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References
- Fraser WD. Hyperparathyroidism. Lancet 2009; 374: 145-58. doi: 10.1016/ s0140-6736(09)60507-9
- Grimelius L, Akerström G, Johansson H, Bergström R. Anatomy and histopathology of human parathyroid glands. Pathol Annu 1981; 16(Pt 2): 1-24. PMID: 7036057
- Cuderman A, Senica K, Rep S, Hocevar M, Kocjan T, Sever, et al. 18F-Fluorocholine PET/CT in primary hyperparathyroidism: superior diagnostic performance to conventional scintigraphic imaging for localization of hyperfunctioning parathyroid glands. J Nucl Med 2019; 61: 577-83. doi: 10.2967/jnumed.119.229914
- Lezaic L, Rep S, Sever MJ, Kocjan T, Hocevar M, Fettich J. 18F-Fluorocholine PET/CT for localization of hyperfunctioning parathyroid tissue in primary hyperparathyroidism: a pilot study. Eur J Nucl Med Mol Imaging 2014; 41: 2083-9. doi: 10.1007/s00259-014-2837-0
- Graves CE, Hope TA, Kim J, Pampaloni MH, Kluijfhout W, Seib CD, et al. Superior sensitivity of 18F-fluorocholine: PET localization in primary hyperparathyroidism. Surgery 2022; 171: 47-54. doi: 10.1016/j.surg.2021.05.056
- Michaud L, Balogova S, Burgess A, Ohnona J, Huchet V, Kerrou K, et al. A pilot comparison of 18F-fluorocholine PET/CT, ultrasonography and 123I/99mTc-sestaMIBI dual-phase dual-isotope scintigraphy in the preoperative localization of hyperfunctioning parathyroid glands in primary or secondary hyperparathyroidism. Medicine 2015; 94: e1701. doi: 10.1097/ md.0000000000001701
- Kluijfhout WP, Vorselaars WM, van den Berk SA, Vriens MR, Borel Rinkes IH, Valk GD, et al. Fluorine-18 fluorocholine PET-CT localizes hyperparathyroidism in patients with inconclusive conventional imaging. Nucl Med Commun 2016; 37: 1246-52. doi: 10.1097/mnm.0000000000000595
- Kluijfhout WP, Pasternak JD, Drake FT, Beninato T, Gosnell JE, Shen WT, et al. Use of PET tracers for parathyroid localization: a systematic review and meta-analysis. Langenbecks Arch Surg 2016; 401: 925-35. doi: 10.1007/ s00423-016-1425-0
- Thanseer N, Bhadada SK, Sood A, Mittal BR, Behera A, Gorla A K R, et al. Comparative effectiveness of ultrasonography, 99mTc-sestamibi, and 18F-fluorocholine PET/CT in detecting parathyroid adenomas in patients with primary hyperparathyroidism. Clin Nucl Med 2017; 42: e491-7. doi: 10.1097/rlu.0000000000001845
- Whitman J, Allen IE, Bergsland EK, Suh I, Hope TA. Assessment and comparison of 18F-Fluorocholine PET and 99mTc-sestamibi scans in identifying parathyroid adenomas: a metaanalysis. J Nucl Med 2021; 62: 1285-91. doi: 10.2967/jnumed.120.257303
- Beheshti M, Hehenwarter L, Paymani Z, Rendl G, Imamovic L, Rettenbacher R, et al. 18F-Fluorocholine PET/CT in the assessment of primary hyperparathyroidism compared with 99mTc-MIBI or 99mTc-tetrofosmin SPECT/CT: a prospective dual-centre study in 100 patients. Eur J Nucl Med Mol Imaging 2018; 45: 1762-71. doi: 10.1007/s00259-018-3980-9
- Broos WAM, Wondergem M, Knol RJJ, Van der Zant FM. Parathyroid imaging with 18F-fluorocholine PET/CT as a first-line imaging modality in primary hyperparathyroidism: a retrospective cohort study. EJNMMI Res 2019; 9: 72. doi: 10.1186/s13550-019-0544-3
- Hope TA, Graves CE, Calais J, Ehman EC, Johnson GB, Thompson D, et al. Accuracy of 18 F-fluorocholine PET for the detection of parathyroid adenomas: prospective single-center study. J Nucl Med 2021; 62: 1511-6. doi: /10.2967/jnumed.120.256735
- Rep S, Hocevar M, Vaupotic J, Zdesar U, Zaletel K, Lezaic L. 18F-choline PET/ CT for parathyroid scintigraphy: significantly lower radiation exposure of patients in comparison to conventional nuclear medicine imaging approaches. J Radiol Prot 2018; 38: 343-56. doi: 10.1088/1361-6498/aaa86f
- Li Y, Sixou B, Peyrin F. A review of the deep learning methods for medical images super resolution problems. IRBM 2021; 42: 120-33. doi: 10.1016/j. irbm.2020.08.004
