Artificial intelligence in pancreatic intraductal papillary mucinous neoplasm imaging: A systematic review

Abstract

Based on the Fukuoka and Kyoto international consensus guidelines, the current clinical management of intraductal papillary mucinous neoplasm (IPMN) largely depends on imaging features. While these criteria are highly sensitive in detecting high-risk IPMN, they lack specificity, resulting in surgical overtreatment. Artificial Intelligence (AI)-based medical image analysis has the potential to augment the clinical management of IPMNs by improving diagnostic accuracy. Based on a systematic review of the academic literature on AI in IPMN imaging, 1041 publications were identified of which 25 published studies were included in the analysis. The studies were stratified based on prediction target, underlying data type and imaging modality, patient cohort size, and stage of clinical translation and were subsequently analyzed to identify trends and gaps in the field. Research on AI in IPMN imaging has been increasing in recent years. The majority of studies utilized CT imaging to train computational models. Most studies presented computational models developed on single-center datasets (n = 11,44%) and included less than 250 patients (n = 18,72%). Methodologically, convolutional neural network (CNN)-based algorithms were most commonly used. Thematically, most studies reported models augmenting differential diagnosis (n = 9,36%) or risk stratification (n = 10,40%) rather than IPMN detection (n = 5,20%) or IPMN segmentation (n = 2,8%). This systematic review provides a comprehensive overview of the research landscape of AI in IPMN imaging. Computational models have potential to enhance the accurate and precise stratification of patients with IPMN. Multicenter collaboration and datasets comprising various modalities are necessary to fully utilize this potential, alongside concerted efforts towards clinical translation.

Fig 1. Flowchart of the systematic review and meta-analysis according to the PRISMA 2020 statement for reporting systematic reviews.

Based on the systematic review of the publications on AI in IPMN imaging, 25 articles published between 2018 and 2023 were included in this analysis.

Fig 2. Published studies by year of publication and prediction target.

The studies are further categorized by the imaging modality used. The radius of each circle is proportional to the number of publications, with colors indicating imaging modality.

Fig 3. Overview of research on AI in IPMN imaging.

(A) Distribution of publications on AI in IPMN imaging based on prediction target (IPMN/cyst detection, cyst segmentation, differential diagnosis, risk stratification) and stage of clinical translation: internal validation (monocentric studies), external validation (multicentric studies), prospective clinical evaluation. Each dot represents a single publication, with the dot color indicating the imaging modality used. (B) Size of the total patient cohort and the patient cohort with IPMN. Colors indicate the imaging modality used, and symbol shapes indicate the number of centers involved in the study. One study evaluating segmentation did not document the number of IPMN patients in the underlying cohort. (C) Performance metrics - Accuracy (Acc), Area Under the Receiver Operating Curve (AUC), Sensitivity (Sen), and Specificity (Spec) - for the proposed model across prediction targets in each study. Colors represent the imaging modalities used. For segmentation, the DSC is presented. Not all studies reported every metric.