{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T00:12:02Z","timestamp":1789517522873,"version":"build-2803163510"},"reference-count":80,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2025,8,28]],"date-time":"2025-08-28T00:00:00Z","timestamp":1756339200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Neuroendocrinology"],"published-print":{"date-parts":[[2025,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Neuroendocrine neoplasms (NENs), once considered rare, are now increasingly diagnosed worldwide, with gastro\u2010entero\u2010pancreatic neuroendocrine tumors (GEP\u2010NETs) accounting for the majority of cases (55%\u201370%). NENs are characterized by considerable heterogeneity, driven by factors such as tumor differentiation, Ki\u201067 index, primary tumor location, somatostatin receptor status, and disease stage. International guidelines advocate for a multidisciplinary approach to ensure individualized treatment strategies. Given the disease's complexity, artificial intelligence (AI) may offer substantial support in the management of NENs. AI is playing an increasingly prominent role in medicine by enabling advanced diagnostic capabilities through machine learning and deep learning algorithms, particularly in imaging. However, current literature on AI applications in NENs is limited, and their routine use in clinical practice has yet to be established. This narrative review aims to provide a comprehensive overview of the potential roles of AI in the diagnosis of GEP\u2010NENs, while also addressing the associated biases and ethical considerations of medical AI implementation.<\/jats:p>","DOI":"10.1111\/jne.70087","type":"journal-article","created":{"date-parts":[[2025,8,28]],"date-time":"2025-08-28T23:45:24Z","timestamp":1756424724000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Artificial intelligence in the diagnosis of gastro\u2010entero\u2010pancreatic neuroendocrine neoplasms: Potential benefits and current limitations"],"prefix":"10.1111","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9553-7684","authenticated-orcid":false,"given":"Elettra","family":"Merola","sequence":"first","affiliation":[{"name":"Dipartimento di Medicina, Chirurgia e Farmacia University of Sassari  Sassari Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8367-5649","authenticated-orcid":false,"given":"Giuseppe","family":"Fanciulli","sequence":"additional","affiliation":[{"name":"Dipartimento di Medicina, Chirurgia e Farmacia University of Sassari  Sassari Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3265-2823","authenticated-orcid":false,"given":"Giovanni Mario","family":"Pes","sequence":"additional","affiliation":[{"name":"Dipartimento di Medicina, Chirurgia e Farmacia University of Sassari  Sassari Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7305-3531","authenticated-orcid":false,"given":"Maria Pina","family":"Dore","sequence":"additional","affiliation":[{"name":"Dipartimento di Medicina, Chirurgia e Farmacia University of Sassari  Sassari Italy"},{"name":"Department of Medicine, Baylor College of Medicine One Baylor Plaza  Houston Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,8,28]]},"reference":[{"issue":"4","key":"e_1_2_12_2_1","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1007\/s11912-021-01029-7","article-title":"Epidemiology, incidence, and prevalence of neuroendocrine neoplasms: are there global differences?","volume":"23","author":"Das S","year":"2021","journal-title":"Curr Oncol Rep"},{"issue":"4","key":"e_1_2_12_3_1","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1007\/s00428-006-0250-1","article-title":"TNM staging of foregut (neuro)endocrine tumors: a consensus proposal including a grading system","volume":"449","author":"Rindi G","year":"2006","journal-title":"Virchows Arch"},{"issue":"4","key":"e_1_2_12_4_1","doi-asserted-by":"crossref","first-page":"757","DOI":"10.1007\/s00428-007-0452-1","article-title":"TNM staging of midgut and hindgut (neuro) endocrine tumors: a consensus proposal including a grading system","volume":"451","author":"Rindi G","year":"2007","journal-title":"Virchows Arch"},{"key":"e_1_2_12_5_1","volume-title":"WHO Classification of Tumours of Endocrine Organs","author":"Kloppel G","year":"2017"},{"key":"e_1_2_12_6_1","unstructured":"WHO Classification of Tumors Editorial Board: Digestive System Tumors. 