- Yang W, Zhang X, Tian Y, Wang W, Xue J-H, Liao Q. Deep learning for single image super-resolution: a brief review. IEEE Trans Multimedia 2019; 21: 3106-21. doi: 10.1109/tmm.2019.2919431
- Wang L, Chen W, Yang W, Bi F, Yu FR. A state-of-the-art review on image synthesis with generative adversarial networks. IEEE Access 2020; 8: 63514-37. doi: 10.1109/access.2020.2982224
- Liu B, Liu J. Overview of image denoising based on deep learning. J Phys Conf Ser 2019; 1176: 022010. doi: 10.1088/1742-6596/1176/2/022010
- Chartrand G, Cheng PM, Vorontsov E, Drozdzal M, Turcotte S, Pal CJ, et al. Deep learning: a primer for radiologists. RadioGraphics 2017; 37: 2113-31. doi: 10.1148/rg.2017170077
- Al-Saffar AAM, Tao H, Talab MA. Review of deep convolution neural network in image classification. In: 2017 International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunication. IEEE 2017. p. 26-31. doi: 10.1109/icramet.2017.8253139
- Minaee S, Boykov Y, Porikli F, Plaza A, Kehtarnavaz N, Terzopoulos D. Image segmentation using deep learning: a survey. [Internet]. arXiv: 2001.05566 2020. Available from: https://doi.org/10.48550/arXiv.2001.05566
- Jiao L, Zhang F, Liu F, Yang S, Li L, Feng Z, et al. A survey of deep learning-based object detection. [Internet]. arXiv: 2019. Available from: http://arxiv.org/abs/1907.09408
- Sahlsten J, Jaskari J, Kivinen J, Turunen L, Jaanio E, Hietala K, et al. Deep learning fundus image analysis for diabetic retinopathy and macular edema grading. Sci Rep 2019; 9: 10750. doi: 10.1038/s41598-019-47181-w
- Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. S. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017; 542: 115-8. doi: 10.1038/nature21056
- Irvin J, Rajpurkar P, Ko M, Yu Y, Ciurea-Ilcus S, Chute C, et al. CheXpert: a large chest radiograph dataset with uncertainty labels and expert comparison. Proc Conf AAAI Artif Intell 2019; 33: 590-7. doi: 10.1609/aaai. v33i01.3301590
- Nie D, Cao X, Gao Y, Wang L, Shen D. Estimating CT image from MRI data using 3D fully convolutional networks. Deep Learn Data Label Med Appl 2016; 2016: 170-8. doi: 10.1007/978-3-319-46976-8_18
- Torrado-Carvajal A, Vera-Olmos J, Izquierdo-Garcia D, Catalano OA, Morales MA, Margolin J, et al. Dixon-VIBE Deep Learning (DIVIDE) pseudo-CT synthesis for pelvis PET/MR attenuation correction. J Nucl Med 2019; 60: 429-35. doi: 10.2967/jnumed.118.209288
- Guo R, Hu X, Song H, Xu P, Xu H, Rominger A, et al. Weakly supervised deep learning for determining the prognostic value of 18F-FDG PET/CT in extranodal natural killer/T cell lymphoma, nasal type. Eur J Nucl Med Mol Imaging 2021; 48: 3151-61. doi: 10.1007/s00259-021-05232-3
- Hwang D, Kang SK, Kim KY, Seo S, Paeng JC, Lee DS, et al. Generation of PET attenuation map for whole-body time-of-flight 18F-FDG PET/MRI using a deep neural network trained with simultaneously reconstructed activity and attenuation maps. J Nucl Med 2019; 60: 1183-9. doi: 10.2967/ jnumed.118.219493
- Liu F, Jang H, Kijowski R, Bradshaw T, McMillan AB. Deep learning MR imaging-based attenuation correction for PET/MR imaging. Radiology 2018; 286: 676-84. doi: 10.1148/radiol.2017170700
- Leynes AP, Yang J, Wiesinger F, Kaushik SS, Shanbhag DD, Seo Y, et al. Zero-Echo-Time and Dixon Deep Pseudo-CT (ZeDD CT): direct generation of pseudo-CT images for pelvic PET/MRI attenuation correction using deep convolutional neural networks with multiparametric MRI. J Nucl Med 2018; 59: 852-8. doi: 10.2967/jnumed.117.198051
- Blanc-Durand P, Van Der Gucht A, Schaefer N, Itti E, Prior JO. Automatic lesion detection and segmentation of 18F-FET PET in gliomas: a full 3D U-Net convolutional neural network study. PLoS One 2018; 13: e0195798 doi: 10.1371/journal.pone.0195798
- Zhao X, Li L, Lu W, Tan S. Tumor co-segmentation in PET/CT using multi-modality fully convolutional neural network. Phys Med Biol 2018; 64: 015011 doi: 10.1088/1361-6560/aaf44b
- Zhong Z, Kim Y, Plichta K, Allen BG, Zhou L, Buatti J, et al. Simultaneous cosegmentation of tumors in PET-CT images using deep fully convolutional networks. Med Phys 2019; 46(2): 619-33. doi: 10.1002/mp.13331
