estrogen receptor (ER), progesterone receptor (PR) and Her-2/neu (HER2) expression in breast cancers in Botswana, and to compare them by HIV status. Methods This was a retrospective study using data from the National Health Laboratory and Diagnofirm Medical Laboratory in Gaborone from January 1, 2011 to December 31, 2015. Clinico-pathological details of patients were abstracted from electronic medical records. Results A total of 384 unique breast cancer reports met our inclusion criteria. Of the patients with known HIV status, 42.7% (50/117) were HIV-infected. Median age at the time of breast cancer diagnosis was 54 years (IQR 44–66 years). HIV-infected individuals were more likely to be diagnosed before age 50 years compared to HIV-uninfected individuals (68.2% vs 23.8%, p < 0.001). The majority of patients (68.6%, 35/51) presented with stage III at diagnosis. Stage IV disease was not presented because of the lack of data in pathology records surveyed, and additionally these patients may not present to clinic if the disease is advanced. Overall, 68.9% (151/219) of tumors were ER+ or PR+ and 16.0% (35/219) were HER2+. ER+ or PR+ or both, and HER2- was the most prevalent profile (62.6%, 132/211), followed by triple negative (ER−/PR−/HER2-, 21.3%, 45/211), ER+ or PR+ or both, and HER2+, (9.0%, 19/211) and ER−/PR−/HER2+ (7.1%, 15/211). There was no significant difference in receptor status noted between HIV-infected and HIV-uninfected individuals. Conclusions Majority of breast cancer patients in Botswana present with advanced disease (stage III) at diagnosis and hormone receptor positive disease. HIV-infected breast cancer patients tended to present at a younger age compared to HIV-uninfected patients. HIV status does not appear to be associated with the distribution of receptor status in breast cancers in Botswana.

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Radiological review of prior screening mammograms of screen-detected breast cancer

European Radiology ◽

10.1007/s00330-020-07130-y ◽

2020 ◽

Cited By ~ 1

Author(s):

Tone Hovda ◽

Kaitlyn Tsuruda ◽

Solveig Roth Hoff ◽

Kristine Kleivi Sahlberg ◽

Solveig Hofvind

Keyword(s):

Breast Cancer ◽

Mammographic Density ◽

Breast Imaging ◽

Screening Program ◽

Tumor Characteristics ◽

Breast Cancers ◽

Mammographic Finding ◽

Estrogen Receptor Positivity ◽

Screening Mammograms ◽

Screening And Diagnosis

Abstract Objective To perform a radiological review of mammograms from prior screening and diagnosis of screen-detected breast cancer in BreastScreen Norway, a population-based screening program. Methods We performed a consensus-based informed review of mammograms from prior screening and diagnosis for screen-detected breast cancers. Mammographic density and findings on screening and diagnostic mammograms were classified according to the Breast Imaging-Reporting and Data System®. Cases were classified based on visible findings on prior screening mammograms as true (no findings), missed (obvious findings), minimal signs (minor/non-specific findings), or occult (no findings at diagnosis). Histopathologic tumor characteristics were extracted from the Cancer Registry of Norway. The Bonferroni correction was used to adjust for multiple testing; p < 0.001 was considered statistically significant. Results The study included mammograms for 1225 women with screen-detected breast cancer. Mean age was 62 years ± 5 (SD); 46% (567/1225) were classified as true, 22% (266/1225) as missed, and 32% (392/1225) as minimal signs. No difference in mammographic density was observed between the classification categories. At diagnosis, 59% (336/567) of true and 70% (185/266) of missed cancers were classified as masses (p = 0.004). The percentage of histological grade 3 cancers was higher for true (30% (138/469)) than for missed (14% (33/234)) cancers (p < 0.001). Estrogen receptor positivity was observed in 86% (387/469) of true and 95% (215/234) of missed (p < 0.001) cancers. Conclusions We classified 22% of the screen-detected cancers as missed based on a review of prior screening mammograms with diagnostic images available. One main goal of the study was quality improvement of radiologists’ performance and the program. Visible findings on prior screening mammograms were not necessarily indicative of screening failure. Key Points • After a consensus-based informed review, 46% of screen-detected breast cancers were classified as true, 22% as missed, and 32% as minimal signs. • Less favorable prognostic and predictive tumor characteristics were observed in true screen-detected breast cancer compared with missed. • The most frequent mammographic finding for all classification categories at the time of diagnosis was mass, while the most frequent mammographic finding on prior screening mammograms was a mass for missed cancers and asymmetry for minimal signs.

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Color Doppler Ultrasound Improves Machine Learning Diagnosis of Breast Cancer

Diagnostics ◽

10.3390/diagnostics10090631 ◽

2020 ◽

Vol 10 (9) ◽

pp. 631

Author(s):

Afaf F. Moustafa ◽

Theodore W. Cary ◽

Laith R. Sultan ◽

Susan M. Schultz ◽

Emily F. Conant ◽

...