5th edition (2019)."},{"key":"e_1_2_12_7_1","volume-title":"WHO Classification of Endocrine and Neuroendocrine Tumours","author":"WHO Classification of Tumours Editorial Board","year":"2022"},{"issue":"5","key":"e_1_2_12_8_1","doi-asserted-by":"crossref","first-page":"1486","DOI":"10.1210\/jc.2016-3732","article-title":"Functional imaging in the follow\u2010up of Enteropancreatic neuroendocrine tumors: clinical usefulness and indications","volume":"102","author":"Merola E","year":"2017","journal-title":"J Clin Endocrinol Metab"},{"issue":"2","key":"e_1_2_12_9_1","doi-asserted-by":"crossref","first-page":"78","DOI":"10.4240\/wjgs.v14.i2.78","article-title":"Therapeutic strategies for gastroenteropancreatic neuroendocrine neoplasms: state\u2010of\u2010the\u2010art and future perspectives","volume":"14","author":"Merola E","year":"2022","journal-title":"World J Gastrointest Surg"},{"issue":"6","key":"e_1_2_12_10_1","article-title":"Evaluation of multidisciplinary team decisions in neuroendocrine neoplasms: impact of expert centres","volume":"31","author":"Zandee WT","year":"2022","journal-title":"Eur J Cancer Care"},{"issue":"6","key":"e_1_2_12_11_1","article-title":"Ethical considerations in the use of artificial intelligence and machine learning in health care: a comprehensive review","volume":"16","author":"Harishbhai Tilala M","year":"2024","journal-title":"Cureus"},{"key":"e_1_2_12_12_1","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1053\/j.semnuclmed.2025.02.003","article-title":"The role of AI in the evaluation of neuroendocrine tumors: current state of the art","volume":"55","author":"Lopez\u2010Ramirez F","year":"2025","journal-title":"Semin Nucl Med"},{"issue":"5","key":"e_1_2_12_13_1","doi-asserted-by":"crossref","first-page":"319","DOI":"10.4253\/wjge.v15.i5.319","article-title":"Recent advances in endoscopic management of gastric neoplasms","volume":"15","author":"Cheema HI","year":"2023","journal-title":"World J Gastrointest Endosc"},{"key":"e_1_2_12_14_1","doi-asserted-by":"crossref","first-page":"667","DOI":"10.1055\/a-2543-0370","article-title":"Use of computer\u2010assisted detection (CADe) colonoscopy in colorectal cancer screening and surveillance: European Society of Gastrointestinal Endoscopy (ESGE) Position Statement","volume":"57","author":"Bretthauer M","year":"2025","journal-title":"Endoscopy"},{"issue":"2","key":"e_1_2_12_15_1","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1007\/s10120-021-01261-x","article-title":"Artificial intelligence\u2010based diagnosis of upper gastrointestinal subepithelial lesions on endoscopic ultrasonography images","volume":"25","author":"Hirai K","year":"2022","journal-title":"Gastric Cancer"},{"issue":"6","key":"e_1_2_12_16_1","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0251701","article-title":"Real\u2010time computer\u2010aided diagnosis of focal pancreatic masses from endoscopic ultrasound imaging based on a hybrid convolutional and long short\u2010term memory neural network model","volume":"16","author":"Udristoiu AL","year":"2021","journal-title":"PLoS One"},{"issue":"2","key":"e_1_2_12_17_1","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1055\/a-1873-7920","article-title":"Artificial intelligence using deep learning analysis of endoscopic ultrasonography images for the