期刊论文详细信息
Diagnostics
Artificial Intelligence and Machine Learning in the Diagnosis and Management of Gastroenteropancreatic Neuroendocrine Neoplasms—A Scoping Review
Athanasios G. Pantelis1  Dimitris P. Lapatsanis1  Panagiota A. Panagopoulou2 
[1] 4th Department of Surgery, Evaggelismos General Hospital of Athens, 10676 Athens, Greece;Protypo Dialysis Center of Piraeus, 18233 Piraeus, Greece;
关键词: neuroendocrine tumors;    neuroendocrine neoplasms;    carcinoid;    gastroenteropancreatic;    GEP-NETs;    Pan-NENs;   
DOI  :  10.3390/diagnostics12040874
来源: DOAJ
【 摘 要 】

Neuroendocrine neoplasms (NENs) and tumors (NETs) are rare neoplasms that may affect any part of the gastrointestinal system. In this scoping review, we attempt to map existing evidence on the role of artificial intelligence, machine learning and deep learning in the diagnosis and management of NENs of the gastrointestinal system. After implementation of inclusion and exclusion criteria, we retrieved 44 studies with 53 outcome analyses. We then classified the papers according to the type of studied NET (26 Pan-NETs, 59.1%; 3 metastatic liver NETs (6.8%), 2 small intestinal NETs, 4.5%; colorectal, rectal, non-specified gastroenteropancreatic and non-specified gastrointestinal NETs had from 1 study each, 2.3%). The most frequently used AI algorithms were Supporting Vector Classification/Machine (14 analyses, 29.8%), Convolutional Neural Network and Random Forest (10 analyses each, 21.3%), Random Forest (9 analyses, 19.1%), Logistic Regression (8 analyses, 17.0%), and Decision Tree (6 analyses, 12.8%). There was high heterogeneity on the description of the prediction model, structure of datasets, and performance metrics, whereas the majority of studies did not report any external validation set. Future studies should aim at incorporating a uniform structure in accordance with existing guidelines for purposes of reproducibility and research quality, which are prerequisites for integration into clinical practice.

【 授权许可】

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