scholarly journals An Antibody-based Blood Test Utilizing a Panel of Biomarkers as a New Method for Improved Breast Cancer Diagnosis

2013 ◽  
Vol 5 ◽  
pp. BIC.S13236 ◽  
Author(s):  
Galit Yahalom ◽  
Daria Weiss ◽  
Ilya Novikov ◽  
Therese B. Bevers ◽  
Laszlo G. Radvanyi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Imaging ◽  
Blood Test ◽  
Characteristic Curve ◽  
Clinical Status ◽  
Area Under The Curve ◽  
Breast Cancer Diagnosis ◽  
Receiver Operator Characteristic Curve ◽  
Enzyme Linked Immunosorbent Assay ◽  
Breast Cancer Patients

In order to develop a new tool for diagnosis of breast cancer based on autoantibodies against a panel of biomarkers, a clinical trial including blood samples from 507 subjects was conducted. All subjects showed a breast abnormality on exam or breast imaging and final biopsy pathology of either breast cancer patients or healthy controls. Using an enzyme-linked immunosorbent assay, the samples were tested for autoantibodies against a predetermined number of biomarkers in various models that were used to determine a diagnosis, which was compared to the clinical status. Our new assay achieved a sensitivity of 95.2% [CI = 92.8–96.8%] at a fixed specificity of 49.5%. Receiver-operator characteristic curve analysis showed an area under the curve of 80.1% [CI = 72.6–87.6%]. These results suggest that a blood test which is based on models comprising several autoantibodies to specific biomarkers may be a new and novel tool for improving the diagnostic evaluation of breast cancer.

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.

Comparative Study by Analytical Multidisciplinary Methods and Oxidative Stress in Breast Cancer Diagnosis

2018 ◽  
Vol 69 (8) ◽  
pp. 2254-2259
Author(s):  
Irina Jari ◽  
Alexandru Naum ◽  
Liliana Gheorghe Moisii ◽  
Cipriana Stefanescu ◽  
Dragos Negru ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Oxidative Stress ◽  
Cancer Diagnosis ◽  
Gold Standard ◽  
Breast Imaging ◽  
Diagnostic Performance ◽  
Area Under The Curve ◽  
Breast Mri ◽  
Breast Cancer Diagnosis ◽  
And Oxidative Stress

To evaluate the diagnostic performance of mammography, elastography and breast magnetic resonance imaging (MRI), as tools for breast cancer diagnosis, against pathological diagnosis as the gold standard. Other risk factors such as obesity and oxidative stress are also disccused. In this comparison study, a total of 169 female patients (mean age 51 years, range 35-77 years) were enrolled between January 2016 and June 2017. After the physical examination of the breasts, patients were further randomized into three groups to mammography, elastography, or breast MRI. Only women with detected lesions classified into breast imaging and reporting data system (BI-RADS) category or Tsukuba elasticity score from 2 to 5 were included. Histopathology was used as the gold standard for diagnosis. The diagnostic performance of each modality was calculated. Of a total of 50 pathologically confirmed cancers, 25 were detected by mammography, 11 by elastography, and 14 by breast MRI, which resulted in sensitivities of 84% (PPV = 78%), 75% (PPV = 64%) and 86% (PPV = 75%), respectively. Mammography, elastography, and breast MRI led to 6, 5, and 4 false positive findings, which resulted in specificities of 86% (NPV = 90%), 87% (NPV = 92%) and 89% (NPV = 94%), respectively. The area under the curve (AUC) values for the mammography, elastography and breast MRI were 0.849 (95% CI, 0.758-0.939), 0.809 (95% CI, 0.670-0.948) and 0.876 (95% CI, 0.769-0.983). The DOR values were 32 (95% CI, 8-125), 20 (95% CI, 4-99) and 51 (95% CI, 8-315). The breast MRI proved a slight advantage over mammography as a diagnostic tool in breast cancer diagnosis.

scholarly journals Clinical Significance of Annexin A2 Expression in Breast Cancer Patients

Cancers ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 2
Author(s):  
Lee D. Gibbs ◽  
Kelsey Mansheim ◽  
Sayantan Maji ◽  
Rajesh Nandy ◽  
Cheryl M. Lewis ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Clinical Significance ◽  
Cell Lines ◽  
Breast Cancer Subtypes ◽  
Enzyme Linked Immunosorbent Assay ◽  
Breast Cancer Patients ◽  
Serum Samples ◽  
Cancer Subtypes ◽  
Tumor Tissues

