Survival Analysis on Breast Cancer METABRIC Dataset

Table of Contents

1. Overview
2. Dataset
3. Techniques and Tools
4. Usage
5. Results
6. Future Work

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1. Overview

This project leverages the Breast Cancer METABRIC dataset to analyze survival outcomes, focusing on identifying clinical and molecular factors influencing patient survival.
Key analyses include:

• Exploratory Data Analysis (EDA) – Understanding distributions and patterns in tumor stages, treatments, and survival times.
• Survival Analysis – Applying Kaplan-Meier estimators, log-rank tests, and Cox Proportional Hazards models.
• Data Imputation & Preprocessing – Handling missing values using mode imputation.

The goal is to provide insights into survival probabilities and the impact of treatments, tumor characteristics, and patient demographics.

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2. Dataset

The Breast Cancer METABRIC dataset contains data from over 2,000 breast cancer patients, including:

• Clinical Features: Tumor stage, hormone therapy, chemotherapy, radiotherapy, etc.
• Pathological Markers: HER2 status, PR status, lymph node involvement, etc.
• Survival Data: Overall survival in months, relapse-free survival, patient status.

The primary goal is to study survival time and its influencing factors.

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3. Techniques and Tools

Survival Analysis

• Kaplan-Meier Survival Curves – To estimate survival probabilities across tumor stages and treatment groups.
• Log-Rank Test – To statistically compare survival distributions.
• Cox Proportional Hazards Model – To assess risk factors affecting survival.

EDA (Exploratory Data Analysis)

• Distributions:
  • Tumor Stage, Treatment (Chemo, Radio, Hormone), Age Groups
• Correlation Analysis:
  • Tumor Size vs Tumor Stage
  • Survival Time Across Age Groups
  • Treatment Impact on Survival
• Cluster Analysis:
  • Patients where Relapse-Free Status ≈ Overall Survival
  • Stage 3 vs Stage 4 survival patterns
• Boxplots & Scatter Plots to explore treatment outcomes and tumor progression trends.

Data Imputation

• Mode Imputation – Used for handling missing categorical values.

Tools Used

• Python Libraries:
  • pandas, numpy – Data processing
  • matplotlib, seaborn – Visualization
  • lifelines – Survival analysis

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4. Usage

1️⃣ Load and preprocess the dataset

• Script: load_preprocess_data.ipynb
• Cleans dataset, performs mode imputation, and prepares it for analysis.

2️⃣ Perform Exploratory Data Analysis (EDA)

• Script: eda_analysis.ipynb
• Visualizes distributions, correlations, and key patterns in survival-related features.

3️⃣ Conduct Survival Analysis

• Script: survival_analysis.ipynb
• Implements Kaplan-Meier Curves, Log-Rank Tests, and Cox Proportional Hazards Model.

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5. Results

🔹 Key Findings from EDA

✔ Stage 2 is the most common tumor stage (~1700 cases).
✔ Survival probability decreases with increasing age.
✔ Patients with hormone therapy tend to have longer survival times.
✔ Chemotherapy is selectively applied, mostly in aggressive cases.
✔ Kaplan-Meier analysis confirms lower survival probabilities for Stage 3 & 4 patients.

🔹 Survival Analysis Insights

✔ Higher tumor stages correlate with lower survival probabilities.
✔ Patients with relapse-free survival ≈ overall survival represent a key subgroup for analysis.
✔ Kaplan-Meier curves show significant survival differences across treatment groups.
✔ Cox Proportional Hazards Model will be applied next to quantify risk factors.

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6. Future Work

• Expand Survival Modeling

  • Apply Cox Proportional Hazards Model to estimate the influence of multiple factors on survival time.
  • Investigate non-linear survival models (e.g., Random Survival Forests).

• Optimize Treatment Analysis

  • Assess the impact of treatment combinations on survival rates.
  • Compare survival trends in patients receiving single vs. multiple treatments.

• Develop a Predictive Model

  • Build a risk stratification tool for predicting high-risk patients.
  • Use machine learning approaches for advanced survival prediction.

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