Radiomics

Hiring a Postdoctoral Fellow

Postdoctoral Fellow - Developing Clinically Interpretable Medical Imaging AI in Radiation Therapy https://recruit.jefferson.edu/psp/hcmp/EMPLOYEE/HRMS/c/HRS_HRAM_FL.HRS_CG_SEARCH_FL.GBL?Page=HRS_APP_JBPST_FL&Action=U&FOCUS=Applicant&SiteId=1&JobOpeningId=9272548&PostingSeq=1 PI: Wookjin Choi, Ph.D. <Wookjin.Choi@jefferson.edu> Assistant Professor of Radiation Oncology, Thomas Jefferson University 2 Years Responsibilities POST-DOCTORAL POSITION, DEPARTMENT OF RADIATION ONCOLOGY: Thomas Jefferson University is now accepting applications for a post-doctoral fellow in the Department of Radiation Oncology with the Choi lab. The post-doctoral position is for developing AI techniques for image-guided radiation therapy and clinical outcome prediction and decision-making using radiomics, deep learning, and other computationally intensive techniques. Trainees must have the opportunity to carry out supervised biomedical research with the primary objective of developing or extending their research skills and knowledge in preparation for an independent research career. ...

Artificial Intelligence in Radiation Oncology

Reproducible and Interpretable Spiculation Quantification for Lung Cancer Screening

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

Radiomics in Lung Cancer

Quantitative Image Analysis for Cancer Diagnosis and Radiation Therapy

Sep 17, 2018 May 21, 2018

Radiomics and Deep Learning for Lung Cancer Screening

KOCSEA Technical Symposium 2017, Invited Talk, KSEA Travel Grant

Robust Normal Lung CT Texture Features for the Prediction of Radiation-Induced Lung Disease

2017 ASTRO annual meeting http://www.redjournal.org/article/S0360-3016(17)31540-7/fulltext

Aggressive Lung Adenocarcinoma Subtype Prediction Using FDG-PET/CT Radiomics

This paper has been published in the Computational and Structural Biotechnology Journal. Preoperative 18F-FDG PET/CT and CT radiomics for identifying aggressive histopathological subtypes in early stage lung adenocarcinoma Wookjin Choi a d1, Chia-Ju Liu b 1, Sadegh Riyahi Alam a, Jung Hun Oh a, Raj Vaghjiani c, John Humm a, Wolfgang Weber b, Prasad S. Adusumilli c, Joseph O. Deasy a, Wei Lu a a Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA b Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA c Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA d Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, PA 19107, USA ...