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

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.

scholarly journals Breast Imaging During Pregnancy and Lactation

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.

scholarly journals Review of Metaplastic Carcinoma of the Breast: Imaging Findings and Pathologic Features

2012 ◽  
Vol 2 ◽  
pp. 21 ◽  
Author(s):  
Rebecca Leddy ◽  
Abid Irshad ◽  
Tihana Rumboldt ◽  
Abbie Cluver ◽  
Amy Campbell ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Invasive Ductal Carcinoma ◽  
Breast Imaging ◽  
Ductal Carcinoma ◽  
Metaplastic Carcinoma ◽  
Imaging Features ◽  
Benign Lesions ◽  
Imaging Characteristics ◽  
Pathology Classification ◽  
Pathologic Features

Metaplastic carcinoma (MPC), an uncommon but often aggressive breast cancer, can be challenging to differentiate from other types of breast cancer and even benign lesions based on the imaging appearance. It has a variable pathology classification system. These types of tumors are generally rapidly growing palpable masses. MPCs on imaging can present with imaging features similar to invasive ductal carcinoma and probably even benign lesions. The purpose of this article is to review MPC of the breast including the pathology subtypes, imaging features, and imaging pathology correlations. By understanding the clinical picture, pathology, and overlap in imaging characteristics of MPC with invasive ductal carcinoma and probably benign lesions can assist in diagnosing these difficult malignancies.

scholarly journals Evaluation of multiple transcriptomic gene risk signatures in male breast cancer

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Jane Bayani ◽  
Coralie Poncet ◽  
Cheryl Crozier ◽  
Anouk Neven ◽  
Tammy Piper ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Male Breast Cancer ◽  
Univariate Analysis ◽  
Gene Signature ◽  
Male Breast ◽  
Risk Scores ◽  
Breast Cancers ◽  
Academic Risk ◽  
Genomic Grade Index ◽  
Prognostic Utility

AbstractMale breast cancer (BCa) is a rare disease accounting for less than 1% of all breast cancers and 1% of all cancers in males. The clinical management is largely extrapolated from female BCa. Several multigene assays are increasingly used to guide clinical treatment decisions in female BCa, however, there are limited data on the utility of these tests in male BCa. Here we present the gene expression results of 381 M0, ER+ve, HER2-ve male BCa patients enrolled in the Part 1 (retrospective analysis) of the International Male Breast Cancer Program. Using a custom NanoString™ panel comprised of the genes from the commercial risk tests Prosigna®, OncotypeDX®, and MammaPrint®, risk scores and intrinsic subtyping data were generated to recapitulate the commercial tests as described by us previously. We also examined the prognostic value of other risk scores such as the Genomic Grade Index (GGI), IHC4-mRNA and our prognostic 95-gene signature. In this sample set of male BCa, we demonstrated prognostic utility on univariate analysis. Across all signatures, patients whose samples were identified as low-risk experienced better outcomes than intermediate-risk, with those classed as high risk experiencing the poorest outcomes. As seen with female BCa, the concordance between tests was poor, with C-index values ranging from 40.3% to 78.2% and Kappa values ranging from 0.17 to 0.58. To our knowledge, this is the largest study of male breast cancers assayed to generate risk scores of the current commercial and academic risk tests demonstrating comparable clinical utility to female BCa.

scholarly journals Cancer Grade Model: a multi-gene machine learning-based risk classification for improving prognosis in breast cancer

2021 ◽  
Author(s):  
E. Amiri Souri ◽  
A. Chenoweth ◽  
A. Cheung ◽  
S. N. Karagiannis ◽  
S. Tsoka
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Clinical Decision Making ◽  
Histological Grade ◽  
Tumour Size ◽  
Clinical Decision ◽  
Gene Signature ◽  
Risk Classification ◽  
Breast Cancers ◽  
Grade 3

Abstract Background Prognostic stratification of breast cancers remains a challenge to improve clinical decision making. We employ machine learning on breast cancer transcriptomics from multiple studies to link the expression of specific genes to histological grade and classify tumours into a more or less aggressive prognostic type. Materials and methods Microarray data of 5031 untreated breast tumours spanning 33 published datasets and corresponding clinical data were integrated. A machine learning model based on gradient boosted trees was trained on histological grade-1 and grade-3 samples. The resulting predictive model (Cancer Grade Model, CGM) was applied on samples of grade-2 and unknown-grade (3029) for prognostic risk classification. Results A 70-gene signature for assessing clinical risk was identified and was shown to be 90% accurate when tested on known histological-grade samples. The predictive framework was validated through survival analysis and showed robust prognostic performance. CGM was cross-referenced with existing genomic tests and demonstrated the competitive predictive power of tumour risk. Conclusions CGM is able to classify tumours into better-defined prognostic categories without employing information on tumour size, stage, or subgroups. The model offers means to improve prognosis and support the clinical decision and precision treatments, thereby potentially contributing to preventing underdiagnosis of high-risk tumours and minimising over-treatment of low-risk disease.

