BACKGROUND: To determine whether artificial intelligence (AI) processed PET/CT images of reduced by one-third of 18-F-FDG activity compared to the standard injected dose, were non-inferior to native scans and if so to assess the potential impact of commercialization. MATERIALS AND METHODS: SubtlePET™ AI was introduced in a PET/CT center in Italy. Eligible patients referred for 18F-FDG PET/CT were prospectively enrolled. Administered 18F-FDG was reduced to two-thirds of standard dose. Patients underwent one low-dose CT and two sequential PET scans; "PET-processed" with reduced dose and standard acquisition time, and "PET-native" with an elapsed time to simulate standard acquisition time and dose. PET-processed images were reconstructed using SubtlePET™. PET-native images were defined as the standard of reference. The datasets were anonymized and independently evaluated in random order by four blinded readers. The evaluation included subjective image quality (IQ) assessment, lesion detectability, and assessment of business benefits. RESULTS: From February to April 2020, 61 patients were prospectively enrolled. Subjective IQ was not significantly different between datasets (4.62±0.23, p=0.237) for all scanner models, with "almost perfect" inter-reader agreement. There was no significant difference between datasets in lesions' detectability, target lesion mean SUVmax value, and liver mean SUVmean value (182.75/181.75 [SD:0.71], 9.8/11.4 [SD:1.13], 2.1/1.9 [SD:0.14] respectively). No false-positive lesions were reported in PET-processed examinations. Agreed SubtlePET™ price per examination was 15-20% of FDG savings. CONCLUSION: This is the first real-world study to demonstrate the non-inferiority of AI processed 18F-FDG PET/CT examinations obtained with 66% standard dose and a methodology to define the AI solution price.
BACKGROUND: To determine whether artificial intelligence (AI) processed PET/CT images of reduced by one-third of 18-F-FDG activity compared to the standard injected dose, were non-inferior to native scans and if so to assess the potential impact of commercialization. MATERIALS AND METHODS: SubtlePET™ AI was introduced in a PET/CT center in Italy. Eligible patients referred for 18F-FDG PET/CT were prospectively enrolled. Administered 18F-FDG was reduced to two-thirds of standard dose. Patients underwent one low-dose CT and two sequential PET scans; "PET-processed" with reduced dose and standard acquisition time, and "PET-native" with an elapsed time to simulate standard acquisition time and dose. PET-processed images were reconstructed using SubtlePET™. PET-native images were defined as the standard of reference. The datasets were anonymized and independently evaluated in random order by four blinded readers. The evaluation included subjective image quality (IQ) assessment, lesion detectability, and assessment of business benefits. RESULTS: From February to April 2020, 61 patients were prospectively enrolled. Subjective IQ was not significantly different between datasets (4.62±0.23, p=0.237) for all scanner models, with "almost perfect" inter-reader agreement. There was no significant difference between datasets in lesions' detectability, target lesion mean SUVmax value, and liver mean SUVmean value (182.75/181.75 [SD:0.71], 9.8/11.4 [SD:1.13], 2.1/1.9 [SD:0.14] respectively). No false-positive lesions were reported in PET-processed examinations. Agreed SubtlePET™ price per examination was 15-20% of FDG savings. CONCLUSION: This is the first real-world study to demonstrate the non-inferiority of AI processed 18F-FDG PET/CT examinations obtained with 66% standard dose and a methodology to define the AI solution price.
Authors: Yasasvi Tadavarthi; Valeria Makeeva; William Wagstaff; Henry Zhan; Anna Podlasek; Neil Bhatia; Marta Heilbrun; Elizabeth Krupinski; Nabile Safdar; Imon Banerjee; Judy Gichoya; Hari Trivedi Journal: Radiol Artif Intell Date: 2022-02-02
Authors: Ruiyao Ma; Jiaxi Hu; Hasan Sari; Song Xue; Clemens Mingels; Marco Viscione; Venkata Sai Sundar Kandarpa; Wei Bo Li; Dimitris Visvikis; Rui Qiu; Axel Rominger; Junli Li; Kuangyu Shi Journal: Eur J Nucl Med Mol Imaging Date: 2022-07-11 Impact factor: 10.057
Authors: Stefaan Vandenberghe; Nicolas A Karakatsanis; Maya Abi Akl; Jens Maebe; Suleman Surti; Rudi A Dierckx; Daniel A Pryma; Sadek A Nehmeh; Othmane Bouhali; Joel S Karp Journal: Eur J Nucl Med Mol Imaging Date: 2022-09-30 Impact factor: 10.057
Authors: Christina P W Cox; Daniëlle M E van Assema; Frederik A Verburg; Tessa Brabander; Mark Konijnenberg; Marcel Segbers Journal: EJNMMI Res Date: 2021-07-19 Impact factor: 3.138
Authors: Abolfazl Mehranian; Scott D Wollenweber; Matthew D Walker; Kevin M Bradley; Patrick A Fielding; Kuan-Hao Su; Robert Johnsen; Fotis Kotasidis; Floris P Jansen; Daniel R McGowan Journal: Eur J Nucl Med Mol Imaging Date: 2021-07-28 Impact factor: 9.236