differential diagnosis of pancreatic masses","volume":"55","author":"Kuwahara T","year":"2023","journal-title":"Endoscopy"},{"issue":"7","key":"e_1_2_12_18_1","doi-asserted-by":"crossref","DOI":"10.3390\/diagnostics13071289","article-title":"Deep\u2010learning\u2010enabled computer\u2010aided diagnosis in the classification of pancreatic cystic lesions on confocal laser endomicroscopy","volume":"13","author":"Lee TC","year":"2023","journal-title":"Diagnostics (Basel)"},{"key":"e_1_2_12_19_1","doi-asserted-by":"crossref","DOI":"10.3389\/fendo.2024.1383814","article-title":"Endoscopic ultrasonography\u2010based intratumoral and peritumoral machine learning radiomics analyses for distinguishing insulinomas from non\u2010functional pancreatic neuroendocrine tumors","volume":"15","author":"Mo S","year":"2024","journal-title":"Front Endocrinol"},{"issue":"11","key":"e_1_2_12_20_1","doi-asserted-by":"crossref","DOI":"10.14309\/ctg.0000000000000771","article-title":"Deep learning and automatic differentiation of pancreatic lesions in endoscopic ultrasound: a transatlantic study","volume":"15","author":"Saraiva MM","year":"2024","journal-title":"Clin Transl Gastroenterol"},{"issue":"1","key":"e_1_2_12_21_1","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1111\/jgh.16814","article-title":"Enhancing detection of various pancreatic lesions on endoscopic ultrasound through artificial intelligence: a basis for computer\u2010aided detection systems","volume":"40","author":"Konikoff T","year":"2025","journal-title":"J Gastroenterol Hepatol"},{"issue":"1","key":"e_1_2_12_22_1","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1186\/s12880-025-01555-x","article-title":"Endoscopic ultrasonography\u2010based intratumoral and peritumoral machine learning ultrasomics model for predicting the pathological grading of pancreatic neuroendocrine tumors","volume":"25","author":"Mo S","year":"2025","journal-title":"BMC Med Imaging"},{"key":"e_1_2_12_23_1","doi-asserted-by":"crossref","DOI":"10.3389\/fonc.2025.1442209","article-title":"EUS\u2010based intratumoral and peritumoral machine learning radiomics analysis for distinguishing pancreatic neuroendocrine tumors from pancreatic cancer","volume":"15","author":"Mo S","year":"2025","journal-title":"Front Oncol"},{"issue":"1","key":"e_1_2_12_24_1","doi-asserted-by":"crossref","first-page":"3383","DOI":"10.1038\/s41598-024-84749-7","article-title":"An endoscopic ultrasound\u2010based interpretable deep learning model and nomogram for distinguishing pancreatic neuroendocrine tumors from pancreatic cancer","volume":"15","author":"Yi N","year":"2025","journal-title":"Sci Rep"},{"issue":"4","key":"e_1_2_12_25_1","doi-asserted-by":"crossref","first-page":"542","DOI":"10.5858\/arpa.2016-0265-SA","article-title":"Whole slide imaging for analytical anatomic pathology and telepathology: practical applications today, promises, and perils","volume":"141","author":"Farris AB","year":"2017","journal-title":"Arch Pathol Lab Med"},{"issue":"1","key":"e_1_2_12_26_1","doi-asserted-by":"crossref","first-page":"11064","DOI":"10.1038\/s41598-020-67880-z","article-title":"Improving the accuracy of gastrointestinal neuroendocrine tumor grading with deep learning","volume":"10","author":"Govind D","year":"2020","journal-title":"Sci Rep"},{"key":"e_1_2_12_27_1","doi-asserted-by":"crossref","first-page":"183","DOI":"10.3389\/fmolb.2020.00183","article-title":"Predict ki\u201067 positive cells in H&E\u2010stained images using deep learning independently from IHC\u2010stained images","volume":"7","author":"Liu Y","year":"2020","journal-title":"Front Mol Biosci"},{"key":"e_1_2_12_28_1","doi-asserted-by":"crossref","first-page":"32","DOI":"10.4103\/jpi.jpi_37_20","article-title":"Comparing deep learning and immunohistochemistry in determining the site of origin for well\u2010differentiated