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Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study

PLoS medicine, 2018-11, Vol.15 (11), p.e1002711

2018

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Robust Radiomics feature quantification using semiautomatic volumetric segmentation

PloS one, 2014-07, Vol.9 (7), p.e102107-e102107

2014

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Peritumoral radiomics features predict distant metastasis in locally advanced NSCLC

PloS one, 2018-11, Vol.13 (11), p.e0206108-e0206108

2018

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Associations of Radiomic Data Extracted from Static and Respiratory-Gated CT Scans with Disease Recurrence in Lung Cancer Patients Treated with SBRT

PloS one, 2017-01, Vol.12 (1), p.e0169172-e0169172

2017

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Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats

Cancer, 2018-12, Vol.124 (24), p.4633-4649

2018

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Radiographic prediction of meningioma grade by semantic and radiomic features

PloS one, 2017-11, Vol.12 (11), p.e0187908-e0187908

2017

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Deep learning classification of lung cancer histology using CT images

Scientific reports, 2021-03, Vol.11 (1), p.5471-5471, Article 5471

2021

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Comparison of texture features derived from static and respiratory-gated PET images in non-small cell lung cancer

PloS one, 2014-12, Vol.9 (12), p.e115510-e115510

2014

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Deep Learning for Fully-Automated Localization and Segmentation of Rectal Cancer on Multiparametric MR

Scientific reports, 2017-07, Vol.7 (1), p.5301-9, Article 5301

2017

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Deep convolutional neural networks to predict cardiovascular risk from computed tomography

Nature communications, 2021-01, Vol.12 (1), p.715-715, Article 715

2021

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Quantitative computed tomographic descriptors associate tumor shape complexity and intratumor heterogeneity with prognosis in lung adenocarcinoma

PloS one, 2015-03, Vol.10 (3), p.e0118261-e0118261

2015

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Associations between radiologist-defined semantic and automatically computed radiomic features in non-small cell lung cancer

Scientific reports, 2017-06, Vol.7 (1), p.3519-11, Article 3519

2017

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Robustness and reproducibility for AI learning in biomedical sciences: RENOIR

Scientific reports, 2024-01, Vol.14 (1), p.1933-13, Article 1933

2024

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Deep Learning to Assess Long-term Mortality From Chest Radiographs

JAMA network open, 2019-07, Vol.2 (7), p.e197416-e197416

2019

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Clinical variables and magnetic resonance imaging‐based radiomics predict human papillomavirus status of oropharyngeal cancer

Head & neck, 2021-02, Vol.43 (2), p.485-495

2021

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Machine Learning methods for Quantitative Radiomic Biomarkers

Scientific reports, 2015-08, Vol.5 (1), p.13087, Article 13087

2015

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Image based prognosis in head and neck cancer using convolutional neural networks: a case study in reproducibility and optimization

Scientific reports, 2023-10, Vol.13 (1), p.18176-18176, Article 18176

2023

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Edge roughness quantifies impact of physician variation on training and performance of deep learning auto-segmentation models for the esophagus

Scientific reports, 2024-01, Vol.14 (1), p.2536-2536, Article 2536

2024

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Radiomics performs comparable to morphologic assessment by expert radiologists for prediction of response to neoadjuvant chemoradiotherapy on baseline staging MRI in rectal cancer

Abdominal radiology (New York), 2020-03, Vol.45 (3), p.632-643

2020

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Artificial intelligence in cancer imaging: Clinical challenges and applications

CA: a cancer journal for clinicians, 2019-03, Vol.69 (2), p.127-157

2019

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Lentiviral vectors for cancer immunotherapy: transforming infectious particles into therapeutics

Gene therapy, 2007-06, Vol.14 (11), p.847-862

2007

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The impact of quantitative CT-based tumor volumetric features on the outcomes of patients with limited stage small cell lung cancer

Radiation oncology (London, England), 2020-01, Vol.15 (1), p.14-14, Article 14

2020

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Radiologists can visually predict mortality risk based on the gestalt of chest radiographs comparable to a deep learning network

Scientific reports, 2021-10, Vol.11 (1), p.19586-19586, Article 19586

2021

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Application of the 3D slicer chest imaging platform segmentation algorithm for large lung nodule delineation

PloS one, 2017-06, Vol.12 (6), p.e0178944-e0178944

2017

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Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study

Robust Radiomics feature quantification using semiautomatic volumetric segmentation

Peritumoral radiomics features predict distant metastasis in locally advanced NSCLC

Associations of Radiomic Data Extracted from Static and Respiratory-Gated CT Scans with Disease Recurrence in Lung Cancer Patients Treated with SBRT

Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats

Radiographic prediction of meningioma grade by semantic and radiomic features

Deep learning classification of lung cancer histology using CT images

Comparison of texture features derived from static and respiratory-gated PET images in non-small cell lung cancer

Deep Learning for Fully-Automated Localization and Segmentation of Rectal Cancer on Multiparametric MR

Standardization and quality control studies of 'real-time' quantitative reverse transcriptase polymerase chain reaction of fusion gene transcripts for residual disease detection in leukemia - A Europe Against Cancer Program

Deep convolutional neural networks to predict cardiovascular risk from computed tomography

Quantitative computed tomographic descriptors associate tumor shape complexity and intratumor heterogeneity with prognosis in lung adenocarcinoma

Associations between radiologist-defined semantic and automatically computed radiomic features in non-small cell lung cancer

Robustness and reproducibility for AI learning in biomedical sciences: RENOIR

Deep Learning to Assess Long-term Mortality From Chest Radiographs

Clinical variables and magnetic resonance imaging‐based radiomics predict human papillomavirus status of oropharyngeal cancer

Machine Learning methods for Quantitative Radiomic Biomarkers

Image based prognosis in head and neck cancer using convolutional neural networks: a case study in reproducibility and optimization

Edge roughness quantifies impact of physician variation on training and performance of deep learning auto-segmentation models for the esophagus

Radiomics performs comparable to morphologic assessment by expert radiologists for prediction of response to neoadjuvant chemoradiotherapy on baseline staging MRI in rectal cancer

Artificial intelligence in cancer imaging: Clinical challenges and applications

Lentiviral vectors for cancer immunotherapy: transforming infectious particles into therapeutics

The impact of quantitative CT-based tumor volumetric features on the outcomes of patients with limited stage small cell lung cancer

Radiologists can visually predict mortality risk based on the gestalt of chest radiographs comparable to a deep learning network

Application of the 3D slicer chest imaging platform segmentation algorithm for large lung nodule delineation

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