scholarly journals Breast invasive ductal carcinoma diagnosis with a three-miRNA panel in serum

2021 ◽  
Author(s):  
Xuan Chen ◽  
Xinji Li ◽  
Jingyao Wang ◽  
Liwen Zhao ◽  
Xiqi Peng ◽  
...  
Keyword(s):  
Invasive Ductal Carcinoma ◽  
Target Genes ◽  
Validation Cohort ◽  
Ductal Carcinoma ◽  
Bioinformatic Analysis ◽  
Reverse Transcription Pcr ◽  
Breast Invasive Ductal Carcinoma ◽  
Clinical Burden ◽  
Noninvasive Biomarker ◽  
Noninvasive Biomarkers

Aim: Breast cancer, especially invasive ductal carcinoma (IDC), is the cause of a great clinical burden. miRNA could be considered as a noninvasive biomarkers for IDC diagnosis. Materials & methods: Two hundred and sixty participants (135 IDC patients and 125 healthy controls) were enrolled in a three-cohort study. The expression of 28 miRNAs in serum were detected with quantitative reverse transcription-PCR. Bioinformatic analysis was used for predicting the target genes of three selected miRNAs. Results: The expression level of seven miRNAs (miR-9-5p, miR-34b-3p, miR-1-3p, miR-146a-5p, miR-20a-5p, miR-34a-5p, miR-125b-5p) was discrepant at the validation cohort. Through statistical test, a three-miRNA panel (miR-9-5p, miR-34b-3p, miR-146a-5p) was significant for IDC diagnosis (AUC = 0.880, sensitivity = 86.25%, specificity = 81.25%). Conclusion: The three-miRNA panel in serum could be used as a noninvasive biomarker in the diagnosis of IDC.

scholarly journals Pyridoxine 5′-phosphate oxidase is correlated with human breast invasive ductal carcinoma development

Aging ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 2151-2176 ◽  
Author(s):  
Weimin Ren ◽  
Wencai Guan ◽  
Jinguo Zhang ◽  
Fanchen Wang ◽  
Guoxiong Xu
Keyword(s):  
Invasive Ductal Carcinoma ◽  
Ductal Carcinoma ◽  
Human Breast ◽  
Breast Invasive Ductal Carcinoma

scholarly journals Differentiating Grade in Breast Invasive Ductal Carcinoma Using Texture Analysis of MRI

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Gaoteng Yuan ◽  
Yihui Liu ◽  
Wei Huang ◽  
Bing Hu
Keyword(s):  
Support Vector Machine ◽  
Texture Analysis ◽  
Invasive Ductal Carcinoma ◽  
Ductal Carcinoma ◽  
Classification Model ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Feature Subset ◽  
Breast Invasive Ductal Carcinoma ◽  
Key Features

Purpose. The objective of this study is to investigate the use of texture analysis (TA) of magnetic resonance image (MRI) enhanced scan and machine learning methods for distinguishing different grades in breast invasive ductal carcinoma (IDC). Preoperative prediction of the grade of IDC can provide reference for different clinical treatments, so it has important practice values in clinic. Methods. Firstly, a breast cancer segmentation model based on discrete wavelet transform (DWT) and K-means algorithm is proposed. Secondly, TA was performed and the Gabor wavelet analysis is used to extract the texture feature of an MRI tumor. Then, according to the distance relationship between the features, key features are sorted and feature subsets are selected. Finally, the feature subset is classified by using a support vector machine and adjusted parameters to achieve the best classification effect. Results. By selecting key features for classification prediction, the classification accuracy of the classification model can reach 81.33%. 3-, 4-, and 5-fold cross-validation of the prediction accuracy of the support vector machine model is 77.79%~81.94%. Conclusion. The pathological grading of IDC can be predicted and evaluated by texture analysis and feature extraction of breast tumors. This method can provide much valuable information for doctors’ clinical diagnosis. With further development, the model demonstrates high potential for practical clinical use.

Sign in / Sign up

Export Citation Format

Share Document