Granata Vincenza, Fusco Roberta, Setola Sergio Venanzio, Galdiero Roberta, Maggialetti Nicola, Silvestro Lucrezia, De Bellis Mario, Di Girolamo Elena, Grazzini Giulia, Chiti Giuditta, Brunese Maria Chiara, Belli Andrea, Patrone Renato, Palaia Raffaele, Avallone Antonio, Petrillo Antonella, Izzo Francesco
Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli, 80131 Naples, Italy.
Medical Oncology Division, Igea SpA, 41012 Napoli, Italy.
Cancers (Basel). 2023 Jan 5;15(2):351. doi: 10.3390/cancers15020351.
Pancreatic cancer (PC) is one of the deadliest cancers, and it is responsible for a number of deaths almost equal to its incidence. The high mortality rate is correlated with several explanations; the main one is the late disease stage at which the majority of patients are diagnosed. Since surgical resection has been recognised as the only curative treatment, a PC diagnosis at the initial stage is believed the main tool to improve survival. Therefore, patient stratification according to familial and genetic risk and the creation of screening protocol by using minimally invasive diagnostic tools would be appropriate. Pancreatic cystic neoplasms (PCNs) are subsets of lesions which deserve special management to avoid overtreatment. The current PC screening programs are based on the annual employment of magnetic resonance imaging with cholangiopancreatography sequences (MR/MRCP) and/or endoscopic ultrasonography (EUS). For patients unfit for MRI, computed tomography (CT) could be proposed, although CT results in lower detection rates, compared to MRI, for small lesions. The actual major limit is the incapacity to detect and characterize the pancreatic intraepithelial neoplasia (PanIN) by EUS and MR/MRCP. The possibility of utilizing artificial intelligence models to evaluate higher-risk patients could favour the diagnosis of these entities, although more data are needed to support the real utility of these applications in the field of screening. For these motives, it would be appropriate to realize screening programs in research settings.
胰腺癌（PC）是最致命的癌症之一，其导致的死亡人数几乎与发病率相当。高死亡率有多种原因；主要原因是大多数患者确诊时疾病已处于晚期。由于手术切除被认为是唯一的治愈性治疗方法，因此早期诊断胰腺癌被视为提高生存率的主要手段。因此，根据家族和遗传风险对患者进行分层，并使用微创诊断工具制定筛查方案是合适的。胰腺囊性肿瘤（PCNs）是一类需要特殊管理以避免过度治疗的病变。当前的胰腺癌筛查项目基于每年进行的磁共振成像胰胆管造影序列（MR/MRCP）和/或内镜超声检查（EUS）。对于不适合进行MRI检查的患者，可以考虑使用计算机断层扫描（CT），不过与MRI相比，CT对小病变的检测率较低。目前的主要限制是EUS和MR/MRCP无法检测和表征胰腺上皮内瘤变（PanIN）。利用人工智能模型评估高风险患者的可能性可能有助于这些病变的诊断，尽管需要更多数据来支持这些应用在筛查领域的实际效用。出于这些原因，在研究环境中开展筛查项目是合适的。