- Schwyzer M, Ferraro DA, Muehlematter UJ, Curioni-Fontecedro A, Huellner MW, von Schulthess GK, et al. Automated detection of lung cancer at ultralow dose PET/CT by deep neural networks – initial results. Lung Cancer 2018; 126: 170-3. doi: 10.1016/j.lungcan.2018.11.001
- Hatt M, Laurent B, Ouahabi A, Fayad H, Tan S, Li L, et al. The first MICCAI challenge on PET tumor segmentation. Med Image Anal 2018; 44: 177-95. doi: 10.1016/j.media.2017.12.007
- Student. The probable error of a mean. Biometrika 1908; 6: 1. doi: 10.2307/2331554
- Pearson K. X. On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling. Lond Edinb Dublin Philos Mag J Sci 1900; 50: 157-75. doi: 10.1080/14786440009463897
- Jones E, Oliphant T, Peterson P, Others. SciPy.org SciPy Open source Sci. tools Python2. 2001.
- Good IJ. Rational decisions. J R Stat Soc Ser B 1952; 14: 107-14. doi: 10.1111/ j.2517-6161.1952.tb00104.x
- He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016. doi: 10.1109/cvpr.2016.90
- Hara K, Kataoka H, Satoh Y. Learning spatio-temporal features with 3D residual networks for action recognition. 2017 IEEE International Conference on Computer Vision Workshops (ICCVW) 2017. doi: 10.1109/iccvw.2017.373
- Huang G, Liu Z, Van Der Maaten L, Weinberger KQ. Densely connected convolutional networks. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017. doi: 10.1109/cvpr.2017.243
- Zagoruyko S, Komodakis N. Wide residual networks. Procedings of the British Machine Vision Conference 2016; 2016. doi: 10.5244/c.30.87
- He K, Zhang X, Ren S, Sun J. Identity mappings in deep residual networks. Computer Vision – ECCV 2016. 2016: 630-45. doi: 10.1007/978-3-31946493-0_38
- Full stack deep learning. Lecture 1: DL fundamentals [Internet]. Fullstackdeeplearning.com. [cited 2022 Aug 28]. Available from: https://fullstackdeeplearning.com/spring2021/lecture-1/
- Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation. In: Navab N, Hornegger J, Wells W, Frangi A. editors. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science 2015; 9351: 234-41. Cham: Springer.doi: 10.1007/978-3-319-24574-4_28
- Rossum G Van, Drake FL. Python Tutorial, Technical Report CS-R9526. Cent voor Wiskd en Inform 1995.
- Paszke A, Gross S, Chintala S, Chanan G, Yang E, DeVito Z, et al.. Automatic differentiation in PyTorch. 31st Conf Neural Inf Process Syst 2017.
- Stevenson M, Sergeant E, Nunes T, Heuer C, Marshall J, Sanchez J, et al. epiR: Tools for the analysis of epidemiological data. v1.0-15. 2020. [cited 2022 Mar 15]. Available at: https://CRAN.R-project.org/package=epiR
- R Development Core Team. R: a language and environment for statistical computing. Vienna; R Foundation for Statistical Computing. Available at: . http://www.R-project.org
- McNemar Q. Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika 1947; 12: 153-7. doi: 10.1007/bf02295996
- Stock C, Hielscher T. DTComPair: comparison of binary diagnostic tests in a paired study design. R package version 1.0.3. [Internet]. 2014. Available from: http://cran.r-project.org/package=DTComPair
- Rao SD. Epidemiology of parathyroid disorders. Best Pract Res Clin Endocrinol Metab 2018; 32: 773-80. doi: 10.1016/j.beem.2018.12.003
- Somnay YR, Craven M, McCoy KL, Carty SE, Wang TS, Greenberg CC, et al. Improving diagnostic recognition of primary hyperparathyroidism with machine learning. Surgery 2017;161: 1113-21. doi: 10.1016/j.surg.2016.09.044
- Press DM, Siperstein AE, Berber E, Shin JJ, Metzger R, Monteiro R, et al. The prevalence of undiagnosed and unrecognized primary hyperparathyroidism: a population-based analysis from the electronic medical record. Surgery 2013; 154: 1232-8. doi: 10.1016/j.surg.2013.06.051
- Bilezikian JP, Marcus R, Levine MA, Marcocci C, Silverberg SJ, Potts JT, editors. Parathyroids: basic and clinical concepts. 3rd edition. 2014. Elsevier, Academic Press.