neuroendocrine tumors","volume":"11","author":"Redemann J","year":"2020","journal-title":"J Pathol Inform"},{"key":"e_1_2_12_29_1","doi-asserted-by":"crossref","DOI":"10.3389\/fonc.2020.593211","article-title":"Predicting metastasis risk in pancreatic neuroendocrine tumors using deep learning image analysis","volume":"10","author":"Klimov S","year":"2020","journal-title":"Front Oncol"},{"issue":"1","key":"e_1_2_12_30_1","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1111\/apm.13190","article-title":"Automated assessment of Ki\u201067 proliferation index in neuroendocrine tumors by deep learning","volume":"130","author":"Vesterinen T","year":"2022","journal-title":"APMIS"},{"issue":"10","key":"e_1_2_12_31_1","doi-asserted-by":"crossref","DOI":"10.3390\/diagnostics12102340","article-title":"Artificial intelligence\u2010powered whole\u2010slide image analyzer reveals a distinctive distribution of tumor\u2010infiltrating lymphocytes in neuroendocrine neoplasms","volume":"12","author":"Cho HG","year":"2022","journal-title":"Diagnostics"},{"key":"e_1_2_12_32_1","doi-asserted-by":"crossref","first-page":"5665","DOI":"10.2147\/IJGM.S443952","article-title":"Use of novel open\u2010source deep learning platform for quantification of Ki\u201067 in neuroendocrine tumors \u2010 analytical validation","volume":"16","author":"Zehra T","year":"2023","journal-title":"Int J Gen Med"},{"key":"e_1_2_12_33_1","doi-asserted-by":"crossref","DOI":"10.1016\/j.ebiom.2022.104426","article-title":"An accurate prediction of the origin for bone metastatic cancer using deep learning on digital pathological images","volume":"87","author":"Zhu L","year":"2023","journal-title":"EBioMedicine"},{"key":"e_1_2_12_34_1","doi-asserted-by":"crossref","first-page":"1001","DOI":"10.1007\/s00428-024-03963-w","article-title":"Determination of Ki\u201067 indices in neuroendocrine tumours of the gastrointestinal tract: the past, the present, and the future","volume":"486","author":"Houpt JA","year":"2024","journal-title":"Virchows Arch"},{"issue":"10","key":"e_1_2_12_35_1","doi-asserted-by":"crossref","DOI":"10.1016\/j.xcrm.2024.101785","article-title":"Deep learning model with pathological knowledge for detection of colorectal neuroendocrine tumor","volume":"5","author":"Zheng K","year":"2024","journal-title":"Cell Rep Med"},{"issue":"1","key":"e_1_2_12_36_1","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1007\/s12022-025-09846-3","article-title":"Deep learning enabled scoring of pancreatic neuroendocrine tumors based on cancer infiltration patterns","volume":"36","author":"Koc S","year":"2025","journal-title":"Endocr Pathol"},{"key":"e_1_2_12_37_1","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2024.109443","article-title":"Automatic discrimination between neuroendocrine carcinomas and grade 3 neuroendocrine tumors by deep learning of H&E images","volume":"184","author":"Arrieta Legorburu A","year":"2025","journal-title":"Comput Biol Med"},{"issue":"11","key":"e_1_2_12_38_1","doi-asserted-by":"crossref","first-page":"1981","DOI":"10.1007\/s11548-019-02070-5","article-title":"Deep learning for World Health Organization grades of pancreatic neuroendocrine tumors on contrast\u2010enhanced magnetic resonance images: a preliminary study","volume":"14","author":"Gao X","year":"2019","journal-title":"Int J Comput Assist Radiol Surg"},{"issue":"11","key":"e_1_2_12_39_1","doi-asserted-by":"crossref","first-page":"1475","DOI":"10.1016\/j.jacr.2020.06.033","article-title":"Three\u2010dimensional neural network to automatically assess liver tumor burden change on consecutive liver MRIs","volume":"17","author":"Goehler A","year":"2020","journal-title":"J Am Coll Radiol"},{"key":"e_1_2_12_40_1","doi-asserted-by":"crossref","DOI":"10.3389\/fonc.2020.521831","article-title":"Application of radiomics analysis