Multimodal Data-Driven Intelligent Systems for Breast Cancer Prediction

Author:

Pichai Shanmugavadivu,Kanimozhi G.,Rani M. Mary Shanthi,Riyaz N.K.

Abstract

Cancer, a malignant disease, results from abnormalities in the body cells that lead to uncontrolled growth and division, surpassing healthy growth and stability. In the case of breast cancer, this uncontrolled growth and division occurs in breast cells. Early identification of breast cancer is key to lowering mortality rates. Several new developments in artificial intelligence predictive models show promise for assisting decision-making. The primary goal of the proposed study is to build an efficient Breast Cancer Intelligent System using a multimodal dataset. The aim is to to establish Computer-Aided Diagnosis for breast cancer by integrating various data.This study uses the TCGA "The Cancer Genome Atlas Breast Invasive Carcinoma Collection" (TCGA-BRCA) dataset, which is part of an ongoing effort to create a community integrating cancer phenotypic and genotypic data. The TCGA- BRCA dataset includes: Clinical Data, RNASeq Gene Data, Mutation Data, and Methylation Data. Both clinical and genomic data are used in this study for breast cancer diagnosis. Integrating multiple data modalities enhances the robustness and precision of diagnostic and prognostic models in comparison with conventional techniques. The approach offers several advantages over unimodal models due to its ability to integrate diverse data sources. Additionally, these models can be employed to forecast the likelihood of a patient developing breast cancer in the near future, providing a valuable tool for early intervention and treatment planning.

Publisher

European Alliance for Innovation n.o.

Reference36 articles.

1. World health organization cancer. (2018). Fact Sheet-Cancer. Available at: https://www.who.int/health-topics/cancer

2. https://www.livemint.com/news/india/icmr-data-shows-unequal-toll-of-cancer-on-women-11670349329355.html

3. https://www.industryarc.com/PressRelease/2625/Oncology-Market-Research.html

4. Mertz, S., Mayer, M., Paonessa, D., Papadopoulos, E., Alessandro, F., Peccatori, K. S., ... & Spence, D. (2016). Breast Cancer Center Survey: Cancer center management, support, and perception of mBC patient needs across 582 healthcare professionals

5. https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(20)32381-3/fulltext

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3