1. Yao JC, Hassan M, Phan A, Dagohoy C, Leary C, Mares JE, et al. One hundred years after "carcinoid": epidemiology of and prognostic factors for neuroendocrine tumors in 35,825 cases in the United States. Journal of clinical oncology : official journal of the American Society of Clinical Oncology. 2008;26:3063-72. doi:10.1200/jco.2007.15.4377.
2. Johnbeck CB, Knigge U, Kjær A. PET tracers for somatostatin receptor imaging of neuroendocrine tumors: current status and review of the literature. Future Oncol. 2014;10:2259-77. doi:10.2217/fon.14.139.
3. Panagiotidis E, Alshammari A, Michopoulou S, Skoura E, Naik K, Maragkoudakis E, et al. Comparison of the Impact of 68Ga-DOTATATE and 18F-FDG PET/CT on Clinical Management in Patients with Neuroendocrine Tumors. Journal of nuclear medicine : official publication, Society of Nuclear Medicine. 2017;58:91-6. doi:10.2967/jnumed.116.178095.
4. Riihimäki M, Hemminki A, Sundquist K, Sundquist J, Hemminki K. The epidemiology of metastases in neuroendocrine tumors. International journal of cancer. 2016;139:2679-86. doi:10.1002/ijc.30400.
5. Sadowski SM, Neychev V, Millo C, Shih J, Nilubol N, Herscovitch P, et al. Prospective Study of 68Ga-DOTATATE Positron Emission Tomography/Computed Tomography for Detecting Gastro-Entero-Pancreatic Neuroendocrine Tumors and Unknown Primary Sites. Journal of clinical oncology : official journal of the American Society of Clinical Oncology. 2016;34:588-96. doi:10.1200/JCO.2015.64.0987.
6. Strosberg J, El-Haddad G, Wolin E, Hendifar A, Yao J, Chasen B, et al. Phase 3 Trial of 177Lu-Dotatate for Midgut Neuroendocrine Tumors. New England Journal of Medicine. 2017;376:125-35. doi:doi:10.1056/NEJMoa1607427.
7. Strosberg J, Leeuwenkamp O, Siddiqui MK. Peptide receptor radiotherapy re-treatment in patients with progressive neuroendocrine tumors: A systematic review and meta-analysis. Cancer treatment reviews. 2021;93:102141. doi:10.1016/j.ctrv.2020.102141.
8. Haug AR, Auernhammer CJ, Wangler B, Schmidt GP, Uebleis C, Goke B, et al. 68Ga-DOTATATE PET/CT for the early prediction of response to somatostatin receptor-mediated radionuclide therapy in patients with well-differentiated neuroendocrine tumors. Journal of nuclear medicine : official publication, Society of Nuclear Medicine. 2010;51:1349-56. doi:10.2967/jnumed.110.075002.
9. Liberini V, Huellner MW, Grimaldi S, Finessi M, Thuillier P, Muni A, et al. The Challenge of Evaluating Response to Peptide Receptor Radionuclide Therapy in Gastroenteropancreatic Neuroendocrine Tumors: The Present and the Future. Diagnostics (Basel, Switzerland). 2020;10. doi:10.3390/diagnostics10121083.
10. Fendler WP, Barrio M, Spick C, Allen-Auerbach M, Ambrosini V, Benz M, et al. 68Ga-DOTATATE PET/CT Interobserver Agreement for Neuroendocrine Tumor Assessment: Results of a Prospective Study on 50 Patients. Journal of nuclear medicine : official publication, Society of Nuclear Medicine. 2017;58:307-11. doi:10.2967/jnumed.116.179192.
11. Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, et al. A survey on deep learning in medical image analysis. Medical image analysis. 2017;42:60-88. doi:10.1016/j.media.2017.07.005.
12. Halabi SS, Prevedello LM, Kalpathy-Cramer J, Mamonov AB, Bilbily A, Cicero M, et al. The RSNA Pediatric Bone Age Machine Learning Challenge. Radiology. 2019;290:498-503. doi:10.1148/radiol.2018180736.
13. Häggström I, Schmidtlein CR, Campanella G, Fuchs TJ. DeepPET: A deep encoder-decoder network for directly solving the PET image reconstruction inverse problem. Medical image analysis. 2019;54:253-62. doi:10.1016/j.media.2019.03.013.
14. Hartenstein A, Lübbe F, Baur ADJ, Rudolph MM, Furth C, Brenner W, et al. Prostate Cancer Nodal Staging: Using Deep Learning to Predict (68)Ga-PSMA-Positivity from CT Imaging Alone. Scientific reports. 2020;10:3398. doi:10.1038/s41598-020-60311-z.
