scholarly journals Diagnostic performance of peripheral leukocyte telomere G‐tail length for detecting breast cancer

2020 ◽  
Vol 111 (5) ◽  
pp. 1856-1861
Author(s):  
Yumiko Koi ◽  
Yasuhiro Tsutani ◽  
Yukie Nishiyama ◽  
Miyuki Kanda ◽  
Yoshitomo Shiroma ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Diagnostic Performance ◽  
Tail Length ◽  
Peripheral Leukocyte

In vitro toxicological assessment of flumethrin’s effects on MCF-7 breast cancer cells

2021 ◽  
pp. 096032712110227
Author(s):  
S Kara-Ertekin ◽  
S Yazar ◽  
M Erkan
Keyword(s):  
Breast Cancer ◽  
Cancer Cell Line ◽  
Tail Length ◽  
Tail Moment ◽  
High Concentration ◽  
Toxicological Assessment ◽  
Estrogenic Effects ◽  
Pyrethroid Pesticides ◽  
Mcf 7

Pyrethroid pesticides are frequently used for household insect control of insects and in agriculture and livestock. Flumethrin is a pyrethroid that is used against ectoparasites in many animals. The goal of this study was to evaluate the cytotoxic, apoptotic, genotoxic, and estrogenic effects of flumethrin on the mammalian breast cancer cell line (MCF-7). Compared with control groups, a dose-dependent decrease was observed in cell viability at concentrations of 100 µM and higher. The cytotoxic and apoptotic effects detected by LDH assay and AO/EtBr staining increased significantly at a concentration of 1000 µM. The expression of BCL2, which is an anti-apoptotic gene, significantly decreased, whereas BAX, TP53, and P21 expression significantly increased. The results of a comet assay indicated that flumethrin significantly changed tail length, tail % DNA, tail moment, and Olive tail moment in concentrations above 1 and 10 µM. In addition, a 0.1 µM concentration of flumethrin affected ERα receptor mediated cell proliferation and increased transcription of estrogen-responsive pS2 (TFF1) and progesterone receptor (PGR) genes. As a result, flumethrin-induced apoptosis and cytotoxicity at a high concentration, while induced genotoxicity even at lower concentrations. Flumethrin is an endocrine disrupting insecticide with estrogenic effects at very low concentrations.

Logistic Regression and Artificial Neural Network: A Comparative Study in Diagnosing Breast Cancer

2021 ◽  
pp. 6330-6334
Author(s):  
W. Abdul Hameed ◽  
Anuradha D. ◽  
Kaspar S.
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Artificial Neural Network ◽  
Logistic Regression ◽  
Breast Tumor ◽  
Diagnostic Performance ◽  
Malignant Breast Tumor ◽  
Malignant Breast ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Breast tumor is a common problem in gynecology. A reliable test for preoperative discrimination between benign and malignant breast tumor is highly helpful for clinicians in culling the malignant cells through felicitous treatment for patients. This paper is carried out to generate and estimate both logistic regression technique and Artificial Neural Network (ANN) technique to predict the malignancy of breast tumor, utilizing Wisconsin Diagnosis Breast Cancer Database (WDBC). Our aim in this Paper is: (i) to compare the diagnostic performance of both methods in distinguishing between malignant and benign patterns, (ii) to truncate the number of benign cases sent for biopsy utilizing the best model as an auxiliary implement, and (iii) to authenticate the capability of each model to recognize incipient cases as an expert system.

scholarly journals Breast cancer identification via modeling of peripherally circulating miRNAs

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e4551 ◽  
Author(s):  
Xiaomeng Cui ◽  
Zhangming Li ◽  
Yilei Zhao ◽  
Anqi Song ◽  
Yunbo Shi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Prediction Accuracy ◽  
Diagnostic Performance ◽  
Validation Cohort ◽  
Multiple Linear Regression Model ◽  
Circulating Mirnas ◽  
Independent Validation ◽  
Technical Possibility ◽  
Validation Set ◽  
Prolonged Life Expectancy

Prolonged life expectancy in humans has been accompanied by an increase in the prevalence of cancers. Breast cancer (BC) is the leading cause of cancer-related deaths. It accounts for one-fourth of all diagnosed cancers and affects one in eight females worldwide. Given the high BC prevalence, there is a practical need for demographic screening of the disease. In the present study, we re-analyzed a large microRNA (miRNA) expression dataset (GSE73002), with the goal of optimizing miRNA biomarker selection using neural network cascade (NNC) modeling. Our results identified numerous candidate miRNA biomarkers that are technically suitable for BC detection. We combined three miRNAs (miR-1246, miR-6756-5p, and miR-8073) into a single panel to generate an NNC model, which successfully detected BC with 97.1% accuracy in an independent validation cohort comprising 429 BC patients and 895 healthy controls. In contrast, at least seven miRNAs were merged in a multiple linear regression model to obtain equivalent diagnostic performance (96.4% accuracy in the independent validation set). Our findings suggested that suitable modeling can effectively reduce the number of miRNAs required in a biomarker panel without compromising prediction accuracy, thereby increasing the technical possibility of early detection of BC.

Developing and validating deep learning-based heterogeneous model to improve diagnostic performance of ultrasound elastography for axillary lymph node metastasis of early breast cancer.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e12583-e12583
Author(s):  
Jian Li ◽  
Cai Nian ◽  
Xie Ze-Ming ◽  
Zhou Jingwen ◽  
Huang Kemin
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Lymph Node ◽  
Early Breast Cancer ◽  
Predictive Value ◽  
Axillary Lymph Node ◽  
Diagnostic Performance ◽  
Ultrasound Elastography ◽  
Axillary Lymph ◽  
Heterogeneous Model

e12583 Background: To improve the performance of ultrasound (US) for diagnosing metastatic axillary lymph node (ALN), machine learning was used to reveal the inherently medical hints from ultrasonic images and assist pre-treatment evaluation of ALN for patients with early breast cancer. Methods: A total of 214 eligible patients with 220 breast lesions, from whom 220 target ALNs of ipsilateral axillae underwent ultrasound elastography (UE), were prospectively recruited. Based on feature extraction and fusion of B-mode and shear wave elastography (SWE) images of 140 target ALNs using radiomics and deep learning, with reference to the axillary pathological evaluation from training cohort, a proposed deep learning-based heterogeneous model (DLHM) was established and then validated by a collection of B-mode and SWE images of 80 target ALNs from testing cohort. Performance was compared between UE based on radiological criteria and DLHM in terms of areas under the receiver operating characteristics curve (AUC), sensitivity, specificity, accuracy, negative predictive value, and positive predictive value for diagnosing ALN metastasis. Results: DLHM achieved an excellent performance for both training and validation cohorts. In the prospectively testing cohort, DLHM demonstrated the best diagnostic performance with AUC of 0.911(95% confidence interval [CI]: 0.826, 0.963) in identifying metastatic ALN, which significantly outperformed UE in terms of AUC (0.707, 95% CI: 0.595, 0.804, P<0.001). Conclusions: DLHM provides an effective, accurate and non-invasive preoperative method for assisting the diagnosis of ALN metastasis in patients with early breast cancer.[Table: see text]

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