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Artificial intelligence, machine (deep) learning and radio(geno)mics: definitions and nuclear medicine imaging applications

European journal of nuclear medicine and molecular imaging, 2019-12, Vol.46 (13), p.2630-2637

2019

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Characterization of PET/CT images using texture analysis: the past, the present… any future?

European journal of nuclear medicine and molecular imaging, 2017, Vol.44 (1), p.151-165

2017

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Next-Generation Radiogenomics Sequencing for Prediction of EGFR and KRAS Mutation Status in NSCLC Patients Using Multimodal Imaging and Machine Learning Algorithms

Molecular imaging and biology, 2020-08, Vol.22 (4), p.1132-1148

2020

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Gray-level invariant Haralick texture features

PloS one, 2019-02, Vol.14 (2), p.e0212110-e0212110

2019

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Study of low-dose PET image recovery using supervised learning with CycleGAN

PloS one, 2020-09, Vol.15 (9), p.e0238455-e0238455

2020

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Radiomics in PET/CT: More Than Meets the Eye?

Journal of Nuclear Medicine, 2017-03, Vol.58 (3), p.365-366

2017

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FDG PET/CT radiomics for predicting the outcome of locally advanced rectal cancer

European journal of nuclear medicine and molecular imaging, 2018-03, Vol.45 (3), p.365-375

2018

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Using machine learning and an ensemble of methods to predict kidney transplant survival

PloS one, 2019-01, Vol.14 (1), p.e0209068-e0209068

2019

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Prediction of recurrence after surgery in colorectal cancer patients using radiomics from diagnostic contrast-enhanced computed tomography: a two-center study

European radiology, 2022-01, Vol.32 (1), p.405-414

2022

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Automated clear cell renal carcinoma grade classification with prognostic significance

PloS one, 2019-10, Vol.14 (10), p.e0222641-e0222641

2019

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The added value of PSMA PET/MR radiomics for prostate cancer staging

European journal of nuclear medicine and molecular imaging, 2022-01, Vol.49 (2), p.527-538

2022

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Artificial intelligence: Deep learning in oncological radiomics and challenges of interpretability and data harmonization

Physica medica, 2021-03, Vol.83, p.108-121

2021

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Head and Neck Tumor Segmentation and Outcome Prediction: Third Challenge, HECKTOR 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings

1st Edition 2023, 2023

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MRI-derived radiomics: methodology and clinical applications in the field of pelvic oncology

British journal of radiology, 2019-12, Vol.92 (1104), p.20190105-20190105

2019

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FDG PET/CT texture analysis for predicting the outcome of lung cancer treated by stereotactic body radiation therapy

European journal of nuclear medicine and molecular imaging, 2016-07, Vol.43 (8), p.1453-1460

2016

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A transfer learning approach to facilitate ComBat-based harmonization of multicentre radiomic features in new datasets

PloS one, 2021-07, Vol.16 (7), p.e0253653-e0253653

2021

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Enhancing histopathological image classification of invasive ductal carcinoma using hybrid harmonization techniques

Scientific reports, 2023-11, Vol.13 (1), p.20014-20014, Article 20014

2023

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Are quantitative features of lung nodules reproducible at different CT acquisition and reconstruction parameters?

PloS one, 2020-10, Vol.15 (10), p.e0240184

2020

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Towards guidelines to harmonize textural features in PET: Haralick textural features vary with image noise, but exposure-invariant domains enable comparable PET radiomics

PloS one, 2020-03, Vol.15 (3), p.e0229560-e0229560

2020

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[18F]FDG PET radiomics to predict disease-free survival in cervical cancer: a multi-scanner/center study with external validation

European journal of nuclear medicine and molecular imaging, 2021-10, Vol.48 (11), p.3432-3443

2021

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“Fuzzy” radiomics: the way forward for nuclear medicine imaging applications?

European journal of nuclear medicine and molecular imaging, 2023-05, Vol.50 (6), p.1558-1559

Hatt, Mathieu

2023

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Preliminary study on discriminating HER2 2+ amplification status of breast cancers based on texture features semi-automatically derived from pre-, post-contrast, and subtraction images of DCE-MRI

PloS one, 2020-06, Vol.15 (6), p.e0234800-e0234800

2020

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Automatic emphysema detection using weakly labeled HRCT lung images

PloS one, 2018-10, Vol.13 (10), p.e0205397-e0205397

2018

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Head and Neck Tumor Segmentation and Outcome Prediction: Second Challenge, HECKTOR 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings

1, 2022

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PET functional volume delineation: a robustness and repeatability study

European journal of nuclear medicine and molecular imaging, 2011-04, Vol.38 (4), p.663-672

2011

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Artificial intelligence, machine (deep) learning and radio(geno)mics: definitions and nuclear medicine imaging applications

Characterization of PET/CT images using texture analysis: the past, the present… any future?

Next-Generation Radiogenomics Sequencing for Prediction of EGFR and KRAS Mutation Status in NSCLC Patients Using Multimodal Imaging and Machine Learning Algorithms

Gray-level invariant Haralick texture features

Study of low-dose PET image recovery using supervised learning with CycleGAN

Radiomics in PET/CT: More Than Meets the Eye?

FDG PET/CT radiomics for predicting the outcome of locally advanced rectal cancer

Using machine learning and an ensemble of methods to predict kidney transplant survival

Prediction of recurrence after surgery in colorectal cancer patients using radiomics from diagnostic contrast-enhanced computed tomography: a two-center study

Automated clear cell renal carcinoma grade classification with prognostic significance

The added value of PSMA PET/MR radiomics for prostate cancer staging

Artificial intelligence: Deep learning in oncological radiomics and challenges of interpretability and data harmonization

Head and Neck Tumor Segmentation and Outcome Prediction: Third Challenge, HECKTOR 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings

MRI-derived radiomics: methodology and clinical applications in the field of pelvic oncology

FDG PET/CT texture analysis for predicting the outcome of lung cancer treated by stereotactic body radiation therapy

A transfer learning approach to facilitate ComBat-based harmonization of multicentre radiomic features in new datasets

Enhancing histopathological image classification of invasive ductal carcinoma using hybrid harmonization techniques

Are quantitative features of lung nodules reproducible at different CT acquisition and reconstruction parameters?

Towards guidelines to harmonize textural features in PET: Haralick textural features vary with image noise, but exposure-invariant domains enable comparable PET radiomics

[18F]FDG PET radiomics to predict disease-free survival in cervical cancer: a multi-scanner/center study with external validation

“Fuzzy” radiomics: the way forward for nuclear medicine imaging applications?

Preliminary study on discriminating HER2 2+ amplification status of breast cancers based on texture features semi-automatically derived from pre-, post-contrast, and subtraction images of DCE-MRI

Automatic emphysema detection using weakly labeled HRCT lung images

Head and Neck Tumor Segmentation and Outcome Prediction: Second Challenge, HECKTOR 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings

PET functional volume delineation: a robustness and repeatability study

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