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Artificial intelligence in radiation oncology: A review of its current status and potential application for the radiotherapy workforce

Radiography (London, England. 1995), 2021-10, Vol.27, p.S63-S68

2021

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Head and neck target delineation using a novel PET automatic segmentation algorithm

Radiotherapy and oncology, 2017-02, Vol.122 (2), p.242-247

2017

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Development and validation of RAYDOSE: a Geant4-based application for molecular radiotherapy

Physics in medicine & biology, 2013-04, Vol.58 (8), p.2491-2508

2013

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Characterization of a 2D ion chamber array for the verification of radiotherapy treatments

Physics in medicine & biology, 2005-07, Vol.50 (14), p.3361-3373

2005

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Evaluation of advanced automatic PET segmentation methods using nonspherical thin-wall inserts

Medical physics (Lancaster), 2014-02, Vol.41 (2), p.022502-n/a

2014

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Influence of cold walls on PET image quantification and volume segmentation: A phantom study

Medical physics (Lancaster), 2013-08, Vol.40 (8), p.082505-n/a

2013

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PO-2103 Demonstrating variability in radiomic analysis due to inconsistent conversion from contour to mask

Radiotherapy and oncology, 2023-05, Vol.182, p.S1887-S1888

2023

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Monte Carlo simulation and patient dosimetry for a kilovoltage cone-beam CT unit

Medical physics (Lancaster), 2009-09, Vol.36 (9), p.4156-4167

2009

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PO-0702: The use of pet texture analysis to predict lymph node metastases in patients with oesophageal cancer

Radiotherapy and oncology, 2016-04, Vol.119, p.S327-S328

2016

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PO-0933: Towards standardisation of PET auto-segmentation with the ATLAAS machine learning algorithm

Radiotherapy and oncology, 2016-04, Vol.119, p.S452-S452

2016

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Interim 18F-FDG Positron Emission Tomography/Computed Tomography During Chemoradiotherapy in the Management of Cancer Patients: a Response

Clinical oncology (Royal College of Radiologists (Great Britain)), 2019-09, Vol.31 (9), p.669-670

2019

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Improving radiotherapy quality assurance in clinical trials: assessment of target volume delineation of the pre-accrual benchmark case

British journal of radiology, 2013-04, Vol.86 (1024), p.20120398-20120398

2013

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PO-0977: ATLAAS: A model for optimal auto-segmentation of PET images in RT planning

Radiotherapy and oncology, 2015-04, Vol.115, p.S519-S520

2015

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PO-0711: Relating CT image heterogeneity to patient outcome in the SCOPE 1 oesophageal cancer trial

Radiotherapy and oncology, 2016-04, Vol.119, p.S332-S333

2016

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EP-1872: Benchmarking texture analysis for PET in oesophageal cancer

Radiotherapy and oncology, 2016-04, Vol.119, p.S883-S883

2016

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EP-1819: Plan of the Day is the optimal approach to address organ motion for cervical cancer IMRT

Radiotherapy and oncology, 2016-04, Vol.119, p.S853-S853

2016

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SU‐E‐J‐08: Assessing and Minimizing the Dose From KV Cone Beam CT to Pediatric Patients Undergoing Radiation Therapy

Medical Physics, 2013-06, Vol.40 (6), p.151-151

2013

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Superiority of Deformable Image Co-registration in the Integration of Diagnostic Positron Emission Tomography-Computed Tomography to the Radiotherapy Treatment Planning Pathway for Oesophageal Carcinoma

Clinical oncology (Royal College of Radiologists (Great Britain)), 2016-10, Vol.28 (10), p.655-662

2016

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EP-1794: Quantification of stomach movement using CBCT images

Radiotherapy and oncology, 2016-04, Vol.119, p.S840-S841

2016

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EP-1878: Feasibility of gel phantoms in MRI for the assessment of kurtosis for prostate brachytherapy

Radiotherapy and oncology, 2016-04, Vol.119, p.S887-S888

2016

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PO-0949: Automated approval of a pre trial benchmark RTTQA case. The ARISTOTLE experience

Radiotherapy and oncology, 2016-04, Vol.119, p.S461-S462

2016

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P-222Analysis of tumour contours and radiotherapy planning of "on-trial" patients undergoing chemoradiotherapy (CRT) in SCALOP trial: does pre-trial Radiotherapy Quality Assurance (RTQA) improve the quality of "on-trial" radiotherapy?

