Using Machine Learning Based CNN Architectural Models for Breast Ductal Carcinoma Recognition

2022 ◽  
pp. 453-464
Author(s):  
Prashant Vats ◽  
Reenu Batra ◽  
Faraz Doja ◽  
Manu Phogat ◽  
Piyush Kumar Gupta ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ductal Carcinoma ◽  
Architectural Models

Machine learning methods with salivary metabolomics for breast cancer detection.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 3135-3135
Author(s):  
Takeshi Murata ◽  
Takako Yanagisawa ◽  
Toshiaki Kurihara ◽  
Miku Kaneko ◽  
Sana Ota ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Cancer Patients ◽  
Ductal Carcinoma ◽  
Healthy Controls ◽  
Breast Cancer Patients ◽  
Learning Methods ◽  
Validation Data ◽  
Machine Learning Methods ◽  
Significant Difference

3135 Background: Saliva is non-invasively accessible and informative biological fluid which has high potential for the early diagnosis of various diseases. The aim of this study is to develop machine learning methods and to explore new salivary biomarkers to discriminate breast cancer patients from healthy controls. Methods: We conducted a comprehensive metabolite analysis of saliva samples obtained from 101 patients with invasive carcinoma (IC), 23 patients with ductal carcinoma in situ (DCIS) and 42 healthy controls, using capillary electrophoresis and liquid chromatography with mass spectrometry to quantify hundreds of hydrophilic metabolites. Saliva samples were collected under 9h fasting and were split into training and validation data. Conventional statistical analyses and artificial intelligence-based methods were used to access the discrimination abilities of the quantified metabolite. Multiple logistic regression (MLR) model and an alternative decision tree (ADTree)-based machine learning methods were used. The generalization abilities of these mathematical models were validated in various computational tests, such as cross-validation and resampling methods. Results: Among quantified 260 metabolites, amino acids and polyamines showed significantly elevated in saliva from breast cancer patients, e.g. spermine showed the highest area under the receiver operating characteristic curves (AUC) to discriminate IC from C; 0.766 (95% confidence interval [CI]; 0.671 – 0.840, P < 0.0001). These metabolites showed no significant difference between C and DICS, i.e., these metabolites were elevated only in the samples of IC. The MLR yielded higher AUC to discriminate IC from C; 0.790 (95% CI; 0.699 – 0.859, P < 0.0001). The ADTree with ensemble approach showed the best AUC; 0.912 (95% CI; 0.838 – 0.961, P < 0.0001). In the comparison of these metabolites in the analysis of each subtype, seven metabolites were significantly different between Luminal A-like and Luminal B-like while, but few metabolites were significantly different among the other subtypes. Conclusions: These data indicated the combination of salivary metabolomic profiles including polyamines showed potential ability to screening breast cancer in a non-invasive way.

scholarly journals Boosting Breast Cancer Detection Using Convolutional Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Saad Awadh Alanazi ◽  
M. M. Kamruzzaman ◽  
Md Nazirul Islam Sarker ◽  
Madallah Alruwaili ◽  
Yousef Alhwaiti ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Ductal Carcinoma ◽  
Automatic Identification ◽  
Life Threatening ◽  
Breast Cells ◽  
Quantitative Results ◽  
Validation Tests

Breast cancer forms in breast cells and is considered as a very common type of cancer in women. Breast cancer is also a very life-threatening disease of women after lung cancer. A convolutional neural network (CNN) method is proposed in this study to boost the automatic identification of breast cancer by analyzing hostile ductal carcinoma tissue zones in whole-slide images (WSIs). The paper investigates the proposed system that uses various convolutional neural network (CNN) architectures to automatically detect breast cancer, comparing the results with those from machine learning (ML) algorithms. All architectures were guided by a big dataset of about 275,000, 50 × 50-pixel RGB image patches. Validation tests were done for quantitative results using the performance measures for every methodology. The proposed system is found to be successful, achieving results with 87% accuracy, which could reduce human mistakes in the diagnosis process. Moreover, our proposed system achieves accuracy higher than the 78% accuracy of machine learning (ML) algorithms. The proposed system therefore improves accuracy by 9% above results from machine learning (ML) algorithms.

Classification of Invasive Ductal Carcinoma from histopathology breast cancer images using Stacked Generalized Ensemble

2020 ◽  
pp. 1-16
Author(s):  
Deepika Kumar ◽  
Usha Batra
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Deep Learning ◽  
Invasive Ductal Carcinoma ◽  
Cancer Detection ◽  
Ductal Carcinoma ◽  
Machine Learning Algorithms ◽  
Diagnostic Methods ◽  
Learner Model ◽  
Histopathological Images

Breast cancer positions as the most well-known threat and the main source of malignant growth-related morbidity and mortality throughout the world. It is apical of all new cancer incidences analyzed among females. However, Machine learning algorithms have given rise to progress across different domains. There are various diagnostic methods available for cancer detection. However, cancer detection through histopathological images is considered to be more accurate. In this research, we have proposed the Stacked Generalized Ensemble (SGE) approach for breast cancer classification into Invasive Ductal Carcinoma+ and Invasive Ductal Carcinoma-. SGE is inspired by the stacking model which utilizes output predictions. Here, SGE uses six deep learning models as level-0 learner models or sub-models and Logistic regression is used as Level – 1 learner or meta – learner model. Invasive Ductal Carcinoma dataset for histopathology images is used for experimentation. The results of the proposed methodology have been compared and analyzed with existing machine learning and deep learning methods. The results demonstrate that the proposed methodology performed exponentially good in image classification in terms of accuracy, precision, recall, and F1 measure.