Increasing evidence suggests that AnxA2 contributes to invasion and metastasis of breast cancer. However, the clinical significance of AnxA2 expression in breast cancer has not been reported. The expression of AnxA2 in cell lines, tumor tissues, and serum samples of breast cancer patients were analyzed by immunoblotting, immunohistochemistry, and enzyme-linked immunosorbent assay, respectively. We found that AnxA2 was significantly upregulated in tumor tissues and serum samples of breast cancer patients compared with normal controls. The high expression of serum AnxA2 was significantly associated with tumor grades and poor survival of the breast cancer patients. Based on molecular subtypes, AnxA2 expression was significantly elevated in tumor tissues and serum samples of triple-negative breast cancer (TNBC) patients compared with other breast cancer subtypes. Our analyses on breast cancer cell lines demonstrated that secretion of AnxA2 is associated with its tyrosine 23 (Tyr23) phosphorylation in cells. The expression of non-phosphomimetic mutant of AnxA2 in HCC1395 cells inhibits its secretion from cells compared to wild-type AnxA2, which further suggest that Tyr23 phosphorylation is a critical step for AnxA2 secretion from TNBC cells. Our analysis of AnxA2 phosphorylation in clinical samples further confirmed that the phosphorylation of AnxA2 at Tyr23 was high in tumor tissues of TNBC patients compared to matched adjacent non-tumorigenic breast tissues. Furthermore, we observed that the diagnostic value of serum AnxA2 was significantly high in TNBC compared with other breast cancer subtypes. These findings suggest that serum AnxA2 concentration could be a potential diagnostic biomarker for TNBC patients.

scholarly journals Serum Long Non-Coding RNAs PVT1, HOTAIR, and NEAT1 as Potential Biomarkers in Egyptian Women with Breast Cancer

Biomolecules ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 301
Author(s):  
Amal Ahmed Abd El-Fattah ◽  
Nermin Abdel Hamid Sadik ◽  
Olfat Gamil Shaker ◽  
Amal Mohamed Kamal ◽  
Nancy Nabil Shahin
Keyword(s):  
Breast Cancer ◽  
Logistic Regression ◽  
Cancer Patients ◽  
Breast Cancer Diagnosis ◽  
Serum Levels ◽  
Breast Cancer Patients ◽  
Multivariate Logistic Regression ◽  
Expression Levels ◽  
Non Coding Rnas ◽  
Pai 1

Long non-coding RNAs play an important role in tumor growth, angiogenesis, and metastasis in several types of cancer. However, the clinical significance of using lncRNAs as biomarkers for breast cancer diagnosis and prognosis is still poorly investigated. In this study, we analyzed the serum expression levels of lncRNAs PVT1, HOTAIR, NEAT1, and MALAT1, and their associated proteins, PAI-1, and OPN, in breast cancer patients compared to fibroadenoma patients and healthy subjects. Using quantitative real-time PCR (qRT-PCR), we compared the serum expression levels of the four circulating lncRNAs in patients with breast cancer (n = 50), fibroadenoma (n = 25), and healthy controls (n = 25). The serum levels of PAI-1 and OPN were measured using ELISA. Receiveroperating-characteristic (ROC) analysis and multivariate logistic regression were used to evaluate the diagnostic value of the selected parameters. The serum levels of HOTAIR, PAI-1, and OPN were significantly higher in breast cancer patients compared to controls and fibroadenoma patients. The serum level of PVT1 was significantly higher in breast cancer patients than in the controls, while that of NEAT1 was significantly lower in breast cancer patients compared to controls and fibroadenoma patients. Both ROC and multivariate logistic regression analyses revealed that PAI-1 has the greatest power in discriminating breast cancer from the control, whereas HOTAIR, PAI-1, and OPN have the greatest power in discriminating breast cancer from fibroadenoma patients. In conclusion, our data suggest that the serum levels of PVT1, HOTAIR, NEAT1, PAI-1, and OPN could serve as promising diagnostic biomarkers for breast cancer.

scholarly journals Plasma Level of MMP-10 May Be a Prognostic Marker in Early Stages of Breast Cancer

