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SCIENCE AND RESEARCH

Publications

Our science team is committed to pioneering breakthroughs that enhance early detection, enable precise diagnosis, and facilitate personalized treatment for cancer care. We work tirelessly to shape the future of healthcare, improving patient outcomes and contributing to cutting-edge advancements in the field of medical imaging. Explore our publications as we redefine the boundaries of what's possible in medicine for diagnostic and therapeutic applications.

Publications 

Title: Comparative effectiveness of standard versus AI-assisted PET/CT reading workflow for pre-treatment lymphoma staging – Multi-institutional reader study evaluation.

Frood R, Willaime JMY, Miles B, Chambers G, Al-Chalabi H, Ali T, Hougham N, Brooks N, Petrides G, Naylor M, Ward D, Sulkin T, Chaytor R, Strouhal P, Patel C, Scarsbrook A. Comparative effectiveness of standard versus AI-assisted PET/CT reading workflow for pre-treatment lymphoma staging – Multi-institutional reader study evaluation [Conference Presentation Abstract, M3-SSNMMI03-2]. RSNA 2022, Chicago, IL, United States.

 

Title: Reporter confidence of an AI-assisted PET/CT reading workflow in pre treatment assessment of high-grade lymphoma : multi-centre reader study 

Frood R, Willaime JMY, Miles B, Brooks N, Strouhal P, Patel C, Scarsbrook A. Reporter confidence of an AI-assisted PET/CT reading workflow in pre-treatment assessment of high-grade lymphoma : multi-centre reader study [Poster Presentation]. ECR 2023, Vienna, Austria.

Title : Impact of training dataset size on technical performance of a deep learning model for detection and quantification of lymphomatous disease on 18F-FDG PET/CT

 Ionescu GV, Frood R, Scarsbrook AF, Willaime JMY. Impact of training dataset size on technical performance of a deep learning model for detection and quantification of lymphomatous disease on 18F-FDG PET/CT study [Poster Presentation]. SNMMI 2023, Chicago, IL, United States. In: J Nucl Med 2023; 64 (Supplement 1): 1069. https://jnm.snmjournals.org/content/64/supplement_1/P1069

 

 

Title:  Non-Uniqueness of Multiexponential Time-Activity Curves

Balfour D, Sage J, Willaime JMY and Boukerroui D. Non-Uniqueness of Multiexponential Time-Activity Curves in Few-Timepoint Theranostic Workflows Can Increase Dosimetric Error [Conference Presentation, OP-907]. EANM 2023, Vienna, Austria. In: EANM’23 Abstract Book Congress, Eur J Nucl Med Mol Imaging 50 (Suppl 1), 1–898 (2023). https://doi.org/10.1007/s00259-023-06333-x

                                              

Title:  Uncertainty analysis of AI-generated contours for molecular radiotherapy dosimetry

Looney P, McQuinlan Y, Sage J and Willaime JMY. Uncertainty analysis of AI-generated contours for molecular radiotherapy dosimetry [Poster Presentation]. The British Institute of Radiology AI in NM&PET meeting, 2023 October 9, Oxford, UK.

Investigation into the risk of population bias in deep learning autocontouring.

McQuinlan Y, Brouwer CL, Lin Z, Gan Y, Sung Kim J, van Elmpt W, Gooding M J. An investigation into the risk of population bias in deep learning autocontouring. Radiotherapy and Oncology 186, 109747.

 

Analytic calculations and synthetic shapes for validation of quantitative contour comparison software.

Boukerroui D, Osorio EV, Brunenberg E, Gooding MJ. Analytic calculations and synthetic shapes for validation of quantitative contour comparison software. Physics and Imaging in Radiation Oncology 26, 100436. https://doi.org/10.1016/j.phro.2023.100436

EXPLORE MIRADA'S KNOWLEDGE CENTER OF NEWS, OPINION, CASE STUDIES AND PRODUCT INFORMATION

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NCIMI case study:  AI & Lymphoma

Learn more about our work on automatic PET/CT reporting

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News: Announcing the launch of TeleDaas PLLC

TeleDaaS, PLLC, a pioneering medical practice that will revolutionize the radiopharmaceutical industry

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