Cyst Fluid Carcinoembryonic Antigen Level Difference between Mucinous Cystic Neoplasms and Intraductal Papillary Mucinous Neoplasms

Köker, İbrahim Hakkı; Ünver, Nurcan; Malya, Fatma Ümit; Uysal, Ömer; Keskin, Elmas Biberci; Şentürk, Hakan

doi:10.5946/ce.2020.083

Cited by 3 publications

(1 citation statement)

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“…Among the imaging modalities, EUS has the advantage of capturing internal structures [1,4,12,16] and can be combined with a biopsy by using fine-needle aspiration (FNA) [3,10]. Köker et al [17] performed the EUS-FNA to investigate the effectiveness of CEA level for differentiating low risk IPMN from low risk MCN. The study achieved an AUC around 0.930 for differentiating between low risk IPMN and low risk MCN.…”

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confidence: 99%

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

et al. 2021

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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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