RTpipeline

The Big Data Radiotherapy Pipeline

From raw clinical exports to research-ready datasets in one command.

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Overview

RTpipeline is a comprehensive, research-grade pipeline that transforms raw DICOM radiotherapy exports into analysis-ready data. It bridges the technical gap between clinical Treatment Planning Systems (TPS) and statistical/ML analysis by automating:

• DICOM Organization - Groups scattered files into patient courses
• AI Segmentation - TotalSegmentator (~100 standardized structures) + custom nnU-Net models
• DVH Extraction - Comprehensive dose-volume metrics
• Radiomics - IBSI-compliant features with robustness assessment
• Quality Control - Automated checks and audit reports

Who Is This For?

Audience	Value Proposition
PhD Students	Spend your PhD on science, not reinventing DICOM parsing
Clinical Researchers	Minimal coding—drag & drop in Web UI, get Excel tables
Multi-Center Consortia	Shared configs ensure identical preprocessing at every site

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Key Features

1. Standardized Anatomy via AI

Run TotalSegmentator on every CT to get consistent structure definitions:

Input: "Heart", "hrt", "Coeur", "cardiac"  (inconsistent)
Output: "heart"  (standardized for every patient)

2. Systematic CT Cropping

Normalize field-of-view using anatomical landmarks for comparable metrics:

Before: V20Gy = 500cc / 18,000cc = 2.8% (long scan)
After:  V20Gy = 500cc / 12,000cc = 4.2% (standardized FOV)

3. Robustness-Aware Radiomics

NTCV perturbation chains (Zwanenburg et al., 2019) identify stable features:

• Noise injection (scanner variability)
• Translation (positioning uncertainty)
• Contour randomization (inter-observer variability)
• Volume adaptation (segmentation uncertainty)

Features with ICC ≥ 0.90 and CoV ≤ 10% classified as robust.

4. Analysis-Ready Outputs

_RESULTS/
├── dvh_metrics.xlsx      # Dmean, D95%, V20Gy for every structure
├── radiomics_ct.xlsx     # 1000+ IBSI-compliant features
├── case_metadata.xlsx    # Clinical tags, scanner info
└── qc_reports.xlsx       # Quality control summary

5. High-Performance Computing

Designed for modern hardware with automatic optimization:

• GPU Acceleration: CUDA-accelerated deep learning for segmentation
• Smart Parallelization: Automatically scales to available CPU cores
• Resource Management: Adaptive worker scaling prevents memory overflows
• Speed: Process hundreds of patients in hours, not days

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Quick Start

Option 1: Interactive Docker Setup (Recommended)

curl -sSL https://raw.githubusercontent.com/kstawiski/rtpipeline/main/setup_docker_project.sh | bash

Option 2: Docker Compose

# Create folders
mkdir -p Input Output Logs

# Start pipeline + Web UI
docker-compose up -d

# Open http://localhost:8080

Option 3: Google Colab

Try it in the cloud with free GPU access:

• Part 1: GPU Segmentation
• Part 2: CPU Analysis

Option 4: Local Installation

git clone https://github.com/kstawiski/rtpipeline.git
cd rtpipeline
snakemake --cores all --use-conda

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Documentation

Full documentation at kstawiski.github.io/rtpipeline

Section	Description
Getting Started	From zero to first analyzed patient
Web UI Guide	Drag-and-drop interface
Output Format	Data table schemas
Case Studies	Real-world research examples
Reproducibility	Methods templates for publications
Radiomics Robustness	NTCV perturbation methodology

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Case Studies

1. NTCP Modeling for Rectal Toxicity

Build dose-response models from standardized DVH metrics. Learn more →

2. Radiomics Signature Development

Create robust imaging biomarkers with NTCV perturbation assessment. Learn more →

3. Multi-Center Data Harmonization

Federated learning with identical preprocessing at every institution. Learn more →

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Architecture

┌─────────────────┐     ┌──────────────────────────────────┐     ┌─────────────────┐
│     EXTRACT     │     │           TRANSFORM              │     │      LOAD       │
│                 │     │                                  │     │                 │
│  • DICOM CT     │     │  • Structure harmonization       │     │  • DVH tables   │
│  • RTSTRUCT     │ ──► │  • TotalSegmentator              │ ──► │  • Radiomics    │
│  • RTDOSE       │     │  • Systematic cropping           │     │  • Metadata     │
│  • RTPLAN       │     │  • Robustness analysis           │     │  • QC reports   │
│                 │     │                                  │     │                 │
└─────────────────┘     └──────────────────────────────────┘     └─────────────────┘

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Citation

If you use RTpipeline in your research, please cite:

@software{rtpipeline,
  title = {RTpipeline: Automated Radiotherapy DICOM Processing Pipeline},
  author = {Stawiski, Konrad},
  url = {https://github.com/kstawiski/rtpipeline},
  year = {2025}
}

Also cite the underlying tools:

• TotalSegmentator: Wasserthal et al., Radiology: AI (2023)
• PyRadiomics: van Griethuysen et al., Cancer Research (2017)
• IBSI: Zwanenburg et al., Radiology (2020)

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License

MIT License - see LICENSE for details.

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Note: Model weights for TotalSegmentator are downloaded automatically. Custom nnU-Net models must be provided separately. See documentation for details.