2020 ◽  
Vol 9 (12) ◽  
pp. 4122
Author(s):  
Barbara Maria Piskór ◽  
Andrzej Przylipiak ◽  
Emilia Dąbrowska ◽  
Iwona Sidorkiewicz ◽  
Marek Niczyporuk ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Area Under The Curve ◽  
Disease Stage ◽  
Enzyme Linked Immunosorbent Assay ◽  
Control Group ◽  
Early Stages ◽  
Potential Marker ◽  
Breast Cancer Biomarkers ◽  
Diagnostic Usefulness ◽  
Chemiluminescent Microparticle Immunoassay

Background: Stromelysins are potential breast cancer biomarkers. The aim of the study was to evaluate if plasma levels of selected metalloproteinases (MMPs) (stromelysin-1 (MMP-3) and stromelysin-10 (MMP-10)) and cancer antigen 15-3 (CA 15-3) used separately and in combination demonstrated diagnostic usefulness in breast cancer (BC). Methods: The study group consisted of 120 patients with BC, while the control group included 40 patients with benign breast cancer and 40 healthy individuals. Concentrations of MMP-3 and MMP-10 were determined by enzyme-linked immunosorbent assay; CA 15-3 was determined by chemiluminescent microparticle immunoassay. Results: In the group of patients with BC, the area under the curve (AUC) was significantly higher for all markers (except MMP-3) and all sets of markers. At the earliest disease stage, only MMP-10 had a significantly higher AUC (AUC = 0.8692, p < 0.001). Moreover, MMP-10 had the highest AUC (0.9166) among parameters tested separately. The highest AUC was observed for the combination of MMP-10 + CA 15-3 and MMP-3 + MMP-10 + CA 15-3 in line with disease progression (stage I 0.8884 and 0.8906, stage II 0.9244 and 0.9308, stages III + IV 0.9919 and 0.9944, respectively, p < 0.001 in all cases). Conclusions: The results suggest that MMP-10 could be a potential marker in early stages of BC. Moreover, plasma concentration of MMP-10 and MMP-3 in combination with CA 15-3 may improve diagnosis of this type of cancer.

Urine β-2-glycoprotein 1 as a biomarker for diagnosis of systemic lupus erythematosus

Lupus ◽  
2021 ◽  
pp. 096120332110142
Author(s):  
Jung Sun Lee ◽  
Eun-Ju Lee ◽  
Jeonghun Yeom ◽  
Ji Seon Oh ◽  
Seokchan Hong ◽  
...  
Keyword(s):  
Systemic Lupus Erythematosus ◽  
Lupus Erythematosus ◽  
Medical Center ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Diagnostic Value ◽  
Urine Samples ◽  
Enzyme Linked Immunosorbent Assay ◽  
Systemic Lupus ◽  
Asan Medical

Objective The need for a biomarker with robust sensitivity and specificity in diagnosing systemic lupus erythematosus (SLE) remains unmet. Compared with blood samples, urine samples are more easily collected; thus, we aimed to identify such a biomarker based on urinary proteomics which could distinguish patients with SLE from healthy controls (HCs). Methods Urine samples were collected from 76 SLE patients who visited rheumatology clinic in 2019 at Asan medical center and from 25 HCs. Urine proteins were analyzed using sequential windowed acquisition of all theoretical fragment ion spectra-mass spectrometry, and the candidate marker was confirmed by enzyme-linked immunosorbent assay (ELISA). Receiver operating characteristic curve analysis was used to determine the diagnostic value of the candidate biomarker. Results Of 1157 proteins quantified, 153 were differentially expressed in urine samples from HCs. Among them were previously known markers including α-1-acid glycoprotein 1, α-2-HS-glycoprotein, ceruloplasmin, and prostaglandin-H2 D-isomerase. Moreover, the amount of β-2 glycoprotein (APOH) was increased in the urine of patients with SLE. The ELISA results also showed the level of urine APOH was higher in patients with SLE than in HCs and patients with rheumatoid arthritis. Moreover, the level was not different between SLE patients with and without nephritis. The urine APOH had an area under the curve value of 0.946 at a cut-off value of 228.53 ng/mg (sensitivity 91.5%, specificity 92.0%) for the diagnosis of SLE. Conclusion The results indicate that the urine APOH level can be an appropriate screening tool in a clinical setting when SLE is suspected.

scholarly journals What mediates the racial/ethnic disparity in psychosocial stress among breast cancer patients?

