# Prediction models of adverse outcomes following surgery and radiotherapy for breast cancer: a systematic review

## TL;DR

This paper reviews models that predict side effects from breast cancer surgery and radiotherapy, finding most are not yet ready for clinical use due to lack of validation.

## Contribution

The study systematically reviews prediction models for adverse outcomes in breast cancer treatment, highlighting gaps in external validation and long-term effect prediction.

## Key findings

- Most prediction models lack external validation, limiting their clinical use.
- Few models predict long-term effects like breast appearance and quality of life.
- Machine learning models show promise for complex endpoints.

## Abstract

Breast surgery and radiotherapy are the most common treatment modalities for breast cancer, although both may have side-effects that can affect quality of life. Being able to identify patients at risk of adverse outcomes would enable optimisation of individualised treatment plans to improve the experience of breast cancer survivors. A systematic review of prediction models for adverse outcomes following surgery and radiotherapy for breast cancer was conducted. PubMed, Medline, Scopus, Web of Science, and CINAHL databases were searched using relevant key words and Medical Subject Heading terms. The search yielded 5376 articles, of which 33 articles were included. Data were extracted on study design, sources of training and test/validation data, predictors, outcomes, model performance, and validation. Several prediction models for adverse outcomes following breast surgery with or without radiotherapy have been developed. For short-term side-effects, these include the American College of Surgeons National Surgical Quality Improvement Program Surgical Risk Calculator, the Breast Reconstruction Risk Assessment score, and the Breast Cancer Surgery Risk Calculator. Despite the relatively large training datasets used in the development of prediction models, they suffer from a relative lack of external validation. There is as yet no externally validated prediction model for long-term adverse outcomes, although machine learning and multiscale finite element models show promise. Overall, while significant advancements have been made in developing these prediction models, the majority are not yet ready for widespread clinical implementation. This systematic review also highlights a lack of prediction models for long-term side-effects and more complex outcomes, suggesting areas for future research.

•A systematic review of prediction models for side-effects from breast cancer therapy was conducted.•The majority of models lack external validation, limiting their clinical use.•ML-based models show promise for complex endpoints.•Few models predict long-term effects like breast appearance and QoL.•Future research should prioritise external validation across diverse populations.

A systematic review of prediction models for side-effects from breast cancer therapy was conducted.

The majority of models lack external validation, limiting their clinical use.

ML-based models show promise for complex endpoints.

Few models predict long-term effects like breast appearance and QoL.

Future research should prioritise external validation across diverse populations.