Spiculation 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:

Clinically-Interpretable Radiomics

Wed, 29 Jun 2022 21:01:32 -0400

MICCAI'22 Paper | CMPB'21 Paper | CIRDataset

This library serves as a one-stop solution for analyzing datasets using clinically-interpretable radiomics (CIR) in cancer imaging (https://github.com/choilab-jefferson/CIR). The primary motivation for this comes from our collaborators in radiology and radiation oncology inquiring about the importance of clinically-reported features in state-of-the-art deep learning malignancy/recurrence/treatment response prediction algorithms. Previous methods have performed such prediction tasks but without robust attribution to any clinically reported/actionable features (see extensive literature on the sensitivity of attribution methods to hyperparameters). This motivated us to curate datasets by annotating clinically-reported features at the voxel/vertex level on public datasets (using our published advanced mathematical algorithms) and relating these to prediction tasks (bypassing the “flaky” attribution schemes). With the release of these comprehensively-annotated datasets, we hope that previous malignancy prediction methods can also validate their explanations and provide clinically-actionable insights. We also provide strong end-to-end baselines for extracting these hard-to-compute clinically-reported features and using these in different prediction tasks.

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.

Quantitative Cancer Image Analysis

Sun, 03 Nov 2019 01:17:37 -0400

Radiomics in Lung Cancer

Mon, 01 Oct 2018 14:04:27 -0400

Interpretable Spiculation Quantification for Lung Cancer Screening

Tue, 11 Sep 2018 14:02:03 -0400

UKC2018 Aug 4, 2018

MSKCC Postdoctoral Research Symposium Sep 28, 2018

https://twitter.com/arxiv_org/status/1034746650089021445

Presented at MICCAI ShapeMI Workshop https://shapemi.github.io/program/