Radiomics_visualization_Software

Here I created a repository for installing and running the code. I put the instructions step by step: This software allows you to process, visualize, and analyze radiomics datasets without programming knowledge.

System Requirements

Operating System: Windows, macOS, or Linux Python Version: 3.8 or later Required Libraries:

• tkinter
• pandas
• numpy
• scikit-learn
• matplotlib
• seaborn
• Pillow

Installation Steps

Step 1: Install Python Download Python from https://www.python.org/downloads/. During installation: Check the box: "Add Python to PATH". Select the Customize installation option and ensure pip is selected. Verify Python installation: Open a terminal or command prompt (you can just write cmd in the search box) Type: python --version If installed correctly, this shows the installed Python version.

Step 2: Install Required Libraries

1. Open a terminal or command prompt.
2. Run the following command to install the necessary libraries:

pip install pandas numpy scikit-learn matplotlib seaborn pillow

3. Ensure the installation completes without errors.

Step 3: Save the Software Code

Copy the provided code into a text editor (e.g., Notepad). Save it as radiomics_analysis.py. Example:

C:\Users\<YourName>\Documents\radiomics_analysis.py

Step 5: Run the Software

1. Open a terminal or command prompt.
2. Navigate to the folder containing radiomics_analysis.py. For example:

cd C:\Users\<YourName>\Documents

3. Run the program:

python radiomics_analysis.py

4. The software will launch with a user-friendly graphical interface.

How to Use the Software

First make it full size

Step 1: Load Your Data

Click Browse to select a radiomics dataset (CSV file). Confirm successful data loading in the popup message.

Step 2: Configure Settings

Variance Threshold: Remove features with low variation across samples (default: 0.01). Correlation Threshold: Remove features that are highly correlated (default: 0.9). Additional Features to Drop: Checkboxes appear if the dataset contains fields like Age, Sex, or BMI.

Step 3: Analyze Data

Select a Metric for Analysis: Feature Importance: Rank features by their contribution to survival prediction. Mutual Information: Identify features most related to survival outcomes. Click Process Data (You need to click only once to the Process Data) you can later change the metric.

Step 4: Visualize Results

Show Results: View a bar chart ranking features by importance or mutual information. Show Scatter Plot: Examine the relationship between the most significant feature and survival time, with a regression line and value. Show Heatmap: Visualize the correlation between top features and survival time.

Step 5: Save Processed Data

Click Save Processed Data to export the dataset after feature selection as a CSV file.