based on CT combined with machine learning in diagnostic of pancreatic neuroendocrine tumors Patient's pathological grades","volume":"10","author":"Zhang T","year":"2020","journal-title":"Front Oncol"},{"issue":"8","key":"e_1_2_12_41_1","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1530\/ERC-21-0064","article-title":"Predicting symptomatic mesenteric mass in small intestinal neuroendocrine tumors using radiomics","volume":"28","author":"Blazevic A","year":"2021","journal-title":"Endocr Relat Cancer"},{"issue":"11","key":"e_1_2_12_42_1","doi-asserted-by":"crossref","DOI":"10.3390\/cancers13112726","article-title":"Automatized hepatic tumor volume analysis of neuroendocrine liver metastases by Gd\u2010EOB MRI\u2010A deep\u2010learning model to support multidisciplinary cancer conference decision\u2010making","volume":"13","author":"Fehrenbach U","year":"2021","journal-title":"Cancers (Basel)"},{"key":"e_1_2_12_43_1","doi-asserted-by":"crossref","DOI":"10.3389\/fonc.2021.606677","article-title":"Application of CT\u2010based radiomics in discriminating pancreatic cystadenomas from pancreatic neuroendocrine tumors using machine learning methods","volume":"11","author":"Han X","year":"2021","journal-title":"Front Oncol"},{"issue":"9","key":"e_1_2_12_44_1","doi-asserted-by":"crossref","first-page":"3498","DOI":"10.1109\/JBHI.2021.3070708","article-title":"Accurate and feasible deep learning based semi\u2010automatic segmentation in CT for radiomics analysis in pancreatic neuroendocrine neoplasms","volume":"25","author":"Huang B","year":"2021","journal-title":"IEEE J Biomed Health Inform"},{"key":"e_1_2_12_45_1","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1200\/CCI.20.00121","article-title":"Quantitative computed tomography image analysis to predict pancreatic neuroendocrine tumor grade","volume":"5","author":"Pulvirenti A","year":"2021","journal-title":"JCO Clin Cancer Inform"},{"issue":"12","key":"e_1_2_12_46_1","doi-asserted-by":"crossref","first-page":"4139","DOI":"10.1007\/s00261-022-03663-6","article-title":"Classification of pancreatic cystic neoplasms using radiomic feature analysis is equivalent to an experienced academic radiologist: a step toward computer\u2010augmented diagnostics for radiologists","volume":"47","author":"Chu LC","year":"2022","journal-title":"Abdom Radiol (NY)"},{"key":"e_1_2_12_47_1","doi-asserted-by":"crossref","DOI":"10.3389\/fonc.2022.843376","article-title":"Development and validation of a novel radiomics\u2010based nomogram with machine learning to preoperatively predict histologic grade in pancreatic neuroendocrine tumors","volume":"12","author":"Wang X","year":"2022","journal-title":"Front Oncol"},{"issue":"22","key":"e_1_2_12_48_1","doi-asserted-by":"crossref","DOI":"10.3390\/jcm11226789","article-title":"Radiomics combined with multiple machine learning algorithms in differentiating pancreatic ductal adenocarcinoma from pancreatic neuroendocrine tumor: more hands produce a stronger flame","volume":"11","author":"Zhang T","year":"2022","journal-title":"J Clin Med"},{"issue":"11","key":"e_1_2_12_49_1","doi-asserted-by":"crossref","first-page":"791","DOI":"10.1097\/RLI.0000000000000992","article-title":"Automatic detection of pancreatic lesions and Main pancreatic duct dilatation on portal venous CT scans using deep learning","volume":"58","author":"Abi Nader C","year":"2023","journal-title":"Invest Radiol"},{"key":"e_1_2_12_50_1","doi-asserted-by":"crossref","DOI":"10.1016\/j.eclinm.2023.102269","article-title":"Development and validation of CT\u2010based radiomics deep learning signatures to predict lymph node metastasis in non\u2010functional pancreatic neuroendocrine tumors: a multicohort study","volume":"65","author":"Gu W","year":"2023","journal-title":"EClinicalMedicine"},{"issue":"1","key":"e_1_2_12_51_1","doi-asserted-by":"crossref","DOI":"10.3390\/bioengineering10010083","article-title":"Pancreatic