15. Jemaa S, Fredrickson J, Carano RAD, Nielsen T, de Crespigny A, Bengtsson T. Tumor Segmentation and Feature Extraction from Whole-Body FDG-PET/CT Using Cascaded 2D and 3D Convolutional Neural Networks. J Digit Imaging. 2020;33:888-94. doi:10.1007/s10278-020-00341-1.
16. Pfaehler E, Mesotten L, Kramer G, Thomeer M, Vanhove K, de Jong J, et al. Repeatability of two semi-automatic artificial intelligence approaches for tumor segmentation in PET. EJNMMI research. 2021;11:4. doi:10.1186/s13550-020-00744-9.
17. Thapa P, Ranade R, Ostwal V, Shrikhande SV, Goel M, Basu S. Performance of 177Lu-DOTATATE-based peptide receptor radionuclide therapy in metastatic gastroenteropancreatic neuroendocrine tumor: a multiparametric response evaluation correlating with primary tumor site, tumor proliferation index, and dual tracer imaging characteristics. Nuclear medicine communications. 2016;37:1030-7. doi:10.1097/mnm.0000000000000547.
18. 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. p. 770-8.
19. Dumoulin V, Visin F. A guide to convolution arithmetic for deep learning. 2016. p. arXiv:1603.07285.
20. Taghanaki SA, Zheng Y, Kevin Zhou S, Georgescu B, Sharma P, Xu D, et al. Combo loss: Handling input and output imbalance in multi-organ segmentation. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society. 2019;75:24-33. doi:10.1016/j.compmedimag.2019.04.005.
21. Baek S, He Y, Allen BG, Buatti JM, Smith BJ, Tong L, et al. Deep segmentation networks predict survival of non-small cell lung cancer. Scientific reports. 2019;9:17286. doi:10.1038/s41598-019-53461-2.
22. Choi JH, Kim HA, Kim W, Lim I, Lee I, Byun BH, et al. Early prediction of neoadjuvant chemotherapy response for advanced breast cancer using PET/MRI image deep learning. Scientific reports. 2020;10:21149. doi:10.1038/s41598-020-77875-5.
23. Langlotz CP, Allen B, Erickson BJ, Kalpathy-Cramer J, Bigelow K, Cook TS, et al. A Roadmap for Foundational Research on Artificial Intelligence in Medical Imaging: From the 2018 NIH/RSNA/ACR/The Academy Workshop. Radiology. 2019;291:781-91. doi:10.1148/radiol.2019190613.
24. Chin BB WJ, Silosky M, Halley C, Niman R, Moses K, Karki R, Xing F. Automated Liver Lesion Detection in 68Ga DOTATATE PET / CT: Preliminary Results using a Deep Learning 3D Fully Convolutional Network Journal of Nuclear Medicine. 2021:abstract.
25. Chin B, Silosky M, Morgan R, Karki R, Anderson J. 68Ga DOTATATE organ specific tumor signal to noise (S/N) ratios: Comparison of lesion detectability from phantom studies to lesion detectability in clinical practice. Journal of Nuclear Medicine. 2019;60:478.
26. Groendahl AR, Skjei Knudtsen I, Huynh BN, Mulstad M, Moe YM, Knuth F, et al. A comparison of methods for fully automatic segmentation of tumors and involved nodes in PET/CT of head and neck cancers. Physics in medicine and biology. 2021;66:065012. doi:10.1088/1361-6560/abe553.
27. Anderson J, Silosky M, Karki R, Morgan R, Chin B. Normal biodistribution and tumor uptake of 68Ga DOTATATE PET / CT in the clinical setting: normal background activity, and organ specific tumor characterization of metastatic lesions. Journal of Nuclear Medicine. 2019;60:3031.
28. Chin BB, Green ED, Turkington TG, Hawk TC, Coleman RE. Increasing uptake time in FDG-PET: standardized uptake values in normal tissues at 1 versus 3 h. Molecular imaging and biology : MIB : the official publication of the Academy of Molecular Imaging. 2009;11:118-22. doi:10.1007/s11307-008-0177-9.
29. Silosky MS, Karki R, Morgan R, Anderson J, Chin BB. Physical characteristics of (68)Ga DOTATATE PET/CT affecting small lesion detectability. American journal of nuclear medicine and molecular imaging. 2021;11:27-39.
30. Silosky M, Karki R, Chin B. 68Ga and 18F quantification, and detectability of hot spots using an ACR Phantom: Contributions of radionuclide physical differences to hot spot detectability. Journal of Nuclear Medicine. 2019;60:1200.
31. Beauregard JM, Hofman MS, Kong G, Hicks RJ. The tumour sink effect on the biodistribution of 68Ga-DOTA-octreotate: implications for peptide receptor radionuclide therapy. Eur J Nucl Med Mol Imaging. 2012;39:50-6. doi:10.1007/s00259-011-1937-3.