20.500.12556/DiRROS-19799 Detection and localization of hyperfunctioning parathyroid glands on [18F]fluorocholine PET/CT using deep learning – model performance and comparison to human experts In the setting of primary hyperparathyroidism (PHPT), [18F]fluorocholine PET/CT (FCH-PET) has excellent diagnostic performance, with experienced practitioners achieving 97.7% accuracy in localising hyperfunctioning parathyroid tissue (HPTT). Due to the relative triviality of the task for human readers, we explored the performance of deep learning (DL) methods for HPTT detection and localisation on FCH-PET images in the setting of PHPT. Patients and methods. We used a dataset of 93 subjects with PHPT imaged using FCH-PET, of which 74 subjects had visible HPTT while 19 controls had no visible HPTT on FCH-PET. A conventional Resnet10 as well as a novel mPETResnet10 DL model were trained and tested to detect (present, not present) and localise (upper left, lower left, upper right or lower right) HPTT. Our mPETResnet10 architecture also contained a region-of-interest masking algorithm that we evaluated qualitatively in order to try to explain the model’s decision process. Results. The models detected the presence of HPTT with an accuracy of 83% and determined the quadrant of HPTT with an accuracy of 74%. The DL methods performed statistically worse (p < 0.001) in both tasks compared to human readers, who localise HPTT with the accuracy of 97.7%. The produced region-of-interest mask, while not showing a consistent added value in the qualitative evaluation of model’s decision process, had correctly identified the foreground PET signal. Conclusions. Our experiment is the first reported use of DL analysis of FCH-PET in PHPT. We have shown that it is possible to utilize DL methods with FCH-PET to detect and localize HPTT. Given our small dataset of 93 subjects, results are nevertheless promising for further research primary hyperparathyroidism deep learning nuclear medicine primarni hiperparatoroidizem globoko učenje nuklearna medicina true false true Association of Radiology and Oncology Ljubljana Angleški jezik Slovenski jezik by Authors Neznano 2024-07-25 08:55:40 2024-07-25 08:55:40 2024-07-26 04:28:33 0000-00-00 00:00:00 2022 0 0 Soavtorji: Jan Jamsek, Anka Cuderman, Sebastijan Rep, Marko Hocevar, Tomaz Kocjan, Mojca Jensterle, Ziga Spiclin, Ziga Macek Lezaic, Filip Cvetko, Luka Lezaic; str. 440-452 no. 4 Vol. 56 2022 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 2022-01-01 616.447 1318-2099 10.2478/raon-2022-0037 119825411 https://www.radioloncol.com/index.php/ro/article/view/3890/5102 1 25740659-4a42-11ef-b487-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=27420 RAZ_Jarabek_Leon_2022.pdf RAZ_Jarabek_Leon_2022.pdf 1 CA2F3912F4BDACE16DD712DAE332C74B 13325d1278d69c7b022e59ddabe3af97dd35f1d7449fe2718e8be977ad1fda3e 4c048ffd-4a42-11ef-b487-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=27421 Onkološki inštitut Ljubljana 0 0 0