mass characterization using IVIM\u2010DKI MRI and machine learning\u2010based multi\u2010parametric texture analysis","volume":"10","author":"Malagi AV","year":"2023","journal-title":"Bioengineering (Basel)"},{"issue":"6","key":"e_1_2_12_52_1","doi-asserted-by":"crossref","first-page":"4412","DOI":"10.1007\/s00330-022-09351-9","article-title":"Prediction of the characteristics of aggressiveness of pancreatic neuroendocrine neoplasms (PanNENs) based on CT radiomic features","volume":"33","author":"Mori M","year":"2023","journal-title":"Eur Radiol"},{"issue":"32","key":"e_1_2_12_53_1","doi-asserted-by":"crossref","first-page":"2185","DOI":"10.2217\/fon-2022-1136","article-title":"The use of deep learning models to predict progression\u2010free survival in patients with neuroendocrine tumors","volume":"19","author":"Pavel M","year":"2023","journal-title":"Future Oncol"},{"key":"e_1_2_12_54_1","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.clinimag.2023.01.008","article-title":"Histogram array and convolutional neural network of DWI for differentiating pancreatic ductal adenocarcinomas from solid pseudopapillary neoplasms and neuroendocrine neoplasms","volume":"96","author":"Shi YJ","year":"2023","journal-title":"Clin Imaging"},{"issue":"5","key":"e_1_2_12_55_1","doi-asserted-by":"crossref","first-page":"1898","DOI":"10.1016\/j.acra.2023.10.040","article-title":"Machine learning methods based on CT features differentiate G1\/G2 from G3 pancreatic neuroendocrine tumors","volume":"31","author":"Chen HY","year":"2024","journal-title":"Acad Radiol"},{"issue":"1","key":"e_1_2_12_56_1","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.diii.2023.08.002","article-title":"Accurate non\u2010invasive grading of nonfunctional pancreatic neuroendocrine tumors with a CT derived radiomics signature","volume":"105","author":"Javed AA","year":"2024","journal-title":"Diagn Interv Imaging"},{"issue":"3","key":"e_1_2_12_57_1","first-page":"1994","article-title":"A radiomics\u2010based interpretable model to predict the pathological grade of pancreatic neuroendocrine tumors","volume":"34","author":"Ye JY","year":"2024","journal-title":"Eur Radiol"},{"issue":"1","key":"e_1_2_12_58_1","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.diii.2024.08.003","article-title":"Radiomics machine learning algorithm facilitates detection of small pancreatic neuroendocrine tumors on CT","volume":"106","author":"Lopez\u2010Ramirez F","year":"2025","journal-title":"Diagn Interv Imaging"},{"issue":"3","key":"e_1_2_12_59_1","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1056\/NEJMoa1316158","article-title":"Lanreotide in metastatic enteropancreatic neuroendocrine tumors","volume":"371","author":"Caplin ME","year":"2014","journal-title":"N Engl J Med"},{"issue":"5","key":"e_1_2_12_60_1","doi-asserted-by":"crossref","DOI":"10.3390\/diagnostics11050870","article-title":"A [68Ga]Ga\u2010DOTANOC PET\/CT radiomic model for non\u2010invasive prediction of tumour grade in pancreatic neuroendocrine Tumours","volume":"11","author":"Bevilacqua A","year":"2021","journal-title":"Diagnostics (Basel)"},{"issue":"1","key":"e_1_2_12_61_1","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1186\/s13550-021-00839-x","article-title":"Automated liver lesion detection in (68)Ga DOTATATE PET\/CT using a deep fully convolutional neural network","volume":"11","author":"Wehrend J","year":"2021","journal-title":"EJNMMI Res"},{"issue":"1","key":"e_1_2_12_62_1","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1186\/s13550-022-00901-2","article-title":"A convolutional neural network for total tumor segmentation in [(64)Cu]Cu\u2010DOTATATE PET\/CT of patients with neuroendocrine neoplasms","volume":"12","author":"Carlsen