Detection and localization of hyperfunctioning parathyroid glands on [18F]fluorocholine PET/ CT using deep learning – model performance and comparison to human experts
This work is licensed under the Creative Commons Attribution 4.0 International License.
Fraser WD. Hyperparathyroidism. Lancet 2009; 374: 145-58. doi: 10.1016/ s0140-6736(09)60507-9FraserWDHyperparathyroidism20093741455810.1016/s0140-6736(09)60507-9Open DOISearch in Google Scholar
Grimelius L, Akerström G, Johansson H, Bergström R. Anatomy and histopathology of human parathyroid glands. Pathol Annu 1981; 16(Pt 2): 1-24. PMID: 7036057GrimeliusLAkerströmGJohanssonHBergströmRAnatomy and histopathology of human parathyroid glands198116Pt 2124PMID: 7036057Search in Google Scholar
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.119.229914CudermanASenicaKRepSHocevarMKocjanTSever, et al18F-Fluorocholine PET/CT in primary hyperparathyroidism: superior diagnostic performance to conventional scintigraphic imaging for localization of hyperfunctioning parathyroid glands2019615778310.2967/jnumed.119.22991431562221Open DOISearch in Google Scholar
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/s00259-014-2837-0LezaicLRepSSeverMJKocjanTHocevarMFettichJ18F-Fluorocholine PET/CT for localization of hyperfunctioning parathyroid tissue in primary hyperparathyroidism: a pilot study2014412083910.1007/s00259-014-2837-025063039Open DOISearch in Google Scholar
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.2021.05.056GravesCEHopeTAKimJPampaloniMHKluijfhoutWSeibCDet alSuperior sensitivity of 18F-fluorocholine: PET localization in primary hyperparathyroidism2022171475410.1016/j.surg.2021.05.05634301418Open DOISearch in Google Scholar
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.0000000000001701MichaudLBalogovaSBurgessAOhnonaJHuchetVKerrouKet alA 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 hyperparathyroidism201594e170110.1097/md.0000000000001701461678126469908Open DOISearch in Google Scholar
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.0000000000000595KluijfhoutWPVorselaarsWMvan denBerk SAVriensMRBorelRinkes IHValkGDet alFluorine-18 fluorocholine PET-CT localizes hyperparathyroidism in patients with inconclusive conventional imaging20163712465210.1097/mnm.000000000000059527612033Open DOISearch in Google Scholar
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/ s00423-016-1425-0KluijfhoutWPPasternakJDDrakeFTBeninatoTGosnellJEShenWTet alUse of PET tracers for parathyroid localization: a systematic review and meta-analysis20164019253510.1007/s00423-016-1425-0508634627086309Open DOISearch in Google Scholar
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.0000000000001845ThanseerNBhadadaSKSoodAMittalBRBeheraAGorlaA K Ret alComparative effectiveness of ultrasonography, 99mTc-sestamibi, and 18F-fluorocholine PET/CT in detecting parathyroid adenomas in patients with primary hyperparathyroidism201742e491710.1097/rlu.0000000000001845Open DOISearch in Google Scholar
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.120.257303WhitmanJAllenIEBergslandEKSuhIHopeTAAssessment and comparison of 18F-Fluorocholine PET and 99mTc-sestamibi scans in identifying parathyroid adenomas: a metaanalysis20216212859110.2967/jnumed.120.257303888289233452040Open DOISearch in Google Scholar
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/s00259-018-3980-9BeheshtiMHehenwarterLPaymaniZRendlGImamovicLRettenbacherRet al18F-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 patients20184517627110.1007/s00259-018-3980-9609775429516131Open DOISearch in Google Scholar
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/s13550-019-0544-3BroosWAMWondergemMKnolRJJVander Zant FMParathyroid imaging with 18F-fluorocholine PET/CT as a first-line imaging modality in primary hyperparathyroidism: a retrospective cohort study201997210.1186/s13550-019-0544-3666922531367807Open DOISearch in Google Scholar
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.120.256735HopeTAGravesCECalaisJEhmanECJohnsonGBThompsonDet alAccuracy of 18 F-fluorocholine PET for the detection of parathyroid adenomas: prospective single-center study20216215116doi: /10.2967/jnumed.120.25673510.2967/jnumed.120.256735861234333674400Search in Google Scholar
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/aaa86fRepSHocevarMVaupoticJZdesarUZaletelKLezaicL18F-choline PET/ CT for parathyroid scintigraphy: significantly lower radiation exposure of patients in comparison to conventional nuclear medicine imaging approaches2018383435610.1088/1361-6498/aaa86f29339573Open DOISearch in Google Scholar
