| 1. |
- Del Chiaro, M, et al.
(author)
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European evidence-based guidelines on pancreatic cystic neoplasms
- 2018
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In: Gut. - : BMJ. - 1468-3288 .- 0017-5749. ; 67:5, s. 789-804
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Journal article (peer-reviewed)abstract
- Evidence-based guidelines on the management of pancreatic cystic neoplasms (PCN) are lacking. This guideline is a joint initiative of the European Study Group on Cystic Tumours of the Pancreas, United European Gastroenterology, European Pancreatic Club, European-African Hepato-Pancreato-Biliary Association, European Digestive Surgery, and the European Society of Gastrointestinal Endoscopy. It replaces the 2013 European consensus statement guidelines on PCN. European and non-European experts performed systematic reviews and used GRADE methodology to answer relevant clinical questions on nine topics (biomarkers, radiology, endoscopy, intraductal papillary mucinous neoplasm (IPMN), mucinous cystic neoplasm (MCN), serous cystic neoplasm, rare cysts, (neo)adjuvant treatment, and pathology). Recommendations include conservative management, relative and absolute indications for surgery. A conservative approach is recommended for asymptomatic MCN and IPMN measuring <40 mm without an enhancing nodule. Relative indications for surgery in IPMN include a main pancreatic duct (MPD) diameter between 5 and 9.9 mm or a cyst diameter ≥40 mm. Absolute indications for surgery in IPMN, due to the high-risk of malignant transformation, include jaundice, an enhancing mural nodule >5 mm, and MPD diameter >10 mm. Lifelong follow-up of IPMN is recommended in patients who are fit for surgery. The European evidence-based guidelines on PCN aim to improve the diagnosis and management of PCN.
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| 2. |
- Scholten, L, et al.
(author)
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Pancreatic Cystic Neoplasms: Different Types, Different Management, New Guidelines
- 2018
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In: Visceral medicine. - : S. Karger AG. - 2297-4725 .- 2297-475X. ; 34:3, s. 173-177
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Journal article (peer-reviewed)abstract
- Pancreatic cystic neoplasms (PCN) include different types of cysts with various biological behavior. The most prevalent PCN are intraductal papillary mucinous neoplasm (IPMN), mucinous cystic neoplasm (MCN), and serous cystic neoplasm (SCN). Management of PCN should focus on the prevention of malignant progression, while avoiding unnecessary morbidity of surgery. This requires specialized centers with dedicated multidisciplinary PCN teams. The malignant potential of PCN varies enormously between the various types of PCN. A combination of computed tomography, magnetic resonance imaging/magnetic resonance cholangiopancreatography, and endoscopic ultrasound with or without fine needle aspiration is typically needed before a reliable diagnosis can be made. Several guidelines discuss the management of PCN; however, most of these are non-evidence-based without clear consensus on the optimal treatment and follow-up strategy. The 2018 European guidelines on PCN are the first evidence-based guidelines to include IPMN, MCN, SCN, and all other PCN. This guideline advises a more conservative approach to side-branch IPMN and MCN smaller than 40 mm and more often a surgical approach in IPMN with a main duct dilatation beyond 5 mm. The goal of this review is to summarize the different types and management of the most common PCN based on the current literature and guidelines.
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| 3. |
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| 4. |
- Kang, JS, et al.
(author)
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Risk prediction for malignant intraductal papillary mucinous neoplasm of the pancreas: logistic regression versus machine learning
- 2020
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In: Scientific reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 10:1, s. 20140-
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Journal article (peer-reviewed)abstract
- Most models for predicting malignant pancreatic intraductal papillary mucinous neoplasms were developed based on logistic regression (LR) analysis. Our study aimed to develop risk prediction models using machine learning (ML) and LR techniques and compare their performances. This was a multinational, multi-institutional, retrospective study. Clinical variables including age, sex, main duct diameter, cyst size, mural nodule, and tumour location were factors considered for model development (MD). After the division into a MD set and a test set (2:1), the best ML and LR models were developed by training with the MD set using a tenfold cross validation. The test area under the receiver operating curves (AUCs) of the two models were calculated using an independent test set. A total of 3,708 patients were included. The stacked ensemble algorithm in the ML model and variable combinations containing all variables in the LR model were the most chosen during 200 repetitions. After 200 repetitions, the mean AUCs of the ML and LR models were comparable (0.725 vs. 0.725). The performances of the ML and LR models were comparable. The LR model was more practical than ML counterpart, because of its convenience in clinical use and simple interpretability.
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