Utilizing AI and Machine Learning for Predictive Analysis of Post-Treatment Cancer Recurrence

TL;DR

This paper reviews how AI and machine learning techniques can improve the prediction of cancer recurrence post-treatment by analyzing complex data, potentially enabling earlier interventions and personalized patient management.

Contribution

It introduces the application of various AI and ML models for more accurate, reliable cancer recurrence prediction, emphasizing their role in personalized medicine and proactive care.

Findings

AI/ML models enhance prediction accuracy

Potential for earlier intervention strategies

Improved personalized treatment planning

Abstract

In oncology, recurrence after treatment is one of the major challenges, related to patients' survival and quality of life. Conventionally, prediction of cancer relapse has always relied on clinical observation with statistical model support, which almost fails to explain the complex, multifactorial nature of tumor recurrence. This research explores how AI and ML models may increase the accuracy and reliability of recurrence prediction in cancer. Therefore, AI and ML create new opportunities not only for personalized medicine but also for proactive management of patients through analyzing large volumes of data on genetics, clinical manifestations, and treatment. The paper describes the various AI and ML techniques for pattern identification and outcome prediction in cancer patients using supervised and unsupervised learning. Clinical implications provide an opportunity to review how early interventions could happen and the design of treatment planning.