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.2020.08.004LiYSixouBPeyrinFA review of the deep learning methods for medical images super resolution problems2021421203310.1016/j.irbm.2020.08.004Open DOISearch in Google Scholar
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.2019.2919431YangWZhangXTianYWangWXueJ-HLiaoQDeep learning for single image super-resolution: a brief review20192131062110.1109/tmm.2019.2919431Open DOISearch in Google Scholar
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.2020.2982224WangLChenWYangWBiFYuFRA state-of-the-art review on image synthesis with generative adversarial networks2020863514-3710.1109/access.2020.2982224Open DOISearch in Google Scholar
Liu B, Liu J. Overview of image denoising based on deep learning. J Phys Conf Ser 2019; 1176: 022010. doi: 10.1088/1742-6596/1176/2/022010LiuBLiuJOverview of image denoising based on deep learning2019117602201010.1088/1742-6596/1176/2/022010Open DOISearch in Google Scholar
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.2017170077ChartrandGChengPMVorontsovEDrozdzalMTurcotteSPalCJet alDeep learning: a primer for radiologists20173721133110.1148/rg.201717007729131760Open DOISearch in Google Scholar
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.2017.8253139Al-SaffarAAMTaoHTalabMAReview of deep convolution neural network in image classificationIn2017p263110.1109/icramet.2017.8253139Open DOISearch in Google Scholar
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.2001.05566MinaeeSBoykovYPorikliFPlazaAKehtarnavazNTerzopoulosDImage segmentation using deep learning: a survey. [Internet]200105566 2020. Available fromhttps://doi.org/10.48550/arXiv.2001.0556610.1109/TPAMI.2021.305996833596172Search in Google Scholar
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/1907.09408JiaoLZhangFLiuFYangSLiLFengZet alA survey of deep learning-based object detection. [Internet]2019Available fromhttp://arxiv.org/abs/1907.09408Search in Google Scholar
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-wSahlstenJJaskariJKivinenJTurunenLJaanioEHietalaKet alDeep learning fundus image analysis for diabetic retinopathy and macular edema grading201991075010.1038/s41598-019-47181-w665688031341220Open DOISearch in Google Scholar
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/nature21056EstevaAKuprelBNovoaRAKoJSwetterSMBlauHMet alS2017542115810.1038/nature21056838223228117445Open DOISearch in Google Scholar
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. v33i01.3301590IrvinJRajpurkarPKoMYuYCiurea-IlcusSChuteCet alCheXpert: a large chest radiograph dataset with uncertainty labels and expert comparison201933590710.1609/aaai.v33i01.3301590Open DOISearch in Google Scholar
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_18NieDCaoXGaoYWangLShenDEstimating CT image from MRI data using 3D fully convolutional networks20162016170810.1007/978-3-319-46976-8_18565458329075680Open DOISearch in Google Scholar
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.118.209288Torrado-CarvajalAVera-OlmosJIzquierdo-GarciaDCatalanoOAMoralesMAMargolinJet alDixon-VIBE Deep Learning (DIVIDE) pseudo-CT synthesis for pelvis PET/MR attenuation correction2019604293510.2967/jnumed.118.209288691062630166357Open DOISearch in Google Scholar
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/s00259-021-05232-3GuoRHuXSongHXuPXuHRomingerAet alWeakly supervised deep learning for determining the prognostic value of 18F-FDG PET/CT in extranodal natural killer/T cell lymphoma, nasal type20214831516110.1007/s00259-021-05232-3789683333611614Open DOISearch in Google Scholar
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.118.219493HwangDKangSKKimKYSeoSPaengJCLeeDSet alGeneration 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 maps2019601183910.2967/jnumed.118.219493668169130683763Open DOISearch in Google Scholar
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.2017170700LiuFJangHKijowskiRBradshawTMcMillanABDeep learning MR imaging-based attenuation correction for PET/MR imaging20182866768410.1148/radiol.2017170700579030328925823Open DOISearch in Google Scholar
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.117.198051LeynesAPYangJWiesingerFKaushikSSShanbhagDDSeoYet alZero-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 MRI201859852810.2967/jnumed.117.198051593253029084824Open DOISearch in Google Scholar
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.0195798Blanc-DurandPVanDer Gucht ASchaeferNIttiEPriorJOAutomatic lesion detection and segmentation of 18F-FET PET in gliomas: a full 3D U-Net convolutional neural network study201813e019579810.1371/journal.pone.0195798589873729652908Open DOISearch in Google Scholar
