Application of Artificial Intelligence in the Management of Pancreatic Cystic Lesions
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%...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Biomimetics |
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| Online Access: | https://www.mdpi.com/2313-7673/7/2/79 |
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| author | Shiva Rangwani Devarshi R. Ardeshna Brandon Rodgers Jared Melnychuk Ronald Turner Stacey Culp Wei-Lun Chao Somashekar G. Krishna |
| author_facet | Shiva Rangwani Devarshi R. Ardeshna Brandon Rodgers Jared Melnychuk Ronald Turner Stacey Culp Wei-Lun Chao Somashekar G. Krishna |
| author_sort | Shiva Rangwani |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T00:18:38Z |
| format | Article |
| id | doaj.art-1c890d412082413d9e8b2229973033b3 |
| institution | Directory Open Access Journal |
| issn | 2313-7673 |
| language | English |
| last_indexed | 2024-03-10T00:18:38Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomimetics |
| spelling | doaj.art-1c890d412082413d9e8b2229973033b32023-11-23T15:45:59ZengMDPI AGBiomimetics2313-76732022-06-01727910.3390/biomimetics7020079Application of Artificial Intelligence in the Management of Pancreatic Cystic LesionsShiva Rangwani0Devarshi R. Ardeshna1Brandon Rodgers2Jared Melnychuk3Ronald Turner4Stacey Culp5Wei-Lun Chao6Somashekar G. Krishna7Department of Internal Medicine, Ohio State University Wexner Medical Center, Columbus, OH 43210, USADepartment of Internal Medicine, Ohio State University Wexner Medical Center, Columbus, OH 43210, USACollege of Medicine, The Ohio State University, Columbus, OH 43210, USACollege of Medicine, The Ohio State University, Columbus, OH 43210, USACollege of Medicine, The Ohio State University, Columbus, OH 43210, USADepartment of Biomedical Informatics, The Ohio State University College of Medicine, Columbus, OH 43210, USADepartment of Computer Science and Engineering, The Ohio State University, Columbus, OH 43210, USADepartment of Gastroenterology, Hepatology, and Nutrition, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USAThe 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.https://www.mdpi.com/2313-7673/7/2/79pancreatic cystic lesionsartificial intelligenceradiomicsendoscopic ultrasoundIPMNgenomics |
| spellingShingle | Shiva Rangwani Devarshi R. Ardeshna Brandon Rodgers Jared Melnychuk Ronald Turner Stacey Culp Wei-Lun Chao Somashekar G. Krishna Application of Artificial Intelligence in the Management of Pancreatic Cystic Lesions Biomimetics pancreatic cystic lesions artificial intelligence radiomics endoscopic ultrasound IPMN genomics |
| title | Application of Artificial Intelligence in the Management of Pancreatic Cystic Lesions |
| title_full | Application of Artificial Intelligence in the Management of Pancreatic Cystic Lesions |
| title_fullStr | Application of Artificial Intelligence in the Management of Pancreatic Cystic Lesions |
| title_full_unstemmed | Application of Artificial Intelligence in the Management of Pancreatic Cystic Lesions |
| title_short | Application of Artificial Intelligence in the Management of Pancreatic Cystic Lesions |
| title_sort | application of artificial intelligence in the management of pancreatic cystic lesions |
| topic | pancreatic cystic lesions artificial intelligence radiomics endoscopic ultrasound IPMN genomics |
| url | https://www.mdpi.com/2313-7673/7/2/79 |
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