Projects on Qualia Radiomics

qradiomics — Radiomics Research CLI

Sun, 17 May 2026 20:31:21 -0400

License: MIT · Python: 3.11+ · Repo: choilab-jefferson/qradiomics

Radiomics research CLI. qr does two things equally well:

Atomic tasks — convert DICOM, extract features, merge clinical, fit a model. Each is a single command, files in / files out.
Workflow assembly — generate, mutate, scaffold, and run multi-step pipelines from those atomic tasks. Default executor is Nextflow (per-patient parallel + cache + HPC); Prefect is the secondary executor; inline is the small-cohort fallback.

The canonical radiomics data flow has four stages — data → image → features → modeling — and one qr workflow plan call instantiates the whole chain:

PathCNN

Fri, 10 Jun 2022 15:59:11 -0400

Interpretable convolutional neural networks for survival prediction and pathway analysis applied to glioblastoma

Pathway image: Grid structure conversion for biological array data (a non-grid structured format) for CNNs.
Interpretation of the CNN model using GradCAM.

Source code: https://github.com/mskspi/PathCNN

Jung Hun Oh, Wookjin Choi, Euiseong Ko, Mingon Kang, Allen Tannenbaum, Joseph O Deasy, PathCNN: interpretable convolutional neural networks for survival prediction and pathway analysis applied to glioblastoma, Bioinformatics, Volume 37, Issue Supplement_1, July 2021, Pages i443–i450, https://doi.org/10.1093/bioinformatics/btab285

Lung Cancer Screening Radiomics

Fri, 10 Jun 2022 15:50:37 -0400

A comprehensive framework for lung cancer screening radiomics using LIDC-IDRI and LUNGx dataset.

Data preprocessing - download data, conversion, etc.
Radiomics feature extraction including spiculation features
AutoML model building and validation

Source code https://github.com/taznux/LungCancerScreeningRadiomics

Wookjin Choi, Jung Hun Oh, Sadegh Riyahi, Chia-Ju Liu, Feng Jiang, Wengen Chen, Charles White, Andreas Rimner, James G. Mechalakos, Joseph O. Deasy, and Wei Lu, “Radiomics analysis of pulmonary nodules in low-dose CT for early detection of lung cancer”, Medical Physics, Vol. 45, No. 4, pp. 1537-1549, April 2018. https://doi.org/10.1002/mp.12820
Wookjin Choi, Saad Nadeem, Sadegh Riyahi, Joseph O. Deasy, Allen Tannenbaum, Wei Lu, “Reproducible and Interpretable Spiculation Quantification for Lung Cancer Screening.” Computer methods and programs in biomedicine. 200 2021. https://doi.org/10.1016/j.cmpb.2020.105839

Open source projects

Sat, 27 Aug 2016 13:58:18 -0400

qradiomics

Open-source source for the qradiomics.com site and content. Source code: https://github.com/choilab-jefferson/qradiomics

Lung Cancer Screening Radiomics

A full system for lung cancer screening radiomics using Lung Image Analysis Framework, Radiomics Tools, and other external tools with Docker.

PathCNN

Interpretable convolutional neural networks for survival prediction and pathway analysis applied to glioblastoma

Radiomics Tools

Radiomic feature analysis tools in C++ and Python with ITK, Simple ITK. Ruffus pipeline structure Image processing tools for radiomics analysis, e.g. DICOM and DICOM-RT handling, contour manipulation, automatic segmentation, etc. Interfaces for invoking MATLAB and external executables, e.g. CERR, plastimatch and etc. 3D and 2D shape, intensity, and texture (GLCM, GLRM) features

Lung Image Analysis Framwork

Sat, 27 Aug 2016 03:31:31 -0400

A basic framework for pulmonary nodule detection and characterization in CT https://github.com/taznux/lung-image-analysis

Tested on LIDC-IDRI dataset (https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI)

LIDC XML parsing
Simple lung segmentation, nodule detection, and feature extraction algorithms
Evaluation of nodule segmentation, detection, and characterization by LIDC XML annotations

written in Matlab by Wookjin Choi and Ji-Seok Yoon

This framework is the essential parts of the following papers.

Wookjin Choi, Tae-Sun Choi, “Automated Pulmonary Nodule Detection based on Three-dimensional Shape-based Feature Descriptor”, Computer Methods and Programs in Biomedicine, Vol. 113, No. 1, January 2014, pp. 37–54, doi: http://dx.doi.org/10.1016/j.cmpb.2013.08.015
Wookjin Choi, Tae-Sun Choi, “Automated Pulmonary Nodule Detection System in Computed Tomography Images: A Hierarchical Block Classification Approach”, Entropy, Vol. 15, No. 2, pp. 507-523, February 2013, doi: http://dx.doi.org/10.3390/e15020507
Wookjin Choi, Tae-Sun Choi, “Genetic Programming-based feature transform and classification for the automatic detection of pulmonary nodules on computed tomography images”, Information Sciences, Vol. 212, pp. 57-78, December 2012, doi:http://dx.doi.org/10.1016/j.ins.2012.05.008

Radiomics Tools

Sat, 23 Jul 2016 23:00:14 -0400

Image processing tools and ruffus based pipeline for radiomics feature analysis

Github repository
https://github.com/taznux/radiomics-tools/
First release 0.1 with OSX binary
https://github.com/taznux/radiomics-tools/releases/tag/release/0.1
Test dataset - https://wiki.cancerimagingarchive.net/display/Public/SPIE-AAPM+Lung+CT+Challenge

Publications

Wookjin Choi, Jung Hun Oh, Sadegh Riyahi, Chia-Ju Liu, Feng Jiang, Wengen Chen, Charles White, Andreas Rimner, James G. Mechalakos, Joseph O. Deasy, and Wei Lu, “Radiomics analysis of pulmonary nodules in low-dose CT for early detection of lung cancer”, Medical Physics, Vol. 45, No. 4, pp. 1537-1549, April 2018. https://doi.org/10.1002/mp.12820
Wookjin Choi, Sadegh Riyahi, Seth J. Kligerman, Chia-Ju Liu, James G. Mechalakos and Wei Lu, “Technical Note: Identification of Normal Lung CT Texture Features Robust to Tumor Size for the Prediction of Radiation-Induced Lung Disease”, International Journal of Medical Physics, Clinical Engineering and Radiation Oncology, Vol.7 No.3, Paper ID 86485, pp. 330-338, August 2018. doi:10.4236/ijmpcero.2018.73027
Sadegh Riyahi, Wookjin Choi, Chia-Ju Liu, Hualiang Zhong, Abraham J Wu, James G Mechalakos, Wei Lu, “Quantifying local tumor morphological changes with Jacobian map for prediction of pathologic tumor response to chemo-radiotherapy in locally advanced esophageal cancer”, Physics in Medicine and Biology, Vol. 63, No. 14, 145020 (13pp), July 2018. doi:10.1088/1361-6560/aacd22

QuaLIA CAD

Mon, 11 Jan 2016 21:29:41 -0500

https://github.com/taznux/qualia

QuaLIA (Quantitative Lung Image Analysis)

Open-source framework for Computer-Aided Detection/Diagnosis
OSX/Java/ITK/VTK/Gradle

QuaLIA CAD Download Source code Download Binary