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Bone scan index: A new biomarker of bone metastasis in patients with prostate cancer

International journal of urology, 2017-09, Vol.24 (9), p.668-673

2017

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Deep learning for segmentation of 49 selected bones in CT scans: First step in automated PET/CT-based 3D quantification of skeletal metastases

European journal of radiology, 2019-04, Vol.113, p.89-95

2019

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RECOMIA—a cloud-based platform for artificial intelligence research in nuclear medicine and radiology

EJNMMI physics, 2020-08, Vol.7 (1), p.51-51, Article 51

2020

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Computer-aided diagnosis system for bone scintigrams from Japanese patients: importance of training database

Annals of nuclear medicine, 2012-10, Vol.26 (8), p.622-626

2012

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Artificial intelligence-aided CT segmentation for body composition analysis: a validation study

European radiology experimental, 2021-03, Vol.5 (1), p.11-11, Article 11

2021

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Artificial neural network retrained to detect myocardial ischemia using a Japanese multicenter database

Annals of nuclear medicine, 2018-06, Vol.32 (5), p.303-310

2018

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AI-based detection of lung lesions in [18F]FDG PET-CT from lung cancer patients

EJNMMI physics, 2021-03, Vol.8 (1), p.32-32, Article 32

2021

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“Global” cardiac atherosclerotic burden assessed by artificial intelligence-based versus manual segmentation in 18F-sodium fluoride PET/CT scans: Head-to-head comparison

Journal of nuclear cardiology, 2022-10, Vol.29 (5), p.2531-2539

2022

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Convolutional neural network-based automatic heart segmentation and quantitation in 123I-metaiodobenzylguanidine SPECT imaging

EJNMMI research, 2021-10, Vol.11 (1), p.105-105, Article 105

2021

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Automated artificial intelligence-based analysis of skeletal muscle volume predicts overall survival after cystectomy for urinary bladder cancer

European radiology experimental, 2021-11, Vol.5 (1), p.50-50, Article 50

2021

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Aortic wall segmentation in 18F-sodium fluoride PET/CT scans: Head-to-head comparison of artificial intelligence-based versus manual segmentation

Journal of nuclear cardiology, 2022-08, Vol.29 (4), p.2001-2010

2022

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Computer-Assisted Interpretation of Planar Whole-Body Bone Scans

Journal of Nuclear Medicine, 2008-12, Vol.49 (12), p.1958-1965

2008

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Freely available convolutional neural network-based quantification of PET/CT lesions is associated with survival in patients with lung cancer

EJNMMI physics, 2022-02, Vol.9 (1), p.6-6, Article 6

2022

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Diagnostic Performance of Artificial Neural Network for Detecting Ischemia in Myocardial Perfusion Imaging

Circulation Journal, 2015/06/25, Vol.79(7), pp.1549-1556

2015

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Bone Scan Index as a prognostic imaging biomarker during androgen deprivation therapy

EJNMMI research, 2014-10, Vol.4 (1), p.58-58, Article 58

2014

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Improved quantification of small hearts for gated myocardial perfusion imaging

European journal of nuclear medicine and molecular imaging, 2013-08, Vol.40 (8), p.1163-1170

2013

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Investigation of computer-aided diagnosis system for bone scans: a retrospective analysis in 406 patients

Annals of nuclear medicine, 2014-05, Vol.28 (4), p.329-339

2014

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Reducing the small-heart effect in pediatric gated myocardial perfusion single-photon emission computed tomography

Journal of nuclear cardiology, 2017-08, Vol.24 (4), p.1378-1388

2017

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Artificial Intelligence-Based Organ Delineation for Radiation Treatment Planning of Prostate Cancer on Computed Tomography

Advances in radiation oncology, 2024-03, Vol.9 (3), p.101383-101383, Article 101383

2024

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Quality of planar whole-body bone scan interpretations—a nationwide survey