- Marzouki HZ, Chavannes M, Tamilia M, Hier MP, Black MJ, Levental M, et al. Location of parathyroid adenomas: 7-year experience. J Otolaryngol Head Neck Surg 2010; 39: 551-4. PMID: 20828518
- Filser B, Uslar V, Weyhe D, Tabriz N. Predictors of adenoma size and location in primary hyperparathyroidism. Langenbeck’s Arch Surg 2021; 406: 1607. doi: 10.1007/s00423-021-02179-9
- Shah VN, Bhadada SK, Bhansali A, Behera A, Mittal BR. Changes in clinical & biochemical presentations of primary hyperparathyroidism in India over a period of 20 years. Indian J Med Res 2014; 139: 694-9. PMID: 25027078
- Xie S, Girshick R, Dollár P, Tu Z, He K. Aggregated residual transformations for deep neural networks. Proc - 30th IEEE Conf Comput Vis Pattern Recognition, CVPR 2017 2017. doi: 10.1109/cvpr.2017.634
- Gao S, Cheng MM, Zhao K, Zhang XY, Yang MH, Torr PHS. Res2Net: a new multi-scale backbone architecture. IEEE Trans Pattern Anal Mach Intell 2019. doi: 10.1109/TPAMI.2019.2938758
- Chen S, Tan X, Wang B, Hu X. Reverse attention for salient object detection. Computer Vision – ECCV 2018 2018; 236-52. doi: 10.1007/978-3-03001240-3_15
- Bailly A, Blanc C, Francis É, Guillotin T, Jamal F, Wakim B, et al. Effects of dataset size and interactions on the prediction performance of logistic regression and deep learning models. Comput Methods Programs Biomed 2022; 213: 106504 doi: 10.1016/j.cmpb.2021.106504
- Hochreiter S, Schmidhuber J. Long Short-Term Memory. Neural Comput 1997; 9: 1735-80. doi: 10.1162/neco.1997.9.8.1735
- Togo R, Hirata K, Manabe O, Ohira H, Tsujino I, Magota K, et al. Cardiac sarcoidosis classification with deep convolutional neural network-based features using polar maps. Comput Biol Med 2019; 104: 81-6. doi: 10.1016/j. compbiomed.2018.11.008
- Lu D, Popuri K, Ding GW, Balachandar R, Beg MF. Multiscale deep neural network based analysis of FDG-PET images for the early diagnosis of Alzheimer’s disease. Med Image Anal 2018; 46: 26-34. doi: 10.1016/j.media.2018.02.002
- Ma L, Ma C, Liu Y, Wang X. Thyroid diagnosis from SPECT images using convolutional neural network with optimization. Comput Intell Neurosci 2019; 2019: 6212759. doi: 10.1155/2019/6212759
- Niu Z, Zhong G, Yu H. A review on the attention mechanism of deep learning. Neurocomputing 2021; 452: 48-62. doi: 10.1016/j.neucom.2021.03.091
- Liu Y, Zhang Y, Wang Y, Hou F, Yuan J, Tian J, et al. A survey of visual transformers. arXiv [csCV] [Internet]. 2021 [cited 2022 Aug 28]; Available from: http://arxiv.org/abs/2111.06091
- Zhou B, Khosla A, Lapedriza A, Oliva A, Torralba A. Learning deep features for discriminative localization. arXiv [csCV] [Internet]. 2015 [cited 2022 Aug 28]; Available from: http://arxiv.org/abs/1512.04150
- Ancona M, Ceolini E, Öztireli C, Gross M. Towards better understanding of gradient-based attribution methods for deep neural networks. arXiv [csLG] [Internet]. 2017 [cited 2022 Aug 28]; Available from: http://arxiv.org/abs/1711.06104