Chintan Parmar
Chintan Parmar
PathAI (Current) | [Novartis, Harvard-DFCI-BWH, ISI-Kolkata] (Previous)
Verified email at dfci.harvard.edu - Homepage
Title
Cited by
Cited by
Year
Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach
HJWL Aerts, ER Velazquez, RTH Leijenaar, C Parmar, P Grossmann, ...
Nature communications 5 (1), 1-9, 2014
29922014
Computational radiomics system to decode the radiographic phenotype
JJM Van Griethuysen, A Fedorov, C Parmar, A Hosny, N Aucoin, ...
Cancer research 77 (21), e104-e107, 2017
14762017
Artificial intelligence in radiology
A Hosny, C Parmar, J Quackenbush, LH Schwartz, HJWL Aerts
Nature Reviews Cancer 18 (8), 500-510, 2018
9862018
Machine learning methods for quantitative radiomic biomarkers
C Parmar, P Grossmann, J Bussink, P Lambin, HJWL Aerts
Scientific reports 5 (1), 1-11, 2015
6482015
Robust radiomics feature quantification using semiautomatic volumetric segmentation
C Parmar, E Rios Velazquez, R Leijenaar, M Jermoumi, S Carvalho, ...
PloS one 9 (7), e102107, 2014
4232014
Radiomic feature clusters and prognostic signatures specific for lung and head & neck cancer
C Parmar, RTH Leijenaar, P Grossmann, ER Velazquez, J Bussink, ...
Scientific reports 5 (1), 1-10, 2015
3532015
Stability of FDG-PET radiomics features: an integrated analysis of test-retest and inter-observer variability
RTH Leijenaar, S Carvalho, ER Velazquez, WJC Van Elmpt, C Parmar, ...
Acta oncologica 52 (7), 1391-1397, 2013
3462013
Exploratory study to identify radiomics classifiers for lung cancer histology
W Wu, C Parmar, P Grossmann, J Quackenbush, P Lambin, J Bussink, ...
Frontiers in oncology 6, 71, 2016
2662016
Radiomic machine-learning classifiers for prognostic biomarkers of head and neck cancer
C Parmar, P Grossmann, D Rietveld, MM Rietbergen, P Lambin, ...
Frontiers in oncology 5, 272, 2015
2602015
Somatic mutations drive distinct imaging phenotypes in lung cancer
ER Velazquez, C Parmar, Y Liu, TP Coroller, G Cruz, O Stringfield, Z Ye, ...
Cancer research 77 (14), 3922-3930, 2017
2102017
Deep learning for lung cancer prognostication: a retrospective multi-cohort radiomics study
A Hosny, C Parmar, TP Coroller, P Grossmann, R Zeleznik, A Kumar, ...
PLoS medicine 15 (11), e1002711, 2018
2072018
Defining the biological basis of radiomic phenotypes in lung cancer
P Grossmann, O Stringfield, N El-Hachem, MM Bui, ER Velazquez, ...
Elife 6, e23421, 2017
1872017
Volumetric CT-based segmentation of NSCLC using 3D-Slicer
ER Velazquez, C Parmar, M Jermoumi, RH Mak, A van Baardwijk, ...
Scientific reports 3 (1), 1-7, 2013
1722013
Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers
S Trebeschi, SG Drago, NJ Birkbak, I Kurilova, AM Cǎlin, AD Pizzi, ...
Annals of Oncology 30 (6), 998-1004, 2019
1662019
Deep Learning for Fully-Automated Localization and Segmentation of Rectal Cancer on Multiparametric MR
HJWLA Stefano Trebeschi, Joost JM van Griethuysen, Doenja MJ Lambregts, Max ...
Scientific Reports 7 (1), 5301, 2017
158*2017
Deep learning predicts lung cancer treatment response from serial medical imaging
Y Xu, A Hosny, R Zeleznik, C Parmar, T Coroller, I Franco, RH Mak, ...
Clinical Cancer Research 25 (11), 3266-3275, 2019
1562019
Associations between somatic mutations and metabolic imaging phenotypes in non–small cell lung cancer
SSF Yip, J Kim, TP Coroller, C Parmar, ER Velazquez, E Huynh, RH Mak, ...
Journal of Nuclear Medicine 58 (4), 569-576, 2017
1162017
Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014; 5: 4006
HJ Aerts, ER Velazquez, RT Leijenaar, C Parmar, P Grossmann, ...
762014
Associations of radiomic data extracted from static and respiratory-gated CT scans with disease recurrence in lung cancer patients treated with SBRT
E Huynh, TP Coroller, V Narayan, V Agrawal, J Romano, I Franco, ...
PloS one 12 (1), e0169172, 2017
742017
Data analysis strategies in medical imaging
C Parmar, JD Barry, A Hosny, J Quackenbush, HJWL Aerts
Clinical cancer research 24 (15), 3492-3499, 2018
722018
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