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/aaf44bZhaoXLiLLuWTanSTumor co-segmentation in PET/CT using multi-modality fully convolutional neural network20186401501110.1088/1361-6560/aaf44b749381230523964Open DOISearch in Google Scholar
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.13331ZhongZKimYPlichtaKAllenBGZhouLBuattiJet alSimultaneous cosegmentation of tumors in PET-CT images using deep fully convolutional networks20194626193310.1002/mp.13331652732730537103Open DOISearch in Google Scholar
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.2018.11.001SchwyzerMFerraroDAMuehlematterUJCurioni-FontecedroAHuellnerMWvon SchulthessGKet alAutomated detection of lung cancer at ultralow dose PET/CT by deep neural networks – initial results2018126170310.1016/j.lungcan.2018.11.00130527183Open DOISearch in Google Scholar
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.2017.12.007HattMLaurentBOuahabiAFayadHTanSLiLet alThe first MICCAI challenge on PET tumor segmentation2018441779510.1016/j.media.2017.12.007746072229268169Open DOISearch in Google Scholar
Student. The probable error of a mean. Biometrika 1908; 6: 1. doi: 10.2307/2331554StudentThe probable error of a mean19086110.2307/2331554Open DOISearch in Google Scholar
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: 10.1080/14786440009463897PearsonK. X.1900501577510.1080/14786440009463897Open DOISearch in Google Scholar
Jones E, Oliphant T, Peterson P, Others. SciPy.orgSciPy Open source Sci. tools Python2. 2001.JonesEOliphantTPetersonPOthersSciPy.org2001Search in Google Scholar
Good IJ. Rational decisions. J R Stat Soc Ser B 1952; 14: 107-14. doi: 10.1111/ j.2517-6161.1952.tb00104.xGoodIJRational decisions1952141071410.1111/j.2517-6161.1952.tb00104.xOpen DOISearch in Google Scholar
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.2016.90HeKZhangXRenSSunJDeep residual learning for image recognition201610.1109/cvpr.2016.90Open DOISearch in Google Scholar
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.2017.373HaraKKataokaHSatohYLearning spatio-temporal features with 3D residual networks for action recognition201710.1109/iccvw.2017.373Open DOISearch in Google Scholar
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.2017.243HuangGLiuZVanDer Maaten LWeinbergerKQDensely connected convolutional networks201710.1109/cvpr.2017.243Open DOISearch in Google Scholar
Zagoruyko S, Komodakis N. Wide residual networks. Procedings of the British Machine Vision Conference 2016; 2016. doi: 10.5244/c.30.87ZagoruykoSKomodakisNWide residual networks2016201610.5244/c.30.87Open DOISearch in Google Scholar
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_38HeKZhangXRenSSunJIdentity mappings in deep residual networks201620166304510.1007/978-3-31946493-0_38Open DOISearch in Google Scholar
Full stack deep learning. Lecture 1: DL fundamentals [Internet]. Fullstackdeeplearning.com. [cited 2022 Aug 28]. Available from: https://fullstackdeeplearning.com/spring2021/lecture-1/Full stack deep learningAvailable fromhttps://fullstackdeeplearning.com/spring2021/lecture-1/Search in Google Scholar
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_28RonnebergerOFischerPBroxTU-Net: convolutional networks for biomedical image segmentation2015935123441Cham: Springer10.1007/978-3-319-24574-4_28Open DOISearch in Google Scholar
Rossum G Van, Drake FL. Python Tutorial, Technical Report CS-R9526. Cent voor Wiskd en Inform 1995.RossumG VanDrakeFLPython Tutorial, Technical Report CS-R95261995Search in Google Scholar
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.PaszkeAGrossSChintalaSChananGYangEDeVitoZet alAutomatic differentiation in PyTorch2017Search in Google Scholar
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=epiRStevensonMSergeantENunesTHeuerCMarshallJSanchezJet al2020[cited 2022 Mar 15]. Available athttps://CRAN.R-project.org/package=epiRSearch in Google Scholar
R Development Core Team. R: a language and environment for statistical computing. Vienna; R Foundation for Statistical Computing. Available at: . http://www.R-project.orgR Development Core TeamAvailable athttp://www.R-project.orgSearch in Google Scholar
McNemar Q. Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika 1947; 12: 153-7. doi: 10.1007/bf02295996McNemarQ.Note on the sampling error of the difference between correlated proportions or percentages194712153710.1007/bf0229599620254758Open DOISearch in Google Scholar
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=DTComPairStockCHielscherT2014Available fromhttp://cran.r-project.org/package=DTComPairSearch in Google Scholar
