818 KB56 KB199220252022Cuderman, AnkaCvetko, FilipHočevar, MarkoJamšek, JanJarabek, LeonJensterle Sever, MojcaKocjan, TomažLežaič, LukaMaček Ležaić, ŽigaRep, SebastijanŠpiclin, Žigaštevilka:4letnik:56str. 440-452DOI:10.2478/raon-2022-0037COBISSID_HOST:119825411ISSN:1318-2099URN:URN:NBN:SI:doc-BPIBJ3UYenCroatian Medical Association - Croatian Society of RadiologySlovenian Medical Society - Section of RadiologyRadiology and oncology (Ljubljana)deep learninggloboko učenjenuclear medicinenuklearna medicinaprimarni hiperparatoroidizemprimary hyperparathyroidismDetection and localization of hyperfunctioning parathyroid glands on 18Ffluorocholine PET/CT using deep learning – model performance and comparison to human experts|d. In the setting of primary hyperparathyroidism (PHPT), 18Ffluorocholine 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 researchTEXTznanstveno časopisjejournalsSlovenian National E-content AggregatorNational and University Library of SloveniaDruštvo radiologije in onkologije