2021 ◽  
Author(s):  
C. T. Sánchez-Díaz ◽  
S. Strayhorn ◽  
S. Tejeda ◽  
G. Vijayasiri ◽  
G. H. Rauscher ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Social Support ◽  
Cancer Patients ◽  
Cancer Diagnosis ◽  
Psychosocial Stress ◽  
Breast Cancer Diagnosis ◽  
Support Needs ◽  
Breast Cancer Patients ◽  
Hispanic Patients ◽  
Racial Ethnic

Abstract Background Prior studies have observed greater levels of psychosocial stress (PSS) among non-Hispanic (nH) African American and Hispanic women when compared to nH White patients after a breast cancer diagnosis. We aimed to determine the independent and interdependent roles of socioeconomic position (SEP) and unmet support in the racial disparity in PSS among breast cancer patients. Methods Participants were recruited from the Breast Cancer Care in Chicago study (n = 989). For all recently diagnosed breast cancer patients, aged 25–79, income, education, and tract-level disadvantage and affluence were summed to create a standardized socioeconomic position (SEP) score. Three measures of PSS related to loneliness, perceived stress, and psychological consequences of a breast cancer diagnosis were defined based on previously validated scales. Five domains of unmet social support needs (emotional, spiritual, informational, financial, and practical) were defined from interviews. We conducted path models in MPlus to estimate the extent to which PSS disparities were mediated by SEP and unmet social support needs. Results Black and Hispanic patients reported greater PSS compared to white patients and greater unmet social support needs (p = 0.001 for all domains). Virtually all of the disparity in PSS could be explained by SEP. A substantial portion of the mediating influence of SEP was further transmitted by unmet financial and practical needs among Black patients and by unmet emotional needs for Hispanic patients. Conclusions SEP appeared to be a root cause of the racial/ethnic disparities in PSS within our sample. Our findings further suggest that different interventions may be necessary to alleviate the burden of SEP for nH AA (i.e., more financial support) and Hispanic patients (i.e., more emotional support).

scholarly journals Association among B lymphocyte subset and rheumatoid arthritis in a Chinese population

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Haiyan You ◽  
Mengwei Cheng ◽  
Cui Ma ◽  
Wenjuan Zheng ◽  
Yu Jiang ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Healthy Subjects ◽  
B Lymphocyte ◽  
Lymphocyte Subsets ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Enzyme Linked Immunosorbent Assay ◽  
Roc Curve Analysis ◽  
Autoantibody Production ◽  
Plasma Cytokines

Abstract Background and aim Autoantibody production are the main risk factors for inflammation of rheumatoid arthritis (RA). This study aimed to investigate differences in B lymphocyte subsets (native B, memory B, and plasmablasts) and several cytokines in RA patients and their correlation with the clinical parameters. Methods In total, 81 RA patients (active RA and inactive RA) and 40 healthy subjects were recruited between September 2018 and October 2020. The distribution of B lymphocyte subsets in peripheral blood samples was measured via flow cytometry and the plasma cytokines were detected by enzyme linked immunosorbent assay. The receiver operating characteristic curve (ROC) was used to evaluate the value of each index for RA diagnosis and activity prediction. Results The percentages of native B and memory B cells in RA patients did not differ significantly from the percentages of those in healthy controls. However, the percentage of plasmablasts in active RA patients was significantly higher compared with healthy subjects and inactive RA patients. The percentage of plasmablasts was significantly related to C reaction protein. ROC curve analysis showed that when the best cutoff value of plasmablasts/B cell was 1.08%, the area under the curve (AUC) for diagnosing RA was 0.831 (95% CI 0.748 ~ 0.915), the specificity was 91.4%, and the sensitivity was 67.5%. The AUC predicted by the combination of plasmablast and anti-CCP for active RA patients was 0.760, which was higher than that of plasmablast and anti-CCP. Conclusion In conclusion, the percentage of plasmablast varies among RA patients in different stages. The percentage of plasmablasts can be used as an early diagnosis marker for RA.

scholarly journals Feasibility of circulating tumor cells (CTCs) combined with medical imaging examinations to screen for cancer and improve diagnosis in non-metastatic invasive breast cancer patients: a cohort study

