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OncoPath

Specialized Oncological and Pathological Research Tools for jamovi

Project Status: Active GitHub release GitHub issues

Overview

OncoPath is a specialized jamovi module designed specifically for oncological and pathological research. It provides comprehensive patient follow-up visualization tools that are essential for clinical research, treatment response analysis, and patient timeline tracking.

This module is part of the ClinicoPath ecosystem, offering specialized tools for oncology and pathology research that complement the broader statistical analysis capabilities of the main ClinicoPath module.

Features

🏊‍♂️ Swimmer Plot Analysis

Comprehensive swimmer plots for visualizing patient timelines, clinical events, milestones, and treatment responses.

  • Patient Timeline Visualization: Create professional swimmer plots using enhanced ggswim package integration
  • Multi-dimensional Data Support: Visualize clinical events, milestones, treatment responses, and adverse events
  • Enhanced Data Validation: Robust input validation with comprehensive error handling
  • Flexible Timeline Display: Customizable patient journey visualization with event overlays
  • Clinical Research Integration: Designed specifically for oncological clinical trial reporting

Documentation: Swimmer Plot Guide

🌊 Waterfall Plot Analysis

Treatment response analysis with RECIST criteria, creating waterfall and spider plots for tumor response visualization.

  • Treatment Response Visualization: Create comprehensive waterfall and spider plots for tumor response analysis
  • RECIST Criteria Support: Built-in Response Evaluation Criteria In Solid Tumors (RECIST) guidelines
  • Dual Data Input: Supports both raw tumor measurements and pre-calculated percentage changes
  • Clinical Metrics: Automated calculation of ORR (Overall Response Rate), DCR (Disease Control Rate), and person-time metrics
  • Publication Ready: Professional visualization suitable for clinical publications and presentations
  • Longitudinal Analysis: Spider plots for tracking individual patient responses over time

Documentation: Waterfall Plot Guide

🔬 IHC Heterogeneity Analysis

Statistical analysis of immunohistochemistry marker heterogeneity for pathology research.

  • Immunohistochemistry Analysis: Statistical analysis of IHC marker heterogeneity
  • Multi-marker Support: Comprehensive evaluation of multiple biomarkers
  • Statistical Validation: Robust statistical methods for heterogeneity assessment
  • Pathology Research: Specialized tools for immunohistochemical studies

📊 Diagnostic Test Meta-Analysis

Comprehensive meta-analysis tools for diagnostic test accuracy studies, specifically designed for pathology and AI algorithm validation.

  • Bivariate Meta-Analysis: Advanced bivariate random-effects modeling using the Reitsma method
  • HSROC Analysis: Hierarchical Summary ROC curve analysis for diagnostic accuracy
  • Meta-Regression: Covariate analysis to explore heterogeneity sources
  • Publication Bias Assessment: Comprehensive bias detection and visualization
  • Forest and SROC Plots: Publication-ready visualizations for diagnostic test accuracy
  • AI Algorithm Validation: Designed for validating AI/ML diagnostic algorithms in pathology
  • Biomarker Studies: Comprehensive synthesis of diagnostic biomarker accuracy studies

Installation

Prerequisites

  • jamovi version 2.7.2 or higher
  1. Open jamovi
  2. Click on the “Modules” (⊞) button in the top-right
  3. Select “jamovi library”
  4. Search for “OncoPath”
  5. Click “Install”

Method 2: Sideload Installation

  1. Download the latest .jmo file from releases
  2. In jamovi, click “Modules” (⊞) → “Sideload”
  3. Select the downloaded .jmo file

Method 3: R Installation 

# Install from GitHub
remotes::install_github("sbalci/OncoPath")

Quick Start

Swimmer Plot Example

  1. Load your patient timeline data with columns for:

    • Patient ID
    • Start time
    • End time
    • Events (optional)
    • Response data (optional)

