T
Thibaud P. Coroller
Researcher at Brigham and Women's Hospital
Publications - 31
Citations - 2646
Thibaud P. Coroller is an academic researcher from Brigham and Women's Hospital. The author has contributed to research in topics: Lung cancer & KRAS. The author has an hindex of 15, co-authored 28 publications receiving 1847 citations. Previous affiliations of Thibaud P. Coroller include Maastricht University & Novartis.
Papers
More filters
Journal ArticleDOI
CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma
Thibaud P. Coroller,Thibaud P. Coroller,Patrick Grossmann,Patrick Grossmann,Ying Hou,Emmanuel Rios Velazquez,Ralph T.H. Leijenaar,Gretchen Hermann,Philippe Lambin,Benjamin Haibe-Kains,Benjamin Haibe-Kains,Raymond H. Mak,Hugo J.W.L. Aerts,Hugo J.W.L. Aerts +13 more
TL;DR: In this article, the authors evaluated computed-tomography (CT) radiomic features for their capability to predict distant metastasis (DM) for lung adenocarcinoma patients.
Journal ArticleDOI
Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study
Ahmed Hosny,Chintan Parmar,Thibaud P. Coroller,Patrick Grossmann,Roman Zeleznik,Avnish Kumar,Johan Bussink,Robert J. Gillies,Raymond H. Mak,Hugo J.W.L. Aerts +9 more
TL;DR: Evidence that deep learning networks may be used for mortality risk stratification based on standard-of-care CT images from NSCLC patients is provided and the biological basis of the captured phenotypes as being linked to cell cycle and transcriptional processes is presented.
Journal ArticleDOI
Deep Learning Predicts Lung Cancer Treatment Response from Serial Medical Imaging
Yiwen Xu,Ahmed Hosny,Ahmed Hosny,Roman Zeleznik,Roman Zeleznik,Chintan Parmar,Thibaud P. Coroller,Idalid Franco,Raymond H. Mak,Hugo J.W.L. Aerts,Hugo J.W.L. Aerts +10 more
TL;DR: It is demonstrated that deep learning can integrate imaging scans at multiple timepoints to improve clinical outcome predictions and AI-based noninvasive radiomics biomarkers can have a significant impact in the clinic given their low cost and minimal requirements for human input.
Journal ArticleDOI
Somatic Mutations Drive Distinct Imaging Phenotypes in Lung Cancer.
Emmanuel Rios Velazquez,Chintan Parmar,Ying Liu,Thibaud P. Coroller,Gisele Cruz,Olya Stringfield,Zhaoxiang Ye,G. Mike Makrigiorgos,Fiona M. Fennessy,Raymond H. Mak,Robert J. Gillies,John Quackenbush,Hugo J.W.L. Aerts +12 more
TL;DR: It is argued that somatic mutations drive distinct radiographic phenotypes that can be predicted by radiomics, which has implications for the use of imaging-based biomarkers in the clinic, as applied noninvasively, repeatedly, and at low cost.
Journal ArticleDOI
Radiomic phenotype features predict pathological response in non-small cell lung cancer.
Thibaud P. Coroller,Vishesh Agrawal,Vivek Narayan,Ying Hou,Patrick Grossmann,Stephanie W. Lee,Raymond H. Mak,Hugo J.W.L. Aerts +7 more
TL;DR: Predictive radiomic features for pathological response are identified, although no conventional features were significantly predictive, which demonstrates that radiomics can provide valuable clinical information, and performed better than conventional imaging features.