scholarly journals Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning

2019 ◽  
Author(s):  
Shikha Roy ◽  
Rakesh Kumar ◽  
Vaibhav Mittal ◽  
Dinesh Gupta
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Machine Learning ◽  
Invasive Ductal Carcinoma ◽  
Ductal Carcinoma ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Classification Models ◽  
Rna Seq ◽  
Late Stages

AbstractEarly detection of breast cancer and its correct stage determination are important for prognosis and rendering appropriate personalized clinical treatment to breast cancer patients. However, despite considerable efforts and progress, there is a need to identify the specific genomic factors responsible for, or accompanying Invasive Ductal Carcinoma (IDC) progression stages, which can aid the determination of the correct cancer stages. We have developed two-class machine-learning classification models to differentiate the early and late stages of invasive ductal carcinoma. The prediction models are trained with RNA-seq gene expression profiles representing different IDC stages of 610 patients, obtained from The Cancer Genome Atlas (TCGA). Different supervised learning algorithms were trained and evaluated with an enriched model learning, facilitated by different feature selection methods. We also developed a machine-learning classifier trained on the same datasets with training sets reduced data corresponding to IDC driver genes. Based on these two classifiers, we have developed a web-server Duct-BRCA-CSP to predict early stage from late stages of IDC based on input RNA-seq gene expression profiles. The analysis conducted by us also enables deeper insights into the stage-dependent molecular events accompanying breast ductal carcinoma progression. The server is publicly available at http://bioinfo.icgeb.res.in/duct-BRCA-CSP.

scholarly journals Machine Learning-Based Image Analysis for Accelerating the Diagnosis of Complicated Preneoplastic and Neoplastic Ductal Lesions in Breast Biopsy Tissues

2021 ◽  
Author(s):  
Shinya Sato ◽  
Satoshi Maki ◽  
Takashi Yamanaka ◽  
Daisuke Hoshino ◽  
Yukihide Ota ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Breast Biopsy ◽  
Ductal Carcinoma ◽  
Pathological Diagnosis ◽  
Comparison Analysis ◽  
Preneoplastic Lesions ◽  
Biopsy Specimens ◽  
Ductal Hyperplasia ◽  
Neoplastic Lesions

Abstract Purpose: Diagnosis of breast preneoplastic and neoplastic lesions is difficult due to their similar morphology in breast biopsy specimens. To diagnose these lesions, pathologists perform immunohistochemical analysis and consult with expert breast pathologists. These additional examinations are time-consuming and expensive. Artificial intelligence (AI)-based image analysis has recently improved, and may help in ordinal pathological diagnosis. Here, we showed the significance of machine learning-based image analysis of breast preneoplastic and neoplastic lesions for facilitating high-throughput diagnosis.Methods: Images were obtained from normal mammary glands, hyperplastic lesions, preneoplastic lesions and neoplastic lesions, such as usual ductal hyperplasia (UDH), columnar cell lesion (CCL), ductal carcinoma in situ (DCIS), and DCIS with comedo necrosis (comedo DCIS) in breast biopsy specimens. The original enhanced convoluted neural network (CNN) system was used for analyzing the pathological images. Results: The AI-based image analysis provided the following area under the curve values (AUC): normal lesion vs. DCIS, 0.9902; DCIS vs. comedo DCIS, 0.9942; normal lesion vs. CCL, 0.9786; and UDH vs. DCIS, 1.000. Multiple comparison analysis showed precision and recall scores similar to those of single comparison analysis. Based on the Gradient-weighted Class Activation Mapping (Grad-CAM) used to visualize the important regions reflecting the result of CNN analysis, the ratio of stromal tissue in the whole weighted area was significantly higher in UDH and CCL than that in DCIS. Conclusions: These analyses may provide a more accurate and rapid pathological diagnosis of patients. Moreover, Grad-CAM identifies uncharted important histological characteristics for newer pathological findings and targets of research for understanding diseases.

scholarly journals Cross-cancer Prediction: A Novel Machine Learning Approach to Discover Molecular Targets for Development of Treatments for Multiple Cancers

2018 ◽  
Vol 17 ◽  
pp. 117693511880539 ◽  
Author(s):  
Katie Gao ◽  
Dayong Wang ◽  
Yi Huang
Keyword(s):  
Machine Learning ◽  
Ductal Carcinoma ◽  
Molecular Targets ◽  
Prediction Method ◽  
New Drugs ◽  
Cancer Drug ◽  
Learning Approach ◽  
Cancer Prediction ◽  
Multiple Cancers ◽  
Machine Learning Approach

Conventional cancer drug development has long been limited to organ- or tissue-specific cancer types. However, it has become increasingly known that specific genetic abnormalities are responsible for the carcinogenesis of multiple cancers. The recent US Food and Drug Administration (FDA) approval of the first multi-cancer drug, Keytruda, has demonstrated the feasibility of developing new drugs that target multiple cancers. Despite a promising future, methodological development for identifying multi-cancer molecular targets remains encumbered. This study developed a novel machine learning approach to identify such genes responsible for multiple cancers by synthesizing salient genomic information from cancer-specific classification models. This approach centered on the cross-cancer prediction method for identifying groups of cancers with high cross-cancer predictability. Furthermore, a robust hybrid classifier, comprising Prediction Analysis for Microarrays and Random Forest, was developed to integrate predictive models for gene inference. This approach has successfully identified key genes shared by endometrial cancer, mammary gland ductal carcinoma, and small cell lung cancer. The results are supported by published experimental evidence. This framework holds potential to transform the current methods of discovering multi-cancer molecular targets for clinical oncology.

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