Rao SD. Epidemiology of parathyroid disorders. Best Pract Res Clin Endocrinol Metab 2018; 32: 773-80. doi: 10.1016/j.beem.2018.12.003RaoSDEpidemiology of parathyroid disorders2018327738010.1016/j.beem.2018.12.00330559041Open DOISearch in Google Scholar
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.2016.09.044SomnayYRCravenMMcCoyKLCartySEWangTSGreenbergCCet alImproving diagnostic recognition of primary hyperparathyroidism with machine learning201716111132110.1016/j.surg.2016.09.044536795827989606Open DOISearch in Google Scholar
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.2013.06.051PressDMSipersteinAEBerberEShinJJMetzgerRMonteiroRet alThe prevalence of undiagnosed and unrecognized primary hyperparathyroidism: a population-based analysis from the electronic medical record20131541232810.1016/j.surg.2013.06.05124383100Open DOISearch in Google Scholar
Bilezikian JP, Marcus R, Levine MA, Marcocci C, Silverberg SJ, Potts JT, editors. Parathyroids: basic and clinical concepts. 3rd edition. 2014. Elsevier, Academic Press.BilezikianJPMarcusRLevineMAMarcocciCSilverbergSJPottsJTeditors2014Elsevier, Academic PressSearch in Google Scholar
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: 20828518MarzoukiHZChavannesMTamiliaMHierMPBlackMJLeventalMet alLocation of parathyroid adenomas: 7-year experience2010395514PMID: 20828518Search in Google Scholar
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/s00423-021-02179-9FilserBUslarVWeyheDTabrizNPredictors of adenoma size and location in primary hyperparathyroidism2021406160710.1007/s00423-021-02179-9837094933928428Open DOISearch in Google Scholar
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: 25027078ShahVNBhadadaSKBhansaliABeheraAMittalBRChanges in clinical & biochemical presentations of primary hyperparathyroidism in India over a period of 20 years20141396949PMID: 25027078Search in Google Scholar
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.2017.634XieSGirshickRDollárPTuZHeKAggregated residual transformations for deep neural networks2017201710.1109/cvpr.2017.634Open DOISearch in Google Scholar
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.2019.2938758GaoSChengMMZhaoKZhangXYYangMHTorrPHSRes2Net: a new multi-scale backbone architecture201910.1109/TPAMI.2019.293875831484108Open DOISearch in Google Scholar
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_15ChenSTanXWangBHuXReverse attention for salient object detection20182365210.1007/978-3-03001240-3_15Open DOISearch in Google Scholar
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.2021.106504BaillyABlancCFrancisÉGuillotinTJamalFWakimBet alEffects of dataset size and interactions on the prediction performance of logistic regression and deep learning models202221310650410.1016/j.cmpb.2021.10650434798408Open DOISearch in Google Scholar
Hochreiter S, Schmidhuber J. Long Short-Term Memory. Neural Comput 1997; 9: 1735-80. doi: 10.1162/neco.1997.9.8.1735HochreiterSSchmidhuberJLong Short-Term Memory1997917358010.1162/neco.1997.9.8.17359377276Open DOISearch in Google Scholar
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.2018.11.008TogoRHirataKManabeOOhiraHTsujinoIMagotaKet alCardiac sarcoidosis classification with deep convolutional neural network-based features using polar maps201910481610.1016/j.compbiomed.2018.11.00830447397Open DOISearch in Google Scholar
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.2018.02.002LuDPopuriKDingGWBalachandarRBegMFMultiscale deep neural network based analysis of FDG-PET images for the early diagnosis of Alzheimer’s disease201846263410.1016/j.media.2018.02.00229502031Open DOISearch in Google Scholar
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: 10.1155/2019/6212759MaLMaCLiuYWangXThyroid diagnosis from SPECT images using convolutional neural network with optimization20192019621275910.1155/2019/6212759635054730766599Open DOISearch in Google Scholar
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.2021.03.091NiuZZhongGYuHA review on the attention mechanism of deep learning2021452486210.1016/j.neucom.2021.03.091Open DOISearch in Google Scholar
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/2111.06091LiuYZhangYWangYHouFYuanJTianJet alA survey of visual transformers2021[cited 2022 Aug 28]; Available fromhttp://arxiv.org/abs/2111.06091Search in Google Scholar
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/1512.04150ZhouBKhoslaALapedrizaAOlivaATorralbaALearning deep features for discriminative localization2015[cited 2022 Aug 28]; Available fromhttp://arxiv.org/abs/1512.0415010.1109/CVPR.2016.319Search in Google Scholar
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/1711.06104AnconaMCeoliniEÖztireliCGrossM2017[cited 2022 Aug 28]; Available fromhttp://arxiv.org/abs/1711.06104Search in Google Scholar