EA","year":"2022","journal-title":"EJNMMI Res"},{"issue":"1","key":"e_1_2_12_63_1","doi-asserted-by":"crossref","first-page":"13419","DOI":"10.1038\/s41598-022-17611-3","article-title":"CT\u2010derived body composition analysis could possibly replace DXA and BIA to monitor NET\u2010patients","volume":"12","author":"Kroll L","year":"2022","journal-title":"Sci Rep"},{"issue":"8","key":"e_1_2_12_64_1","doi-asserted-by":"crossref","first-page":"892","DOI":"10.1097\/MNM.0000000000001592","article-title":"Differences in tumor\u2010to\u2010normal organ SUV ratios measured with 68 Ga\u2010DOTATATE PET compared with 177 Lu\u2010DOTATATE SPECT in patients with neuroendocrine tumors","volume":"43","author":"Wong KK","year":"2022","journal-title":"Nucl Med Commun"},{"issue":"6","key":"e_1_2_12_65_1","doi-asserted-by":"crossref","first-page":"951","DOI":"10.2967\/jnumed.122.264826","article-title":"An investigation of lesion detection accuracy for artificial intelligence\u2010based denoising of low\u2010dose (64)Cu\u2010DOTATATE PET imaging in patients with neuroendocrine neoplasms","volume":"64","author":"Loft M","year":"2023","journal-title":"J Nucl Med"},{"issue":"5","key":"e_1_2_12_66_1","doi-asserted-by":"crossref","first-page":"897","DOI":"10.1007\/s11307-023-01832-7","article-title":"A machine learning approach using [(18)F]FDG PET\u2010based radiomics for prediction of tumor grade and prognosis in pancreatic neuroendocrine tumor","volume":"25","author":"Park YJ","year":"2023","journal-title":"Mol Imaging Biol"},{"issue":"11","key":"e_1_2_12_67_1","doi-asserted-by":"crossref","first-page":"7222","DOI":"10.1002\/mp.16746","article-title":"A radiomic\u2010 and dosiomic\u2010based machine learning regression model for pretreatment planning in (177) Lu\u2010DOTATATE therapy","volume":"50","author":"Plachouris D","year":"2023","journal-title":"Med Phys"},{"issue":"6","key":"e_1_2_12_68_1","doi-asserted-by":"crossref","first-page":"1516","DOI":"10.1007\/s00259-024-06618-9","article-title":"Deep transformer\u2010based personalized dosimetry from SPECT\/CT images: a hybrid approach for [(177)Lu]Lu\u2010DOTATATE radiopharmaceutical therapy","volume":"51","author":"Mansouri Z","year":"2024","journal-title":"Eur J Nucl Med Mol Imaging"},{"issue":"9","key":"e_1_2_12_69_1","doi-asserted-by":"crossref","first-page":"2774","DOI":"10.1007\/s00259-024-06730-w","article-title":"Preoperative assessment of lymph nodal metastases with [(68)Ga]Ga\u2010DOTATOC PET radiomics for improved surgical planning in well\u2010differentiated pancreatic neuroendocrine tumours","volume":"51","author":"Mapelli P","year":"2024","journal-title":"Eur J Nucl Med Mol Imaging"},{"issue":"3","key":"e_1_2_12_70_1","doi-asserted-by":"crossref","DOI":"10.3390\/bioengineering11030226","article-title":"Improving generalizability of PET DL algorithms: list\u2010mode reconstructions improve DOTATATE PET hepatic lesion detection performance","volume":"11","author":"Yang X","year":"2024","journal-title":"Bioengineering"},{"issue":"4","key":"e_1_2_12_71_1","doi-asserted-by":"crossref","first-page":"565","DOI":"10.2967\/jnumed.124.267809","article-title":"Full\u2010body tumor response heterogeneity of metastatic neuroendocrine tumor patients undergoing peptide receptor radiopharmaceutical therapy","volume":"66","author":"Santoro\u2010Fernandes V","year":"2025","journal-title":"J Nucl Med"},{"key":"e_1_2_12_72_1","doi-asserted-by":"crossref","first-page":"17 (3)","DOI":"10.3390\/cancers17030407","article-title":"Exploring artificial intelligence biases in predictive models for cancer diagnosis","volume":"17","author":"Smiley A","year":"2025","journal-title":"Cancers (Basel)"},{"issue":"2","key":"e_1_2_12_73_1","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1148\/radiol.2019191586","article-title":"Ethics of artificial intelligence in radiology: summary