Qadir Muhammad Ibtsaam, Baril Jackson A, Yip-Schneider Michele T, Schonlau Duane, Tran Thi Thanh Thoa, Schmidt C Max, Kolbinger Fiona R
Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA.
Division of Surgical Oncology, Department of Surgery, Indiana University School of Medicine, Indianapolis, IN, USA.
medRxiv. 2025 Jan 9:2025.01.08.25320130. doi: 10.1101/2025.01.08.25320130.
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.
根据福冈和京都国际共识指南，目前导管内乳头状黏液性肿瘤（IPMN）的临床管理很大程度上依赖于影像学特征。虽然这些标准在检测高危IPMN方面具有高度敏感性，但缺乏特异性，导致手术过度治疗。基于人工智能（AI）的医学图像分析有潜力通过提高诊断准确性来增强IPMN的临床管理。
基于对IPMN成像中AI学术文献的系统综述，确定了1041篇出版物，其中25篇已发表的研究纳入分析。这些研究根据预测目标、基础数据类型和成像方式、患者队列规模以及临床转化阶段进行分层，随后进行分析以确定该领域的趋势和差距。
近年来，IPMN成像中AI的研究一直在增加。大多数研究利用CT成像来训练计算模型。大多数研究展示了在单中心数据集上开发的计算模型（n = 11，44%），且纳入患者少于250例（n = 18，72%）。在方法上，最常用基于卷积神经网络（CNN）的算法。在主题方面，大多数研究报告的模型用于增强鉴别诊断（n = 9，36%）或风险分层（n = 10，40%），而非IPMN检测（n = 5，20%）或IPMN分割（n = 2，8%）。
本系统综述全面概述了IPMN成像中AI的研究现状。计算模型有潜力提高IPMN患者的准确和精确分层。要充分利用这一潜力，多中心合作以及包含各种模态的数据集是必要的，同时还需共同努力推动临床转化。