Keyword(s):

Breast Cancer ◽

Machine Learning ◽

Breast Imaging ◽

Color Doppler ◽

Color Doppler Ultrasound ◽

Ensemble Classifier ◽

Diagnostic Model ◽

Ultrasound Images ◽

Breast Masses ◽

Roc Area

Color Doppler is used in the clinic for visually assessing the vascularity of breast masses on ultrasound, to aid in determining the likelihood of malignancy. In this study, quantitative color Doppler radiomics features were algorithmically extracted from breast sonograms for machine learning, producing a diagnostic model for breast cancer with higher performance than models based on grayscale and clinical category from the Breast Imaging Reporting and Data System for ultrasound (BI-RADSUS). Ultrasound images of 159 solid masses were analyzed. Algorithms extracted nine grayscale features and two color Doppler features. These features, along with patient age and BI-RADSUS category, were used to train an AdaBoost ensemble classifier. Though training on computer-extracted grayscale features and color Doppler features each significantly increased performance over that of models trained on clinical features, as measured by the area under the receiver operating characteristic (ROC) curve, training on both color Doppler and grayscale further increased the ROC area, from 0.925 ± 0.022 to 0.958 ± 0.013. Pruning low-confidence cases at 20% improved this to 0.986 ± 0.007 with 100% sensitivity, whereas 64% of the cases had to be pruned to reach this performance without color Doppler. Fewer borderline diagnoses and higher ROC performance were both achieved for diagnostic models of breast cancer on ultrasound by machine learning on color Doppler features.

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Gene Expression Profile Analysis of T1 and T2 Breast Cancer Reveals Different Activation Pathways

ISRN Oncology ◽

10.1155/2013/924971 ◽

2013 ◽

Vol 2013 ◽

pp. 1-12 ◽

Cited By ~ 2

Author(s):

Margit L. H. Riis ◽

Xi Zhao ◽

Fateme Kaveh ◽

Hilde S. Vollan ◽

Anne-Jorunn Nesbakken ◽

...

Keyword(s):

Breast Cancer ◽

Tumor Size ◽

Profile Analysis ◽

Distant Metastases ◽

Molecular Signature ◽

Receptor Interaction ◽

Lymph Node Status ◽

Breast Cancers ◽

Significant Difference ◽

T1 And T2

Breast cancers today are of predominantly T1 (0.1≥2.0 cm) or T2 (>2≤5 cm) categories due to early diagnosis. Molecular profiling using microarrays has led to the notion of breast cancer as a heterogeneous disease both clinically and molecularly. Given the prognostic power and clinical use of tumor size, the purpose of this study was to search for molecular signatures characterizing clinical T1 and T2. In total 46 samples were included in the discovery dataset. After adjusting for hormone receptor status, lymph node status, grade, and tumor subclass 441 genes were differently expressed between T1 and T2 tumors. Focal adhesion and extracellular matrix receptor interaction were upregulated in the smaller tumors while p38MAPK signaling and immune-related pathways were more dominant in the larger tumors. The T-size signature was then tested on a validation set of 947 breast tumor samples. Using the T-size expression signatures instead of tumor size leads to a significant difference in risk for distant metastases (P<0.001). If further confirmed, this molecular signature can be used to select patients with tumor category T1 who may need more aggressive treatment and patients with tumor category T2 who may have less benefit from it.

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Triple-negative invasive breast cancer (TNBC): Mammographic, US, and MR imaging features according to androgen receptor (AR) expression.

Journal of Clinical Oncology ◽

10.1200/jco.2014.32.26_suppl.159 ◽

2014 ◽

Vol 32 (26_suppl) ◽

pp. 159-159

Author(s):

Woo Kyung Moon

Keyword(s):

Breast Cancer ◽

Mr Imaging ◽

Breast Imaging ◽

Gene Signature ◽

Irregular Shape ◽

Consecutive Series ◽

Imaging Features ◽

Breast Cancers ◽

Pathologic Features ◽

Chi Squared

159 Background: A subset of TNBC is characterized by an androgen gene signature and early clinical trials have demonstrated clinical benefit with the use of the AR antagonist, bicalutamide, for the treatment of patients with AR+, estrogen receptor/progesterone receptor- breast cancer. Methods: AR expression was assessed immunohistochemically in 125 patients (median age; 54 years, range; 26-82 years) with TNBC from a consecutive series of 1,086 operable invasive breast cancers. Two experienced breast imaging radiologists (6 and 24 years of experience, respectively) reviewed the mammograms, US, and MR images without knowledge of clinicopathologic findings. The imaging and pathologic features of 33 AR-positive TNBCs were compared with those of 92 AR-negative TNBCs by using the Fisher’s exact or chi-squared tests. Results: AR expression in TNBC is significantly associated with mammographic findings (P < 0.001), lesion type at MR imaging (P < 0.001), and mass shape or margin at ultrasound (P < 0.001; P= 0.002). The highest PPVs for AR-positive cancer were non-mass enhancement on MR imaging (PPV, 1.00; 95% CI: 0.61, 1.00), calcifications only seen on mammography (PPV, 1.00; 95% CI: 0.37, 1.00), and spiculated masses on US (PPV, 1.00; 95% CI: 0.22, 1.00). Conclusions: AR-positive and AR-negative tumors have distinct imaging features in TNBC. The presence of calcifications or focal asymmetries at mammography, the presence of echogenic halo or non-complex hypoechoic masses at US, masses with irregular shape or indistinct margins at mammography and US, and masses with irregular shape or spiculated margins, or non-mass lesions at MR imaging were associated with AR expression in TNBC. These imaging features may be used to predict AR status, which could assist in treatment planning, prediction of response, and assessment of prognosis for patients with TNBC.

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