J
John H. Lewis
Researcher at Cedars-Sinai Medical Center
Publications - 33
Citations - 329
John H. Lewis is an academic researcher from Cedars-Sinai Medical Center. The author has contributed to research in topics: Imaging phantom & Image registration. The author has an hindex of 8, co-authored 33 publications receiving 182 citations. Previous affiliations of John H. Lewis include University of California, Los Angeles & Brigham and Women's Hospital.
Papers
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Journal ArticleDOI
Deep learning approaches using 2D and 3D convolutional neural networks for generating male pelvic synthetic computed tomography from magnetic resonance imaging
TL;DR: In this article, a 2D and 3D convolutional neural networks (CNNs) were used to generate a male pelvic sCT using a T1-weighted MR image and compare their performance.
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Deep learning-based radiomic features for improving neoadjuvant chemoradiation response prediction in locally advanced rectal cancer.
Jie Fu,Xinran Zhong,Ning Li,Ritchell van Dams,John H. Lewis,Kyunghyun Sung,Ann C. Raldow,Jing Jin,S Qi +8 more
TL;DR: DL-based features extracted from pre-treatment DWIs achieved significantly better classification performance compared with handcrafted features for predicting nCRT response in LARC patients.
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Treatment effect prediction for sarcoma patients treated with preoperative radiotherapy using radiomics features from longitudinal diffusion-weighted MRIs.
Yu Gao,Anusha Kalbasi,William Hsu,Dan Ruan,Jie Fu,Jiaxin Shao,Minsong Cao,Chenyang Wang,Fritz C. Eilber,Nicholas M. Bernthal,Susan V. Bukata,Sarah M. Dry,Scott D. Nelson,Mitchell Kamrava,John H. Lewis,Daniel A. Low,Michael L. Steinberg,Peng Hu,Yingli Yang +18 more
TL;DR: It is found that mean ADC, or delta ADC,or radiomics features alone was not sufficient for response prediction, and including delta radiomics Features of mid- or post- treatment relative to the baseline can optimize the prediction of treatment effect score, a pathologic and clinical endpoint.
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Generation of abdominal synthetic CTs from 0.35T MR images using generative adversarial networks for MR-only liver radiotherapy.
Jie Fu,Kamal Singhrao,Minsong Cao,Victoria Y. Yu,Anand P. Santhanam,Yingli Yang,Minghao Guo,Ann C. Raldow,Dan Ruan,John H. Lewis +9 more
TL;DR: It is demonstrated that abdominal sCT images generated by both cGAN and cycleGAN achieved accurate dose calculation for 8 liver radiotherapy plans.
Journal ArticleDOI
Generation of abdominal synthetic CTs from 0.35T MR images using generative adversarial networks for MR-only liver radiotherapy
Jie Fu,Kamal Singhrao,Minsong Cao,Victoria Y. Yu,Anand P. Santhanam,Yingli Yang,Minghao Guo,Ann C. Raldow,Dan Ruan,John H. Lewis +9 more
TL;DR: In this article, two deep learning models, the conditional generative adversarial network (cGAN) and the cycle-consistent GAN (cycleGAN), were used to generate accurate abdominal synthetic CT (sCT) images from 0.35T MR images for MR-only liver radiotherapy.