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

[1] Schwartz LH(2016)RECIST 1.1-update and clarification: from the RECIST committee Eur J Cancer. 62 132-137

[2] Litiere S(2016)RECIST 1.1 - standardisation and disease-specific adaptations: perspectives from the RECIST Working Group Eur J Cancer. 62 138-145

[3] de Vries E(2016)Comparison of the RECIST and PERCIST criteria in solid tumors: a pooled analysis and review Oncotarget. 7 27848-27854

[4] Ford R(2019)Abdominal multi-organ segmentation with organ-attention networks and statistical fusion Medical image analysis. 55 88-102

[5] Gwyther S(2019)Deep learning for segmentation of 49 selected bones in CT scans: first step in automated PET/CT-based 3D quantification of skeletal metastases Eur J Radiol. 113 89-95

[6] Mandrekar S(2017)3D skeletal uptake of 18F sodium fluoride in PET/CT images is associated with overall survival in patients with prostate cancer EJNMMI Res. 7 15-406

[7] Schwartz LH(2019)Artificial intelligence-based versus manual assessment of prostate cancer in the prostate gland: a method comparison study Clin Physiol Funct Imaging. 39 399-84

[8] Seymour L(2019)Automated quantification of reference levels in liver and mediastinal blood pool for the Deauville therapy response classification using FDG-PET/CT in Hodgkin and non-Hodgkin lymphomas Clin Physiol Funct Imaging. 39 78-undefined

[9] Litiere S(2019)The use of a proposed updated EARL harmonization of (18)F-FDG PET-CT in patients with lymphoma yields significant differences in Deauville score compared with current EARL recommendations EJNMMI Res. 9 65-undefined

[10] Ford R(undefined)undefined undefined undefined undefined-undefined

[1] Schwartz LH(2016)RECIST 1.1-update and clarification: from the RECIST committee Eur J Cancer. 62 132-137

[2] Litiere S(2016)RECIST 1.1 - standardisation and disease-specific adaptations: perspectives from the RECIST Working Group Eur J Cancer. 62 138-145

[3] de Vries E(2016)Comparison of the RECIST and PERCIST criteria in solid tumors: a pooled analysis and review Oncotarget. 7 27848-27854

[4] Ford R(2019)Abdominal multi-organ segmentation with organ-attention networks and statistical fusion Medical image analysis. 55 88-102

[5] Gwyther S(2019)Deep learning for segmentation of 49 selected bones in CT scans: first step in automated PET/CT-based 3D quantification of skeletal metastases Eur J Radiol. 113 89-95

[6] Mandrekar S(2017)3D skeletal uptake of 18F sodium fluoride in PET/CT images is associated with overall survival in patients with prostate cancer EJNMMI Res. 7 15-406

[7] Schwartz LH(2019)Artificial intelligence-based versus manual assessment of prostate cancer in the prostate gland: a method comparison study Clin Physiol Funct Imaging. 39 399-84

[8] Seymour L(2019)Automated quantification of reference levels in liver and mediastinal blood pool for the Deauville therapy response classification using FDG-PET/CT in Hodgkin and non-Hodgkin lymphomas Clin Physiol Funct Imaging. 39 78-undefined

[9] Litiere S(2019)The use of a proposed updated EARL harmonization of (18)F-FDG PET-CT in patients with lymphoma yields significant differences in Deauville score compared with current EARL recommendations EJNMMI Res. 9 65-undefined

[10] Ford R(undefined)undefined undefined undefined undefined-undefined