Guiding Treatment Strategies: The Role of Adjuvant Anti-Her2 Neu Therapy and Skin/Nipple Involvement in Local Recurrence-Free Survival in Breast Cancer Patients

TL;DR

This paper demonstrates how causal inference models, specifically LiNGAM, can analyze observational breast cancer data to identify treatment effects and risk factors, aiding personalized treatment planning beyond traditional RCTs.

Contribution

The study applies LiNGAM to observational breast cancer data, revealing causal effects of therapies and clinical features on recurrence-free survival, enhancing understanding for personalized treatment strategies.

Findings

Adjuvant Anti-Her2 Neu Therapy increases survival by 169 days.

Skin/Nipple involvement decreases survival by 351 days.

Causal inference provides insights beyond correlation in observational data.

Abstract

This study explores how causal inference models, specifically the Linear Non-Gaussian Acyclic Model (LiNGAM), can extract causal relationships between demographic factors, treatments, conditions, and outcomes from observational patient data, enabling insights beyond correlation. Unlike traditional randomized controlled trials (RCTs), which establish causal relationships within narrowly defined populations, our method leverages broader observational data, improving generalizability. Using over 40 features in the Duke MRI Breast Cancer dataset, we found that Adjuvant Anti-Her2 Neu Therapy increased local recurrence-free survival by 169 days, while Skin/Nipple involvement reduced it by 351 days. These findings highlight the therapy's importance for Her2-positive patients and the need for targeted interventions for high-risk cases, informing personalized treatment strategies.