Research
Our research priorities reflect the interests in development and evaluation of new imaging biomarkers and automated imaging solutions to obtain functional and structural characteristics of tumor.
We are developing the innovational imaging biomarkers as supplemental biomarkers to meet new trial requirements and to provide specialty expertise for specific imaging protocols that many other core labs do not have available.
Deep Learning Postdoctoral Fellowship in Cancer Imaging and Radiology
The Quantitative Imaging Core Lab (QICL) of Department of Radiology at Northwestern University Feinberg School of Medicine is seeking a full-time Deep Learning postdoctoral fellow in Cancer Imaging and Radiology. (See more for details)
Radiology Research Fellowship in Cancer Imaging and Radiomics
The Quantitative Imaging Core Lab seeks a full-time Radiology Research Fellow. We are seeking a highly motivated individual with an accomplished background in oncologic imaging research, including experience in quantitative imaging and radiomics. Research topics include development of new imaging biomarkers for assessment of tumor response to therapy, imaging-based tumor diagnostics and classification, as well as validation and reproducibility of imaging methods and quantitative techniques. (See more for details)
KIME - image processing software
We have developed DICOM processing software (KIME) focussed on quantitative oncologic imaging. It includes tools for semi-automated segmentation and calculation of a wide range of textural (radiomic) features from CT, PET and MR images.
Recent publications:
Moataz AS Soliman, Linda C Kelahan, Michael Magnetta, Hatice Savas, Rishi Agrawal, Ryan J Avery, Pascale Aouad, Benjamin Liu, Yue Xue, Young K Chae, Riad Salem, Al B Benson, Vahid Yaghmai, Yuri S Velichko, "A Framework for Harmonization of Radiomics Data for Multicenter Studies and Clinical Trials", JCO Clin Cancer Inform. (6), e2200023, 2022
Ayman H. Gaballah Moataz Soliman, Hatice Savas, Yury S.Velichko, Yue Xue, " Primary splenic lymphoma on top of intrahepatic splenosis: A unique case report", Radiology Case Reports, 17 (8), 2850-2854, 2022
Cyra Y Kang, Samantha E Duarte, Hye Sung Kim, Eugene Kim, Jonghanne Park, Alice Daeun Lee, Yeseul Kim, Leeseul Kim, Sukjoo Cho, Yoojin Oh, Gahyun Gim, Inae Park, Dongyup Lee, Mohamed Abazeed, Yury S Velichko, Young Kwang Chae, " Artificial Intelligence-based Radiomics in the Era of Immuno-oncology", The Oncologist, 27 (6), e471-e483, 2022
Linda C Kelahan, Donald Kim, Moataz Soliman, Ryan J Avery, Hatice Savas, Rishi Agrawal, Michael Magnetta, Benjamin P Liu, Yuri S Velichko, " Role of hepatic metastatic lesion size on inter-reader reproducibility of CT-based radiomics features", European Radiology, 32 (6), 4025-4033, 2022
William Galanter Ayis Pyrros, Jorge Mario Rodriguez-Fernandez, Stephen M. Borstelmann, Judy Wawira Gichoya, Jeanne M.Horowitz, Brian Fornelli, Nasir Siddiqui, Yuri S. Velichko, Oluwasanmi Koyejo Sanmi, " Detecting Racial/Ethnic Health Disparities Using Deep Learning From Frontal Chest Radiography", Journal of the American College of Radiology, 19 (1), 184-191, 2022
Amir A Borhani, Roberta Catania, Yuri S Velichko, Stefanie Hectors, Bachir Taouli, Sara Lewis, " Radiomics of hepatocellular carcinoma: promising roles in patient selection, prediction, and assessment of treatment response", Abdominal Radiology, 46 (8), 3674-3685, 2021
Frank H. Miller, Camila Lopes Vendrami, Ahmed Gabr, Jeanne M. Horowitz, Linda C. Kelahan, Ahsun Riaz, Riad Salem, Robert J. Lewandowski, " Evolution of radioembolization in treatment of hepatocellular carcinoma: A pictorial review", Radiographics, 41, 6, 1802-1818, 2021
Yuri S Velichko, Amirhossein Mozafarykhamseh, Tugce Agirlar Trabzonlu, Zhuoli Zhang, Alfred W Rademaker, Vahid Yaghmai, " Association between the size and 3D CT-based radiomic features of breast cancer hepatic metastasis", Academic radiology, 28 (4), e93-e100, 2021
Aydin Eresen, Yu Li, Jia Yang, Junjie Shangguan, Yury Velichko, Vahid Yaghmai, Al B Benson, Zhuoli Zhang, " Preoperative assessment of lymph node metastasis in Colon Cancer patients using machine learning: a pilot study", Cancer imaging 20 (1), 1-9, 2020
Aydin Eresen, Jia Yang, Junjie Shangguan, Yu Li, Su Hu, Chong Sun, Yury Velichko, Vahid Yaghmai, Al B Benson, Zhuoli Zhang, " MRI radiomics for early prediction of response to vaccine therapy in a transgenic mouse model of pancreatic ductal adenocarcinoma", Journal of translational medicine 18 (1), 1-9, 2020
