| 摘要: |
| 随着影像学检查的普及和人均寿命的延长，胰腺囊性肿瘤（PCN）的检出率越来越高。不同亚型的PCN具有不同的恶变风险，对PCN恶变风险进行准确分层能够为患者提供正确的监测方案并指导手术决策。近年来多项指南明确了多个影像学特征（囊肿大小、壁结节和主胰管管径）为PCN恶变的危险因素，但单个特征衡量标准不一且诊断能力有限，而综合多个特征的模型诊断能力表现欠佳。本文围绕指南中影像学危险因素在PCN恶变预测中的价值及影像组学和人工智能的应用进展进行综述，旨在为PCN的影像学研究提供方向，提高术前PCN危险分层的准确性。 |
| 关键词: 胰腺囊性肿瘤 影像组学 人工智能 X线计算机体层摄影术 磁共振成像 |
| DOI：10.16781/j.CN31-2187/R.20230453 |
| 投稿时间：2023-08-08修订日期：2023-11-19 |
| 基金项目:国家自然科学基金（81871352，82171915，82171930，82271972，82371955），上海市科学技术委员会自然科学基金（21ZR1478500，21Y11910300），上海市申康医院发展中心临床研究项目（SHDC2022CRD028）. |
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| Imaging diagnosis of malignant risk of pancreatic cystic neoplasms: advances and difficulties |
| YUAN Xiaohan,BIAN Yun* |
(Department of Radiology, The First Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai 200433, China
*Corresponding author) |
| Abstract: |
| With the popularity of imaging examination and the increase in average life expectancy, the detection rate of pancreatic cystic neoplasm (PCN) is also increasing.Different subtypes of PCN have different risks of malignancy, therefore, accurate stratification of the malignant potential is crucial for providing surveillance plans and making surgical decision.In recent years, many guidelines have identified several imaging features (cyst size, mural nodules, and main pancreatic duct diameter) as risk factors for PCN malignancy.However, the measurement standards for individual feature are not uniform and their diagnostic capabilities are limited; models that integrate multiple features perform poorly in diagnosis.This article reviews the value of imaging risk factors in PCN malignancy prediction and the application of radiomics and artificial intelligence, providing direction for further imaging research and improving the accuracy of preoperative PCN risk stratification. |
| Key words: pancreatic cystic neoplasms radiomics artificial intelligence X-ray computed tomography magnetic resonance imaging |
