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Cancer Survival Analysis

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...

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Innovations in Artificial Intelligence-Driven Breast Cancer Survival Prediction: A Narrative Review.

Mehwish Mooghal¹, Saad Nasir², Aiman Arif³

¹Section Breast Surgery, Department of Surgery, Aga Khan University Hospital Karachi, Sindh, Pakistan.

Cancer Informatics

|November 1, 2024

Summary

Artificial Intelligence (AI) enhances breast cancer (BC) survival prediction using machine learning and deep neural networks. AI offers personalized care and can reduce healthcare disparities, especially in Low- and Middle-Income Countries (LMICs).

Keywords:

Breast cancer artificial intelligence machine learning prognostic models survival prediction

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Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

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Published on: May 17, 2019

Area of Science:

Oncology
Medical Informatics
Artificial Intelligence

Background:

Breast cancer (BC) survival prediction is critical for patient care and treatment planning.
Traditional prognostic methods face limitations in accuracy and personalization.
Artificial Intelligence (AI) presents novel approaches to enhance BC survival prediction.

Purpose of the Study:

To review the current state of AI-driven BC survival prediction.
To highlight the potential of AI in refining prognosis and tailoring treatments.
To explore AI's role in addressing healthcare disparities in BC care, particularly in LMICs.

Main Methods:

Narrative review of existing literature on AI applications in BC survival prediction.
Exploration of various AI models including machine learning and deep neural networks.
Analysis of challenges and ethical considerations in AI implementation.

Main Results:

Diverse AI models show significant potential for improving prognosis accuracy.
AI can facilitate personalized treatment strategies based on predicted survival outcomes.
AI offers a pathway to bridge healthcare disparities in BC care globally.

Conclusions:

AI is a transformative technology for breast cancer survival prediction and patient care.
Clinician integration, model generalizability, and ethical considerations are key for successful AI adoption.
Collaborative efforts are vital to leverage AI for equitable BC care worldwide, especially in LMICs.