Artificial Intelligence and Machine Learning in the Diagnosis and Management of Gastroenteropancreatic Neuroendocrine Neoplasms-A Scoping Review

Abstract

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.

Keywords: GEP-NETs; Pan-NENs; SI-NETS; artificial intelligence; carcinoid; deep learning; gastroenteropancreatic; machine learning; neuroendocrine neoplasms; neuroendocrine tumors.

Figure 1

Flowchart depicting the selection process of sources of evidence. ML: machine learning; DL: deep learning.

Figure 2

Geographic distribution of the studies included in the review. The darker the hue, the larger the number of studies coming from this particular country.

Figure 3

Temporal distribution of the studies included in the review according to year of publication.

Figure 4

Distribution of studies by type of NET analyzed. GEP: gastroenteropancreatic; GI: gastrointestinal.

Figure 5

Distribution of studies by source of data. CT: computed tomography; EUS: endoscopic ultrasound; MRI: magnetic resonance imaging; PET: positron emission tomography.

Figure 6

The most frequently appearing artificial intelligence algorithms within the included studies. SVC: Supporting Vector Classification; SVM: Supporting Vector Machine; CNN: Convolutional Neural Network; RF: Random Forest; LR: Logistic Regression; DT: Decision Tree; GBDT: Gradient Boosting Decision Tree; MLP: Multi-Layer Perceptron; NB/GNB: (Gaussian) Naïve Bayes; LDA: Linear Discriminant Analysis.