Subtle signs of breast cancer as an important pitfall in mammographic interpretation. Establishing an accurate diagnosis in an equivocal case

2021 ◽  
pp. 1-6
Author(s):  
Nikolaos S. Salemis ◽  
Eleni Mourtzoukou ◽  
Michail Angelopoulos
Keyword(s):  
Breast Cancer ◽  
Imaging Modality ◽  
Benign Lesion ◽  
Imaging Features ◽  
Breast Cancers ◽  
Breast Clinic ◽  
Delay In Diagnosis ◽  
Delay Of Treatment ◽  
Equivocal Case ◽  
Related Mortality

Mammogram is the standard imaging modality for the early detection of breast cancer, and it has been shown to reduce disease-related mortality by up to 30%. Mammogram, however, has its limitations. It is reported that 10–30% of breast cancers may be missed on a mammogram. Delay in the diagnosis and treatment may adversely affect the prognosis of patients with breast cancer. We present a case of multifocal invasive early breast carcinoma, which was misinterpreted twice as intramammary lymph nodes, thus resulting in a delay in diagnosis for eighteen months. The tumors were detected incidentally after the patient presented to our Breast clinic for symptoms related to a concomitant benign lesion involving the same breast. We describe the tumors’ imaging features and discuss the possible reasons that likely led to repeated misinterpretation. Awareness of possible causes for missed breast cancer is necessary to avoid delay of treatment initiation that may adversely affect prognosis.

Distinct MR Imaging Features of Triple-Negative Breast Cancer with Brain Metastasis

2014 ◽  
Vol 25 (3) ◽  
pp. 474-481 ◽  
Author(s):  
Ren-Hua Yeh ◽  
Jyh-Cherng Yu ◽  
Chi-Hong Chu ◽  
Ching-Liang Ho ◽  
Hung-Wen Kao ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Brain Metastasis ◽  
Mr Imaging ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Imaging Features

MR imaging features and tumor biomarkers of screen-detected and non-screen detected breast cancers: preliminary results of a comparative study

2018 ◽  
Vol 52 ◽  
pp. 350-355
Author(s):  
Evangelia Panourgias ◽  
Charis Bourgioti ◽  
Andreas Koureas ◽  
Vassilis Koutoulidis ◽  
Georgios Metaxas ◽  
...  
Keyword(s):  
Mr Imaging ◽  
Comparative Study ◽  
Tumor Biomarkers ◽  
Imaging Features ◽  
Breast Cancers ◽  
Preliminary Results

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

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.

scholarly journals Validation of miRNAs as Breast Cancer Biomarkers with a Machine Learning Approach

Cancers ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 431 ◽  
Author(s):  
Oneeb Rehman ◽  
Hanqi Zhuang ◽  
Ali Muhamed Ali ◽  
Ali Ibrahim ◽  
Zhongwei Li
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Information Gain ◽  
Support Vector ◽  
Learning Approach ◽  
Breast Cancers ◽  
Functional Studies ◽  
Normal Tissues ◽  
Machine Learning Approach ◽  
Chi Squared

Certain small noncoding microRNAs (miRNAs) are differentially expressed in normal tissues and cancers, which makes them great candidates for biomarkers for cancer. Previously, a selected subset of miRNAs has been experimentally verified to be linked to breast cancer. In this paper, we validated the importance of these miRNAs using a machine learning approach on miRNA expression data. We performed feature selection, using Information Gain (IG), Chi-Squared (CHI2) and Least Absolute Shrinkage and Selection Operation (LASSO), on the set of these relevant miRNAs to rank them by importance. We then performed cancer classification using these miRNAs as features using Random Forest (RF) and Support Vector Machine (SVM) classifiers. Our results demonstrated that the miRNAs ranked higher by our analysis had higher classifier performance. Performance becomes lower as the rank of the miRNA decreases, confirming that these miRNAs had different degrees of importance as biomarkers. Furthermore, we discovered that using a minimum of three miRNAs as biomarkers for breast cancers can be as effective as using the entire set of 1800 miRNAs. This work suggests that machine learning is a useful tool for functional studies of miRNAs for cancer detection and diagnosis.

scholarly journals Defining the emergence of myeloid-derived suppressor cells in breast cancer using single-cell transcriptomics

2020 ◽  
Vol 5 (44) ◽  
pp. eaay6017 ◽  
Author(s):  
Hamad Alshetaiwi ◽  
Nicholas Pervolarakis ◽  
Laura Lynn McIntyre ◽  
Dennis Ma ◽  
Quy Nguyen ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Single Cell ◽  
Suppressor Cells ◽  
Gene Signature ◽  
Specific Gene ◽  
Myeloid Derived Suppressor Cells ◽  
Breast Cancers ◽  
Molecular Features ◽  
Immunosuppressive Cell ◽  
Tumor Virus

Myeloid-derived suppressor cells (MDSCs) are innate immune cells that acquire the capacity to suppress adaptive immune responses during cancer. It remains elusive how MDSCs differ from their normal myeloid counterparts, which limits our ability to specifically detect and therapeutically target MDSCs during cancer. Here, we sought to determine the molecular features of breast cancer–associated MDSCs using the widely studied mouse model based on the mouse mammary tumor virus (MMTV) promoter–driven expression of the polyomavirus middle T oncoprotein (MMTV-PyMT). To identify MDSCs in an unbiased manner, we used single-cell RNA sequencing to compare MDSC-containing splenic myeloid cells from breast tumor–bearing mice with wild-type controls. Our computational analysis of 14,646 single-cell transcriptomes revealed that MDSCs emerge through an aberrant neutrophil maturation trajectory in the spleen that confers them an immunosuppressive cell state. We establish the MDSC-specific gene signature and identify CD84 as a surface marker for improved detection and enrichment of MDSCs in breast cancers.

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