S
Stephen S.F. Yip
Researcher at Brigham and Women's Hospital
Publications - 36
Citations - 1554
Stephen S.F. Yip is an academic researcher from Brigham and Women's Hospital. The author has contributed to research in topics: Imaging phantom & Medicine. The author has an hindex of 15, co-authored 33 publications receiving 1109 citations. Previous affiliations of Stephen S.F. Yip include Harvard University & Varian Medical Systems.
Papers
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Journal ArticleDOI
Applications and limitations of radiomics
TL;DR: This technical review will review radiomic application areas and technical issues, as well as proper practices for the designs of radiomic studies.
Journal ArticleDOI
Associations between somatic mutations and metabolic imaging phenotypes in non-small cell lung cancer
Stephen S.F. Yip,John Kim,Thibaud P. Coroller,Chintan Parmar,Emmanuel Rios Velazquez,Elizabeth Huynh,Raymond H. Mak,Hugo J.W.L. Aerts +7 more
TL;DR: Light is shed on genotype–phenotype interactions, using radiomics to capture and describe the phenotype, and may have potential for developing noninvasive imaging biomarkers for somatic mutations in non–small cell lung cancer patients.
Journal ArticleDOI
Associations between radiologist-defined semantic and automatically computed radiomic features in non-small cell lung cancer
Stephen S.F. Yip,Ying Liu,Chintan Parmar,Qian Li,Shichang Liu,Fangyuan Qu,Zhaoxiang Ye,Robert J. Gillies,Hugo J.W.L. Aerts +8 more
TL;DR: The results indicate that radiomic features may capture distinct tumor phenotypes that fail to be perceived by naked eye that semantic features do not describe and vice versa.
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Multi-field-of-view deep learning model predicts nonsmall cell lung cancer programmed death-ligand 1 status from whole-slide hematoxylin and eosin images
Lingdao Sha,Boleslaw Osinski,Irvin Ho,Timothy L. Tan,Caleb Willis,Hannah Weiss,Nike Beaubier,Brett Mahon,Tim Taxter,Stephen S.F. Yip +9 more
TL;DR: The predicted PD-L1 status from hematoxylin and eosin whole-slide images of nonsmall cell lung cancer (NSCLC) tumor samples suggests that PD- L1 expression is correlated with the morphological features of the tumor microenvironment.
Journal ArticleDOI
Comparison of texture features derived from static and respiratory-gated PET images in non-small cell lung cancer.
Stephen S.F. Yip,K McCall,Michalis Aristophanous,Aileen B. Chen,Hugo J.W.L. Aerts,Ross Berbeco +5 more
TL;DR: Texture features, blurred out by respiratory motion during 3D-PET acquisition, can be better resolved by 4D- PET imaging, suggesting that similar quantification can be obtained from all phases.