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

Nguon, Leang Sim; Seo, Kangwon; Lim, Jung-Hyun; Song, Tae-Jun; Cho, Sung Hyun; Park, Jin-Seok; Park, Suhyun

doi:10.3390/diagnostics11061052

Cited by 23 publications

(21 citation statements)

References 32 publications

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“…Particularly, studies focusing on the detection and differentiation between mucinous and non-mucinous pancreatic cystic lesions based on EUS images are scarce. To the authors’ knowledge, only one study focusing on this subject has been previously published [ 13 ]. Nevertheless, the development of AI algorithms for the evaluation of pancreatic diseases is a subject of increasing interest [ 14 , 15 , 16 , 17 , 18 , 19 ].…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, we developed a deep learning algorithm for the automatic classification of PCLs as mucinous vs. non-mucinous. Nguon and coworkers implemented a CNN model to differentiate MCN and SCA using EUS images [ 13 ]. Their algorithm achieved an overall accuracy around 80%, which is in line with the classification performance of experienced endosonographers.…”

Section: Discussionmentioning

confidence: 99%

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Deep Learning for Automatic Differentiation of Mucinous versus Non-Mucinous Pancreatic Cystic Lesions: A Pilot Study

et al. 2022

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Endoscopic ultrasound (EUS) morphology can aid in the discrimination between mucinous and non-mucinous pancreatic cystic lesions (PCLs) but has several limitations that can be overcome by artificial intelligence. We developed a convolutional neural network (CNN) algorithm for the automatic diagnosis of mucinous PCLs. Images retrieved from videos of EUS examinations for PCL characterization were used for the development, training, and validation of a CNN for mucinous cyst diagnosis. The performance of the CNN was measured calculating the area under the receiving operator characteristic curve (AUC), sensitivity, specificity, and positive and negative predictive values. A total of 5505 images from 28 pancreatic cysts were used (3725 from mucinous lesions and 1780 from non-mucinous cysts). The model had an overall accuracy of 98.5%, sensitivity of 98.3%, specificity of 98.9% and AUC of 1. The image processing speed of the CNN was 7.2 ms per frame. We developed a deep learning algorithm that differentiated mucinous and non-mucinous cysts with high accuracy. The present CNN may constitute an important tool to help risk stratify PCLs.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Deep Learning for Automatic Differentiation of Mucinous versus Non-Mucinous Pancreatic Cystic Lesions: A Pilot Study

et al. 2022

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“…In comparison, the diagnostic accuracy of human interpretation was 56%, and that of the presence of an intracystic mural nodule was 68% [ 41 ]. AI has also been able to differentiate between types of PCLs: CNNs have been used to differentiate between EUS morphologies of MCNs ( n = 60) and SCAs ( n = 49) with 82.75% accuracy and a 0.88 (95% CI: 0.817–0.930) area under curve score [ 42 ].…”

Section: Ai and Eus In Pcl Risk Stratificationmentioning

confidence: 99%

Application of Artificial Intelligence in the Management of Pancreatic Cystic Lesions

et al. 2022

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The rate of incidentally detected pancreatic cystic lesions (PCLs) has increased over the past decade and was recently reported at 8%. These lesions pose a unique challenge, as each subtype of PCL carries a different risk of malignant transformation, ranging from 0% (pancreatic pseudocyst) to 34–68% (main duct intraductal papillary mucinous neoplasm). It is imperative to correctly risk-stratify the malignant potential of these lesions in order to provide the correct care course for the patient, ranging from monitoring to surgical intervention. Even with the multiplicity of guidelines (i.e., the American Gastroenterology Association guidelines and Fukuoka/International Consensus guidelines) and multitude of diagnostic information, risk stratification of PCLs falls short. Studies have reported that 25–64% of patients undergoing PCL resection have pancreatic cysts with no malignant potential, and up to 78% of mucin-producing cysts resected harbor no malignant potential on pathological evaluation. Clinicians are now incorporating artificial intelligence technology to aid in the management of these difficult lesions. This review article focuses on advancements in artificial intelligence within digital pathomics, radiomics, and genomics as they apply to the diagnosis and risk stratification of PCLs.

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“…Deep learning models are being developed to extract complex features from image data and are widely used in the field of medical imaging [ 8 ]. Previous studies have used deep learning models for the classification and segmentation of pancreatic EUS images [ 9 , 10 , 11 , 12 , 13 ]. Kuwahara et al [ 9 ] proved that a deep learning model (ResNet50) can diagnose the malignancy of Intraductal Papillary Mucinous Neoplasm (IPMN).…”

Section: Introductionmentioning

confidence: 99%

Semantic Segmentation of Pancreatic Cancer in Endoscopic Ultrasound Images Using Deep Learning Approach

Seo

Lim

Seo

et al. 2022

Cancers

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Endoscopic ultrasonography (EUS) plays an important role in diagnosing pancreatic cancer. Surgical therapy is critical to pancreatic cancer survival and can be planned properly, with the characteristics of the target cancer determined. The physical characteristics of the pancreatic cancer, such as size, location, and shape, can be determined by semantic segmentation of EUS images. This study proposes a deep learning approach for the segmentation of pancreatic cancer in EUS images. EUS images were acquired from 150 patients diagnosed with pancreatic cancer. A network with deep attention features (DAF-Net) is proposed for pancreatic cancer segmentation using EUS images. The performance of the deep learning models (U-Net, Attention U-Net, and DAF-Net) was evaluated by 5-fold cross-validation. For the evaluation metrics, the Dice similarity coefficient (DSC), intersection over union (IoU), receiver operating characteristic (ROC) curve, and area under the curve (AUC) were chosen. Statistical analysis was performed for different stages and locations of the cancer. DAF-Net demonstrated superior segmentation performance for the DSC, IoU, AUC, sensitivity, specificity, and precision with scores of 82.8%, 72.3%, 92.7%, 89.0%, 98.1%, and 85.1%, respectively. The proposed deep learning approach can provide accurate segmentation of pancreatic cancer in EUS images and can effectively assist in the planning of surgical therapies.

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

Cited by 23 publications

References 32 publications

Deep Learning for Automatic Differentiation of Mucinous versus Non-Mucinous Pancreatic Cystic Lesions: A Pilot Study

Deep Learning for Automatic Differentiation of Mucinous versus Non-Mucinous Pancreatic Cystic Lesions: A Pilot Study

Application of Artificial Intelligence in the Management of Pancreatic Cystic Lesions

Semantic Segmentation of Pancreatic Cancer in Endoscopic Ultrasound Images Using Deep Learning Approach

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