of the joint European and north American multisociety statement","volume":"293","author":"Geis JR","year":"2019","journal-title":"Radiology"},{"issue":"11","key":"e_1_2_12_74_1","doi-asserted-by":"crossref","first-page":"2929","DOI":"10.1038\/s41591-023-02608-w","article-title":"The value of standards for health datasets in artificial intelligence\u2010based applications","volume":"29","author":"Arora A","year":"2023","journal-title":"Nat Med"},{"issue":"10","key":"e_1_2_12_75_1","article-title":"The impact of artificial intelligence on optimizing diagnosis and treatment plans for rare genetic disorders","volume":"15","author":"Abdallah S","year":"2023","journal-title":"Cureus"},{"key":"e_1_2_12_76_1","doi-asserted-by":"crossref","DOI":"10.3389\/fonc.2025.1456557","article-title":"Commentary: predicting histologic grades for pancreatic neuroendocrine tumors by radiologic image\u2010based artificial intelligence: a systematic review and meta\u2010analysis","volume":"15","author":"Merola EM","year":"2025","journal-title":"Frontiers in Oncology"},{"issue":"4","key":"e_1_2_12_77_1","doi-asserted-by":"crossref","DOI":"10.1016\/j.heliyon.2024.e26297","article-title":"Ethical and regulatory challenges of AI technologies in healthcare: a narrative review","volume":"10","author":"Mennella C","year":"2024","journal-title":"Heliyon"},{"issue":"10","key":"e_1_2_12_78_1","doi-asserted-by":"crossref","first-page":"728","DOI":"10.1016\/j.crad.2021.03.009","article-title":"Challenges and opportunities for artificial intelligence in oncological imaging","volume":"76","author":"Cheung HMC","year":"2021","journal-title":"Clin Radiol"},{"key":"e_1_2_12_79_1","doi-asserted-by":"crossref","DOI":"10.2196\/53616","article-title":"Benefits and risks of AI in health care: narrative review","volume":"13","author":"Chustecki M","year":"2024","journal-title":"Interact J Med Res"},{"key":"e_1_2_12_80_1","unstructured":"U.S. Food and Drug Administration.Artificial Intelligence\/Machine Learning\u2010Enabled Device Software Functions: Draft Guidance for Industry and FDA Staff.2025."},{"key":"e_1_2_12_81_1","unstructured":"European Parliament and Council of the European Union.Artificial Intelligence Act. Official Journal of the European Union.2024."}],"container-title":["Journal of Neuroendocrinology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/jne.70087","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T10:34:37Z","timestamp":1763116477000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1111\/jne.70087"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,28]]},"references-count":80,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2025,11]]}},"alternative-id":["10.1111\/jne.70087"],"URL":"https:\/\/doi.org\/10.1111\/jne.70087","archive":["Portico"],"relation":{"has-review":[{"id-type":"doi","id":"10.1111\/JNE.70087\/v1\/decision1","asserted-by":"object"},{"id-type":"doi","id":"10.1111\/JNE.70087\/v2\/response1","asserted-by":"object"},{"id-type":"doi","id":"10.1111\/JNE.70087\/v2\/review2","asserted-by":"object"},{"id-type":"doi","id":"10.1111\/JNE.70087\/v1\/review3","asserted-by":"object"},{"id-type":"doi","id":"10.1111\/JNE.70087\/v1\/review2","asserted-by":"object"},{"id-type":"doi","id":"10.1111\/JNE.70087\/v1\/review1","asserted-by":"object"},{"id-type":"doi","id":"10.1111\/JNE.70087\/v2\/review1","asserted-by":"object"},{"id-type":"doi","id":"10.1111\/JNE.70087\/v2\/decision1","asserted-by":"object"}]},"ISSN":["0953-8194","1365-2826"],"issn-type":[{"value":"0953-8194","type":"print"},{"value":"1365-2826","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,28]]},"assertion":[{"value":"2025-05-27","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-08-13","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-08-28","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70087"}}