Timely synergic surgical and radiological aggressiveness improves perioperative mortality after hemorrhagic complication in Whipple procedure

Chierici, Andrea; Intotero, Marcello; Granieri, Stefano; Paleino, Sissi; Flocchini, Giovanni; Germini, Alessandro; Cotsoglou, Christian

doi:10.1016/j.hbpd.2020.12.003

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…It is estimated that two-thirds of surgically resected PCLs demonstrate only LGD or benign neoplasms, indicating a significant rate of overtreatment and unnecessary surgeries [9]. Considering the high morbidity and mortality of Whipple procedures and pancreatectomies, these data point to an unacceptably high false-positive rate with current diagnostic modalities [10,11]. Conversely, several series report that up to 37% of invasive cancers are discovered during routine follow-ups of suspected BD-IPMNs, suggesting possible delays in diagnosis [12].…”

Section: Introductionmentioning

confidence: 99%

Improving Pancreatic Cyst Management: Artificial Intelligence-Powered Prediction of Advanced Neoplasms through Endoscopic Ultrasound-Guided Confocal Endomicroscopy

Jiang,

Chao,

Cao

et al. 2023

Biomimetics

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Despite the increasing rate of detection of incidental pancreatic cystic lesions (PCLs), current standard-of-care methods for their diagnosis and risk stratification remain inadequate. Intraductal papillary mucinous neoplasms (IPMNs) are the most prevalent PCLs. The existing modalities, including endoscopic ultrasound and cyst fluid analysis, only achieve accuracy rates of 65–75% in identifying carcinoma or high-grade dysplasia in IPMNs. Furthermore, surgical resection of PCLs reveals that up to half exhibit only low-grade dysplastic changes or benign neoplasms. To reduce unnecessary and high-risk pancreatic surgeries, more precise diagnostic techniques are necessary. A promising approach involves integrating existing data, such as clinical features, cyst morphology, and data from cyst fluid analysis, with confocal endomicroscopy and radiomics to enhance the prediction of advanced neoplasms in PCLs. Artificial intelligence and machine learning modalities can play a crucial role in achieving this goal. In this review, we explore current and future techniques to leverage these advanced technologies to improve diagnostic accuracy in the context of PCLs.

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