scholarly journals Combining Pathway Identification and Breast Cancer Survival Prediction via Screening-Network Methods

2018 ◽  
Vol 9 ◽  
Author(s):  
Antonella Iuliano ◽  
Annalisa Occhipinti ◽  
Claudia Angelini ◽  
Italia De Feis ◽  
Pietro Liò
Keyword(s):  
Breast Cancer ◽  
Cancer Survival ◽  
Breast Cancer Survival ◽  
Survival Prediction ◽  
Network Methods

scholarly journals Integrating Somatic Mutations for Breast Cancer Survival Prediction Using Machine Learning Methods

2021 ◽  
Vol 11 ◽  
Author(s):  
Zongzhen He ◽  
Junying Zhang ◽  
Xiguo Yuan ◽  
Yuanyuan Zhang
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Feature Selection ◽  
Somatic Mutations ◽  
Cancer Survival ◽  
Feature Selection Method ◽  
Breast Cancer Survival ◽  
Survival Prediction ◽  
Omics Data ◽  
Selection Methods

Breast cancer is the most common malignancy in women, and because it has a high mortality rate, it is urgent to develop computational methods to increase the accuracy of breast cancer survival predictive models. Although multi-omics data such as gene expression have been extensively used in recent studies, the accurate prognosis of breast cancer remains a challenge. Somatic mutations are another important and promising data source for studying cancer development, and its effect on the prognosis of breast cancer remains to be further explored. Meanwhile, these omics datasets are high-dimensional and redundant. Therefore, we adopted multiple kernel learning (MKL) to efficiently integrate somatic mutation to currently molecular data including gene expression, copy number variation (CNV), methylation, and protein expression data for the prediction of breast cancer survival. Before integration, the maximum relevance minimum redundancy (mRMR) feature selection method was utilized to select features that present high relevance to survival and low redundancy among themselves for each type of data. The experimental results demonstrated that the proposed method achieved the most optimal performance and there was a remarkable improvement in the prediction performance when somatic mutations were included, indicating that somatic mutations are critical for improving breast cancer survival predictions. Moreover, mRMR was superior to other feature selection methods used in previous studies. Furthermore, MKL outperformed the other traditional classifiers in multi-omics data integration. Our analysis indicated that through employing promising omics data such as somatic mutations and harnessing the power of proper feature selection methods and effective integration frameworks, the breast cancer survival predictive accuracy can be further increased, thereby providing a more optimal clinical diagnosis and more effective treatment for breast cancer patients.

Identification of a New Eight-Long Noncoding RNA Molecular Signature for Breast Cancer Survival Prediction

2019 ◽  
Vol 38 (12) ◽  
pp. 1529-1539 ◽  
Author(s):  
Yaqiong Zhang ◽  
Zhaoyun Li ◽  
Meifang Chen ◽  
Hanjun Chen ◽  
Qianyi Zhong ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Long Noncoding Rna ◽  
Noncoding Rna ◽  
Cancer Survival ◽  
Breast Cancer Survival ◽  
Molecular Signature ◽  
Survival Prediction

scholarly journals An Artificial Intelligence-Enabled Pipeline for Medical Domain: Malaysian Breast Cancer Survivorship Cohort as a Case Study

Diagnostics ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1492
Author(s):  
Mogana Darshini Ganggayah ◽  
Sarinder Kaur Dhillon ◽  
Tania Islam ◽  
Foad Kalhor ◽  
Teh Chean Chiang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Quality Of Life ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Cancer Survival ◽  
Breast Cancer Survival ◽  
Survival Prediction ◽  
Automated Scoring

Automated artificial intelligence (AI) systems enable the integration of different types of data from various sources for clinical decision-making. The aim of this study is to propose a pipeline to develop a fully automated clinician-friendly AI-enabled database platform for breast cancer survival prediction. A case study of breast cancer survival cohort from the University Malaya Medical Centre was used to develop and evaluate the pipeline. A relational database and a fully automated system were developed by integrating the database with analytical modules (machine learning, automated scoring for quality of life, and interactive visualization). The developed pipeline, iSurvive has helped in enhancing data management as well as to visualize important prognostic variables and survival rates. The embedded automated scoring module demonstrated quality of life of patients whereas the interactive visualizations could be used by clinicians to facilitate communication with patients. The pipeline proposed in this study is a one-stop center to manage data, to automate analytics using machine learning, to automate scoring and to produce explainable interactive visuals to enhance clinician-patient communication along the survivorship period to modify behaviours that relate to prognosis. The pipeline proposed can be modelled on any disease not limited to breast cancer.

scholarly journals Individualized Prediction of Breast Cancer Survival Using Flexible Parametric Survival Modeling: Analysis of a Hospital-Based National Clinical Cancer Registry

Cancers ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1567
Author(s):  
Donsuk Pongnikorn ◽  
Phichayut Phinyo ◽  
Jayanton Patumanond ◽  
Karnchana Daoprasert ◽  
Pachaya Phothong ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Prognostic Model ◽  
Cancer Survival ◽  
External Validation ◽  
Disease Free Survival ◽  
Breast Cancer Survival ◽  
Prognostic Models ◽  
Survival Prediction ◽  
Individualized Prediction ◽  
Parametric Survival

Prognostic models for breast cancer developed from Western countries performed less accurately in the Asian population. We aimed to develop a survival prediction model for overall survival (OS) and disease-free survival (DFS) for Thai patients with breast cancer. We conducted a prognostic model research using a multicenter hospital-based cancer clinical registry from the Network of National Cancer Institutes of Thailand. All women diagnosed with breast cancer who underwent surgery between 1 January 2010 and 31 December 2011 were included in the analysis. A flexible parametric survival model was used for developing the prognostic model for OS and DFS prediction. During the study period, 2021 patients were included. Of these, 1386 patients with 590 events were available for a complete-case analysis. The newly derived individualized prediction of breast cancer survival or the IPBS model consists of twelve routinely available predictors. The C-statistics from the OS and the DFS model were 0.72 and 0.70, respectively. The model showed good calibration for the prediction of five-year OS and DFS. The IPBS model provides good performance for the prediction of OS and PFS for breast cancer patients. A further external validation study is required before clinical implementation.

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