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:

Reproducible and Interpretable Spiculation Quantification for Lung Cancer Screening

Tue, 17 Nov 2020 20:24:09 -0500

Choi, W., Nadeem, S., Alam, S. R., Deasy, J. O., Tannenbaum, A., & Lu, W. (2020). Reproducible and Interpretable Spiculation Quantification for Lung Cancer Screening. Computer Methods and Programs in Biomedicine, 105839. https://doi.org/10.1016/j.cmpb.2020.105839

Source codes: https://github.com/choilab-jefferson/LungCancerScreeningRadiomics

Highlights

A novel interpretable spiculation feature is presented, computed using the area distortion metric from spherical conformal (angle-preserving) parameterization.
A simple one-step feature and prediction model is introduced which only uses our interpretable features (size, spiculation, lobulation, vessel/wall attachment) and has the added advantage of using weak-labeled training data.

Shape on Qualia Radiomics

qradiomics — Radiomics Research CLI

Reproducible and Interpretable Spiculation Quantification for Lung Cancer Screening