European Journal of Nuclear Medicine, 2008-08, Vol.35 (8), p.1464-1472

2008

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The use of a proposed updated EARL harmonization of 18F-FDG PET-CT in patients with lymphoma yields significant differences in Deauville score compared with current EARL recommendations

EJNMMI research, 2019-07, Vol.9 (1), p.1-7, Article 65

2019

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Improved Classifications of Planar Whole-Body Bone Scans Using a Computer-Assisted Diagnosis System: A Multicenter, Multiple-Reader, Multiple-Case Study

Journal of Nuclear Medicine, 2009-03, Vol.50 (3), p.368-375

2009

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Enhanced diagnostic accuracy for quantitative bone scan using an artificial neural network system: a Japanese multi-center database project

EJNMMI research, 2013-12, Vol.3 (1), p.83-83

2013

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Assessment of the bone scan index in a randomized placebo-controlled trial of tasquinimod in men with metastatic castration-resistant prostate cancer (mCRPC)

Urologic oncology, 2014-11, Vol.32 (8), p.1308-1316

2014

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Reporting nuclear cardiology: a joint position paper by the European Association of Nuclear Medicine (EANM) and the European Association of Cardiovascular Imaging (EACVI)

European heart journal cardiovascular imaging, 2015-03, Vol.16 (3), p.272-279

2015

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Bone scan index: A new biomarker of bone metastasis in patients with prostate cancer

Deep learning for segmentation of 49 selected bones in CT scans: First step in automated PET/CT-based 3D quantification of skeletal metastases

RECOMIA—a cloud-based platform for artificial intelligence research in nuclear medicine and radiology

Computer-aided diagnosis system for bone scintigrams from Japanese patients: importance of training database

Artificial intelligence-aided CT segmentation for body composition analysis: a validation study

Artificial neural network retrained to detect myocardial ischemia using a Japanese multicenter database

AI-based detection of lung lesions in [18F]FDG PET-CT from lung cancer patients

“Global” cardiac atherosclerotic burden assessed by artificial intelligence-based versus manual segmentation in 18F-sodium fluoride PET/CT scans: Head-to-head comparison

Convolutional neural network-based automatic heart segmentation and quantitation in 123I-metaiodobenzylguanidine SPECT imaging

Automated artificial intelligence-based analysis of skeletal muscle volume predicts overall survival after cystectomy for urinary bladder cancer

Aortic wall segmentation in 18F-sodium fluoride PET/CT scans: Head-to-head comparison of artificial intelligence-based versus manual segmentation

Computer-Assisted Interpretation of Planar Whole-Body Bone Scans

Freely available convolutional neural network-based quantification of PET/CT lesions is associated with survival in patients with lung cancer

Diagnostic Performance of Artificial Neural Network for Detecting Ischemia in Myocardial Perfusion Imaging

Bone Scan Index as a prognostic imaging biomarker during androgen deprivation therapy

Improved quantification of small hearts for gated myocardial perfusion imaging

Investigation of computer-aided diagnosis system for bone scans: a retrospective analysis in 406 patients

Reducing the small-heart effect in pediatric gated myocardial perfusion single-photon emission computed tomography

Artificial Intelligence-Based Organ Delineation for Radiation Treatment Planning of Prostate Cancer on Computed Tomography

Quality of planar whole-body bone scan interpretations—a nationwide survey

The use of a proposed updated EARL harmonization of 18F-FDG PET-CT in patients with lymphoma yields significant differences in Deauville score compared with current EARL recommendations

Improved Classifications of Planar Whole-Body Bone Scans Using a Computer-Assisted Diagnosis System: A Multicenter, Multiple-Reader, Multiple-Case Study

Enhanced diagnostic accuracy for quantitative bone scan using an artificial neural network system: a Japanese multi-center database project

Assessment of the bone scan index in a randomized placebo-controlled trial of tasquinimod in men with metastatic castration-resistant prostate cancer (mCRPC)

Reporting nuclear cardiology: a joint position paper by the European Association of Nuclear Medicine (EANM) and the European Association of Cardiovascular Imaging (EACVI)

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