Nodule on Qualia Radiomics

Longitudinal CBCT radiomics in Lung Cancer supported by Varian Medical Systems Inc.

Fri, 10 Feb 2023 15:23:27 -0500

Varian will support my research project entitled “Longitudinal CBCT radiomics analysis for lung cancer radiotherapy response and prognosis prediction” with $230,000 over 2 years. This is the first research grant from Varian to the Department of Radiation Oncology, Sidney Kimmel Medical College at Thomas Jefferson University. This project can potentially impact the clinical practice of lung cancer patients by using standard imaging modalities (CBCT and 4D-CBCT) to provide early prediction of prognosis and toxicity.

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.

Lung Cancer Screening Radiomics

Wed, 08 Jun 2022 11:32:36 -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/choilab-jefferson/LungCancerScreeningRadiomics

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, 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

Artificial Intelligence in Radiation Oncology

Fri, 15 Oct 2021 00:49:35 -0400

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.

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/

Radiomics and Deep Learning for Lung Cancer Screening

Sun, 12 Nov 2017 08:12:54 -0500

KOCSEA Technical Symposium 2017, Invited Talk, KSEA Travel Grant

Current Projects - Sep 13, 2016

Wed, 14 Sep 2016 10:30:00 -0400

Computer Aided Detection of Pulmonary Nodules in CT Scans

Fri, 03 Oct 2014 00:43:00 -0400

Pulmonary Nodule Detection using Voxel Classification in Lung CT images (Korean)

Thu, 02 Oct 2014 01:41:10 -0400

Lung structure segmentation and nodule detection based on 3D block analysis in CT image (Korean)

Thu, 02 Oct 2014 01:38:33 -0400

Lung Volume Segmentation using Graph-Cut (Korean)

Thu, 02 Oct 2014 01:36:49 -0400

Computer-aided Detection of Pulmonary Nodules using Genetic Programming (Korean)

Thu, 02 Oct 2014 01:22:27 -0400

Computer aided detection of pulmonary nodules using genetic programming

Thu, 02 Oct 2014 01:16:49 -0400

Computer-aided Detection of Pulmonary Nodules using Genetic Programming

Thu, 02 Oct 2014 01:15:12 -0400

2010 IEEE ICIP

Automatic detection of pulmonary nodules in lung CT images

Thu, 02 Oct 2014 00:53:56 -0400

Previous Works

Thu, 02 Oct 2014 00:21:19 -0400

Automatic Pulmonary Nodule Detection

https://www.youtube.com/watch?v=1rbdBf_-USo

Three dimensional shape reconstruction from auto-focused microscopic image