2020 ◽  
Author(s):  
Xuan Shao ◽  
xiao yan jin ◽  
zhi gang chen ◽  
zhi gang zhang ◽  
ke wang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Medical Imaging ◽  
Tumor Cells ◽  
Circulating Tumor Cells ◽  
Breast Imaging ◽  
Chinese Women ◽  
Clinicopathological Characteristics ◽  
Breast Cancer Patients ◽  
Detection Rates ◽  
Gamma Imaging

Abstract Background: Previous study has reported that circulating tumor cells (CTCs) could be served as a diagnostic biomarker in breast cancer (BC) screening. However, the differential efficacy of routine examination including ultrasound (US), mammogram (MG), magnetic resonance imaging (MR), and breast-specific gamma imaging (BSGI) and CTCs is unknown. This study aimed to compare CTCs with common used BC screening imaging modalities and to evaluate whether their combination would enhance the diagnostic potency in non-metastatic BC patients.Methods: 102 treatment-naive non-metastatic BC patients, 177 patients with breast benign diseases (BBD) and 64 healthy females, who had CTC detection and at least one of the following medical imaging examinations, US, MG or MR between December 2017 and November 2018, were enrolled in this study.Correlations of CTC enumeration with patients’ clinicopathological characteristics and medical imaging examinations were evaluated. Results: CTC detection rates (average CTC counts) in stage I-III BC patients were 92.9% (2.1), 87.2% (2.4) and 100% (4.2), respectively. CTCs counts were positively associated with cancer stage (p = 0.0084) and tumor size (p = 0.0301). CTC counts were more correlated with US than MR or MG. CTC counts were not associated with molecular subtypes of BC nor breast-specific gamma imaging (BSGI) results, indicating that CTC enumeration cannot be used to predict molecular signatures of BC. CTCs and medical imaging examinations would have the best diagnostic performance for BC when CTC cut-off was set to 2 and imaging Breast Imaging-Reporting and Data System (BI-RADS) was set to 4b. Combination of CTC with US, MG or MR increased the sensitivity for BC diagnosis, especially for MG. Sensitivity of MG increased from 0.694 to 0.917, even more than in conjugation with US (0.901). Conclusion: CTCs counts can be used as a diagnostic aid in BC screening and early diagnosis. CTCs counts were more relevant to US than MR or MG. Conjugation of CTCs counts would improve the diagnostic potency of medical imaging examinations for diagnosing BC, especially for MG in Chinese women.

scholarly journals Radiomics Nomogram Based on Radiomics Score from Multiregional Diffusion-Weighted MRI and Clinical Factors for Evaluating HER-2 2+ Status of Breast Cancer

Diagnostics ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1491
Author(s):  
Chunli Li ◽  
Jiandong Yin
Keyword(s):  
Breast Cancer ◽  
Characteristic Curve ◽  
Receiver Operator Characteristic Curve ◽  
Clinical Factors ◽  
Clinical Value ◽  
Logistic Analysis ◽  
Diffusion Weighted ◽  
Apparent Diffusion ◽  
Her 2 ◽  
Multivariate Logistic Analysis

This study aimed to establish and validate a radiomics nomogram using the radiomics score (rad-score) based on multiregional diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) features combined with clinical factors for evaluating HER-2 2+ status of breast cancer. A total of 223 patients were retrospectively included. Radiomic features were extracted from multiregional DWI and ADC images. Based on the intratumoral, peritumoral, and combined regions, three rad-scores were calculated using the logistic regression model. Independent parameters were selected among clinical factors and combined rad-score (com-rad-score) using multivariate logistic analysis and used to construct a radiomics nomogram. The performance of the nomogram was evaluated using calibration, discrimination, and clinical usefulness. The areas under the receiver operator characteristic curve (AUCs) of intratumoral and peritumoral rad-scores were 0.824/0.763 and 0.794/0.731 in the training and validation cohorts, respectively. Com-rad-score achieved the highest AUC (0.860/0.790) among three rad-scores. ER status and com-rad-score were selected to establish the nomogram, which yielded good discrimination (AUC: 0.883/0.848) and calibration. Decision curve analysis demonstrated the clinical value of the nomogram in the validation cohort. In conclusion, radiomics nomogram, including clinical factors and com-rad-score, showed favorable performance for evaluating HER-2 2+ status in breast cancer.

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