Predicting the Malignant Breast Cancer using Tumor Tissue Features

Wenrui Zhao

2022

Abstract

Breast cancer is one of the most common cancers in women and is the second leading cause of death after lung cancer. In clinical diagnosis, fine needle aspiration cytology is often used in tumor diagnosis, considering safety, accuracy, and ease of operation. Pathologists can judge whether the patient’s tumor tissue is malignant by observing the cell population. The accuracy of fine-needle biopsy largely depends on the doctors who participate in sampling and analysis. Therefore, it is crucial to study which characteristics of cells can become a solid basis for discrimination. This article constructs univariate and multivariate logistic regression models to analyze the predictive value of 9 features of the cell to breast cancer. By evaluating the ROC curve, the article shows that the constructed model accurately predicts malignant tumor tissue. The 9 characteristics of FNA quantitative detection of tumor tissue are of great value in predicting malignant breast cancer.

Paper Citation

in Harvard Style

Zhao W. (2022). Predicting the Malignant Breast Cancer using Tumor Tissue Features. In Proceedings of the 4th International Conference on Biomedical Engineering and Bioinformatics - Volume 1: ICBEB, ISBN 978-989-758-595-1, pages 204-211. DOI: 10.5220/0011196000003443

in Bibtex Style

@conference{icbeb22,
author={Wenrui Zhao},
title={Predicting the Malignant Breast Cancer using Tumor Tissue Features},
booktitle={Proceedings of the 4th International Conference on Biomedical Engineering and Bioinformatics - Volume 1: ICBEB,},
year={2022},
pages={204-211},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011196000003443},
isbn={978-989-758-595-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 4th International Conference on Biomedical Engineering and Bioinformatics - Volume 1: ICBEB,
TI - Predicting the Malignant Breast Cancer using Tumor Tissue Features
SN - 978-989-758-595-1
AU - Zhao W.
PY - 2022
SP - 204
EP - 211
DO - 10.5220/0011196000003443