Annals of oncology, 2016-06, Vol.27 (suppl_2), p.ii64-ii65

2016

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PO-0894: Patient specific 3D dose calculation for 177Lu treatment of neuroendocrine tumours using the Raydose MC code

Radiotherapy and oncology, 2015-04, Vol.115, p.S459-S460

2015

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A novel phantom technique for evaluating the performance of PET auto-segmentation methods in delineating heterogeneous and irregular lesions

EJNMMI physics, 2015-12, Vol.2 (1), p.13-13, Article 13

2015

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Oesophageal Chemoradiotherapy in the UK—Current Practice and Future Directions

Clinical oncology (Royal College of Radiologists (Great Britain)), 2013-06, Vol.25 (6), p.368-377

2013

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Artificial intelligence in radiation oncology: A review of its current status and potential application for the radiotherapy workforce

Head and neck target delineation using a novel PET automatic segmentation algorithm

Development and validation of RAYDOSE: a Geant4-based application for molecular radiotherapy

Characterization of a 2D ion chamber array for the verification of radiotherapy treatments

Evaluation of advanced automatic PET segmentation methods using nonspherical thin-wall inserts

Influence of cold walls on PET image quantification and volume segmentation: A phantom study

PO-2103 Demonstrating variability in radiomic analysis due to inconsistent conversion from contour to mask

Monte Carlo simulation and patient dosimetry for a kilovoltage cone-beam CT unit

PO-0702: The use of pet texture analysis to predict lymph node metastases in patients with oesophageal cancer

PO-0933: Towards standardisation of PET auto-segmentation with the ATLAAS machine learning algorithm

Interim 18F-FDG Positron Emission Tomography/Computed Tomography During Chemoradiotherapy in the Management of Cancer Patients: a Response

Improving radiotherapy quality assurance in clinical trials: assessment of target volume delineation of the pre-accrual benchmark case

PO-0977: ATLAAS: A model for optimal auto-segmentation of PET images in RT planning

PO-0711: Relating CT image heterogeneity to patient outcome in the SCOPE 1 oesophageal cancer trial

EP-1872: Benchmarking texture analysis for PET in oesophageal cancer

EP-1819: Plan of the Day is the optimal approach to address organ motion for cervical cancer IMRT

SU‐E‐J‐08: Assessing and Minimizing the Dose From KV Cone Beam CT to Pediatric Patients Undergoing Radiation Therapy

Superiority of Deformable Image Co-registration in the Integration of Diagnostic Positron Emission Tomography-Computed Tomography to the Radiotherapy Treatment Planning Pathway for Oesophageal Carcinoma

EP-1794: Quantification of stomach movement using CBCT images

EP-1878: Feasibility of gel phantoms in MRI for the assessment of kurtosis for prostate brachytherapy

PO-0949: Automated approval of a pre trial benchmark RTTQA case. The ARISTOTLE experience

P-222Analysis of tumour contours and radiotherapy planning of "on-trial" patients undergoing chemoradiotherapy (CRT) in SCALOP trial: does pre-trial Radiotherapy Quality Assurance (RTQA) improve the quality of "on-trial" radiotherapy?

PO-0894: Patient specific 3D dose calculation for 177Lu treatment of neuroendocrine tumours using the Raydose MC code

A novel phantom technique for evaluating the performance of PET auto-segmentation methods in delineating heterogeneous and irregular lesions

Oesophageal Chemoradiotherapy in the UK—Current Practice and Future Directions

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