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License: MIT · Python: 3.11+ · Version: 0.9.0 · Repo: choilab-jefferson/qradiomics Active successor for three earlier Choi Lab radiomics codebases. The C++/MATLAB pipelines in taznux/radiomics-tools, taznux/lung-image-analysis, and choilab-jefferson/LungCancerScreeningRadiomics are superseded by this repo. The feature extractors are now in qradiomics.feature.rtools (Python ITK port, numerically exact to the C++ binary). New work should land here. 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: ...

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. ...