# A Systematic Review on Machine Learning Techniques for Survival Analysis in Cancer

## TL;DR

This paper reviews how machine learning improves survival analysis in cancer studies, finding varied performance across methods and cancer types.

## Contribution

The study systematically evaluates ML techniques for cancer survival analysis, identifying performance trends and methodological variability.

## Key findings

- ML methods generally improve survival prediction across most cancer types.
- Multi-task and deep learning show superior performance but are rarely used.
- Significant variability exists in ML methodologies and implementations.

## Abstract

Machine learning (ML) offers opportunities to overcome limitations of conventional survival analyses, which are commonly found in cancer studies. However, the choice and levels of performance of ML methods vary across studies. As a result, it is unclear whether they consistently outperform traditional statistical methods and whether one particular ML strategy may outperform others for survival analysis. The present study aimed to systematically review the literature around this emerging topic.

This systematic review was conducted using the PRISMA guidelines. Electronic databases were systematically searched using keywords related to Machine Learning, Survival Analysis and Cancer.

The review included 196 studies, from which 39 comparable studies were used for the analysis. Improved predictive performance was seen from the use of ML in almost all cancer types. Predictive performance of the different ML methods varied across cancer types, and multi‐task and deep learning methods appeared to yield superior performance; however, it was reported in only a minority of papers. This study also highlighted great variability in both methodologies and their implementations.