  2. Navigate to OncoPath → Patient Follow-Up Plots → Swimmer Plot

  3. Configure your variables and customize the visualization

Waterfall Plot Example

  1. Prepare your treatment response data with:

    • Patient ID
    • Response variable (percentage change or raw measurements)
    • Time points (for longitudinal analysis)
    • Group variables (optional)

  2. Navigate to OncoPath → Patient Follow-Up Plots → Treatment Response Analysis

  3. Select RECIST criteria options and customize your analysis

Documentation

Sample Data

OncoPath includes sample datasets to help you get started:

  • Swimmer Plot Analysis: swimmerplot_sample.omv
  • Waterfall Plot: waterfall_percentage_basic.omv
  • Waterfall and Spider Plot: waterfall_raw_longitudinal.omv

Access these datasets through the jamovi interface: Open → Data Library → OncoPath

Requirements

Core Dependencies

  • R (≥ 4.1.0)
  • jmvcore (≥ 0.8.5)
  • ggplot2
  • dplyr
  • rlang

Specialized Dependencies

  • ggswim: Enhanced swimmer plot functionality
  • mada: Meta-analysis of diagnostic accuracy studies
  • metafor: Meta-regression and advanced meta-analysis methods
  • pROC: ROC curve analysis for diagnostic tests
  • survival & survminer: Survival analysis and visualization
  • lubridate: Date/time handling
  • RColorBrewer: Professional color schemes
  • gridExtra: Advanced plot layouts
  • boot, dcurves, Hmisc, rms, timeROC: Advanced statistical methods

Use Cases

Clinical Research

  • Clinical Trial Reporting: Patient timelines and treatment responses
  • Longitudinal Studies: Disease progression and treatment effects over time
  • Oncology Research: Tumor response evaluation following RECIST guidelines
  • Diagnostic Accuracy Studies: Meta-analysis of biomarker and diagnostic test performance
  • AI Algorithm Validation: Systematic review and meta-analysis of AI-based diagnostic tools

Pathology Research

  • Biomarker Validation: Comprehensive meta-analysis of diagnostic biomarkers
  • IHC Studies: Statistical analysis of immunohistochemistry heterogeneity
  • Systematic Reviews: Synthesis of diagnostic test accuracy across multiple studies
  • Method Comparison: Evaluation of different diagnostic methods and techniques

Publication Support

  • Manuscript Figures: Publication-ready visualizations with professional styling
  • Conference Presentations: Clear, informative plots for academic presentations
  • Regulatory Submissions: Standardized reporting formats for regulatory agencies
  • Meta-Analysis Reports: Comprehensive forest plots, SROC curves, and funnel plots

Contributing

We welcome contributions! Please see our Contributing Guidelines for details.

Areas for Contribution

  • Additional visualization options
  • Enhanced RECIST criteria support
  • New clinical event types
  • Documentation improvements
  • Bug reports and feature requests

Support

Citation

If you use OncoPath in your research, please cite:

Serdar Balci (2025). OncoPath: Specialized Oncological and Pathological Research Tools for jamovi.
R package version 0.0.32. https://github.com/sbalci/OncoPath

And the main ClinicoPath project:

Serdar Balci (2025). ClinicoPath jamovi Module. doi:10.5281/zenodo.3997188
[R package]. Retrieved from https://github.com/sbalci/ClinicoPathJamoviModule

License

GPL (>= 2) - see LICENSE file for details.

OncoPath is part of the ClinicoPath ecosystem:

Developed by Serdar Balci

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Acknowledgements

OncoPath builds upon the excellent work of many R package developers, including:

  • ggswim for swimmer plot functionality
  • ggplot2 for graphics infrastructure
  • survminer for survival visualizations
  • mada and metafor for meta-analysis tools
  • The jamovi team for creating an accessible statistical platform
  • The broader R community for continuous innovation

Special thanks to the oncology and pathology research communities for feedback and feature requests that have shaped this module’s development.