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Impact of experimental design on PET radiomics in predicting somatic mutation status

SSF Yip, C Parmar, J Kim, E Huynh, RH Mak, HJWL Aerts

European journal of radiology 97, 8-15, 2017

Exigences : US National Institutes of Health

Disponibles quelque part : 26

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

Exigences : US National Institutes of Health

[HTML] nature.com

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), 4006, 2014

Exigences : US National Institutes of Health, Dutch Cancer Society

[HTML] nih.gov

Artificial intelligence in radiology

A Hosny, C Parmar, J Quackenbush, LH Schwartz, HJWL Aerts

Nature Reviews Cancer 18 (8), 500-510, 2018

Exigences : US National Institutes of Health

[HTML] nature.com

Machine learning methods for quantitative radiomic biomarkers

C Parmar, P Grossmann, J Bussink, P Lambin, HJWL Aerts

Scientific reports 5 (1), 1-11, 2015

Exigences : US National Institutes of Health, Dutch Cancer Society

[HTML] plos.org

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

Exigences : US National Institutes of Health, Dutch Cancer Society

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

Exigences : US National Institutes of Health

[PDF] plos.org

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

Exigences : US National Institutes of Health

Radiomic feature clusters and prognostic signatures specific for lung and head & neck cancer

C Parmar, RTH Leijenaar, P Grossmann, E Rios Velazquez, J Bussink, ...

Scientific reports 5 (1), 11044, 2015

Exigences : US National Institutes of Health, Dutch Cancer Society

[HTML] sciencedirect.com

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

Exigences : US National Institutes of Health, Cancer Research UK

[PDF] tandfonline.com

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

Exigences : US National Institutes of Health, Dutch Cancer Society

[HTML] frontiersin.org

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

Exigences : US National Institutes of Health, Netherlands Organisation for Scientific …

[PDF] frontiersin.org

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

Exigences : US National Institutes of Health, Netherlands Organisation for Scientific …

Somatic mutations drive distinct imaging phenotypes in lung cancer

E Rios Velazquez, C Parmar, Y Liu, TP Coroller, G Cruz, O Stringfield, ...

Cancer research 77 (14), 3922-3930, 2017

Exigences : US National Institutes of Health

[PDF] elifesciences.org

Defining the biological basis of radiomic phenotypes in lung cancer

P Grossmann, O Stringfield, N El-Hachem, MM Bui, E Rios Velazquez, ...

elife 6, e23421, 2017

Exigences : US National Institutes of Health, Dutch Cancer Society

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

Exigences : US National Institutes of Health

Volumetric CT-based segmentation of NSCLC using 3D-Slicer

ER Velazquez, C Parmar, M Jermoumi, RH Mak, A Van Baardwijk, ...

Scientific reports 3 (1), 3529, 2013

Exigences : US National Institutes of Health, Dutch Cancer Society

Deep convolutional neural networks to predict cardiovascular risk from computed tomography

R Zeleznik, B Foldyna, P Eslami, J Weiss, I Alexander, J Taron, C Parmar, ...

Nature communications 12 (1), 715, 2021

Exigences : US National Institutes of Health, American Heart Association, German …

[HTML] snmjournals.org

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

Exigences : US National Institutes of Health