Management of Cystic Neoplasms of the Pancreas Including IPMNs

Aims. Mucinous cystic neoplasms (MCNs) are cystic neoplasms with mucinous epithelium surrounded by ovarian-like stroma. Extraovarian MCN occurring in the liver and pancreas have been well characterized. However, only rare case reports of MCN arising outside of these locations have been reported. MCNs arising in unusual locations should enter the differential diagnosis of mucinous intra-abdominal tumors and must be distinguished from more common mimics. Therefore, we aimed to examine a series of MCNs of the retroperitoneum and mesentery to characterize the clinicopathologic features of this entity. Methods and results. Seven MCNs arising in the abdominal mesentery or retroperitoneum were retrospectively identified. A clinicopathologic, histologic, and immunohistochemical (keratin 7, keratin 19, keratin 20, calretinin, inhibin-α, steroidogenic factor-1 (SF-1), estrogen receptor (ER), progesterone receptor (PR), PAX8, CDX2, and CD10) analysis was performed. All 7 MCNs were from females with a median age of 41 years old and a median size of 8 cm. All cases demonstrated mucinous with or without concomitant non-mucinous epithelium overlying spindle cell ovarian-like stroma. Luteinized cells were noted. The epithelium was positive for keratin 7 and keratin 19 in all 7 cases, while the stroma expressed ER, PR, and SF-1 in all cases stained. Calretinin was focally positive in the stroma of 3 of 7 cases, while inhibin-α was focally expressed in 5 of 6 cases. Conclusions. These results highlight the clinicopathologic, histologic, and immunophenotypic similarities between MCNs of the mesentery, retroperitoneum, pancreas, and liver. Overlapping features suggest a common histogenesis for all MCNs, which could include periductal fetal mesenchyme, aberrant migration of primordial germ cells, or abnormal differentiation or metaplasia of the embryonic coelomic epithelium.

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Deep Learning-Based Differentiation between Mucinous Cystic Neoplasm and Serous Cystic Neoplasm in the Pancreas Using Endoscopic Ultrasonography

Diagnostics ◽

10.3390/diagnostics11061052 ◽

2021 ◽

Vol 11 (6) ◽

pp. 1052

Author(s):

Leang Sim Nguon ◽

Kangwon Seo ◽

Jung-Hyun Lim ◽

Tae-Jun Song ◽

Sung-Hyun Cho ◽

...

Keyword(s):

Decision Making ◽

Deep Learning ◽

Network Model ◽

Endoscopic Ultrasonography ◽

Data Augmentation ◽

Clinical Information ◽

Training Data ◽

Fine Tuning ◽

Cystic Neoplasm ◽

Cystic Neoplasms

Mucinous cystic neoplasms (MCN) and serous cystic neoplasms (SCN) account for a large portion of solitary pancreatic cystic neoplasms (PCN). In this study we implemented a convolutional neural network (CNN) model using ResNet50 to differentiate between MCN and SCN. The training data were collected retrospectively from 59 MCN and 49 SCN patients from two different hospitals. Data augmentation was used to enhance the size and quality of training datasets. Fine-tuning training approaches were utilized by adopting the pre-trained model from transfer learning while training selected layers. Testing of the network was conducted by varying the endoscopic ultrasonography (EUS) image sizes and positions to evaluate the network performance for differentiation. The proposed network model achieved up to 82.75% accuracy and a 0.88 (95% CI: 0.817–0.930) area under curve (AUC) score. The performance of the implemented deep learning networks in decision-making using only EUS images is comparable to that of traditional manual decision-making using EUS images along with supporting clinical information. Gradient-weighted class activation mapping (Grad-CAM) confirmed that the network model learned the features from the cyst region accurately. This study proves the feasibility of diagnosing MCN and SCN using a deep learning network model. Further improvement using more datasets is needed.

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