المؤلفون: Lopes Vendrami, Camila, Hammond, Nancy A., Escobar, David J., Zilber, Zachary, Dwyer, Meaghan, Moreno, Courtney C., Mittal, Pardeep K., Miller, Frank H.^{Aff1, IDs00261024043371_cor8}

المصدر: Abdominal Radiology. :1-20

المؤلفون: Rangwani, Shiva, Ardeshna, Devarshi R, Rodgers, Brandon, Melnychuk, Jared, Turner, Ronald, Culp, Stacey, Chao, Wei-Lun, Krishna, Somashekar G

المصدر: Biomimetics (Basel, Switzerland). 7(2)

مصطلحات موضوعية: IPMN, artificial intelligence, endoscopic ultrasound, genomics, pancreatic cystic lesions, radiomics, Digestive Diseases, Rare Diseases, Pancreatic Cancer, Cancer, Patient Safety

الوصف: 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.

وصف الملف: application/pdf

URL الوصول: https://escholarship.org/uc/item/3t01n012

المؤلفون: Wesali, Sahar^Aff1, Demir, Mehmet A., Verbeke, Caroline S., Andersson, Mats^{Aff4, Aff5}, Bratlie, Svein Olav, Sadik, Riadh

المصدر: Surgical Endoscopy: And Other Interventional Techniques. 35(12):6650-6659

المؤلفون: Hasak, Stephen^Aff7, Das, Koushik K.^Aff7

المساهمون: Coleman, William B., Series Editor^Aff1, Tsongalis, Gregory J., Series Editor^Aff2, Michalski, Christoph W., editor^Aff3, Rosendahl, Jonas, editor^Aff4, Michl, Patrick, editor^Aff5, Kleeff, Jörg, editor^Aff6

المصدر: Translational Pancreatic Cancer Research : From Understanding of Mechanisms to Novel Clinical Trials. :37-70

المؤلفون: Shiva Rangwani, Devarshi R. Ardeshna, Brandon Rodgers, Jared Melnychuk, Ronald Turner, Stacey Culp, Wei-Lun Chao, Somashekar G. Krishna

المصدر: Biomimetics, Vol 7, Iss 2, p 79 (2022)

مصطلحات موضوعية: pancreatic cystic lesions, artificial intelligence, radiomics, endoscopic ultrasound, IPMN, genomics, Technology

الوصف: 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.

وصف الملف: electronic resource

Relation: https://www.mdpi.com/2313-7673/7/2/79; https://doaj.org/toc/2313-7673

URL الوصول: https://doaj.org/article/1c890d412082413d9e8b2229973033b3

المؤلفون: Valente, Roberto^Aff4, Löhr, J.-Matthias^Aff4

المساهمون: Del Chiaro, Marco, editor^Aff1, Haas, Stephan L., editor^Aff2, Schulick, Richard D., editor^Aff3

المصدر: Cystic Tumors of the Pancreas : Diagnosis and Treatment. :163-170

Degree: MD

لا يتم عرض هذه النتيجة على الضيوف.
تسجيل الدخول للوصول الكامل.

المؤلفون: Keane, Margaret

مصطلحات موضوعية: SCN, IPMN, Pancreatic cancer, MCN, SPN, Pancreatic cystic lesions

URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_________::45d968331facec0dcc4aa631818b9dde

المؤلفون: Honselmann, Kim C., Krauss, Tobias, Geserick, Sebastian, Wellner, Ulrich F., Wittel, Uwe, Hopt, Ulrich T., Keck, Tobias, Bausch, Dirk

المصدر: Langenbeck's Archives of Surgery. June 2016 401(4):449-456

المؤلفون: Mats Andersson, Riadh Sadik, Caroline S. Verbeke, Sahar Wesali, Mehmet A. Demir, Svein Olav Bratlie

المصدر: Surgical Endoscopy

مصطلحات موضوعية: Endoscopic ultrasound, medicine.medical_specialty, Article, Endosonography, 03 medical and health sciences, 0302 clinical medicine, Carcinoembryonic antigen, Internal medicine, medicine, Humans, Cyst, Prospective Studies, Prospective cohort study, neoplasms, Endoscopic Ultrasound-Guided Fine Needle Aspiration, Pancreas, Cross-sectional imaging, biology, medicine.diagnostic_test, business.industry, IPMN, Hepatology, MCN, medicine.disease, digestive system diseases, Pancreatic Neoplasms, Serous fluid, Dysplasia, 030220 oncology & carcinogenesis, EUS-FNA, biology.protein, Adenocarcinoma, 030211 gastroenterology & hepatology, Surgery, Radiology, Pancreatic Cyst, business, Pancreatic adenocarcinoma, Pancreatic cystic lesions

الوصف: Background Imaging modalities for characterizing pancreatic cystic lesions (PCLs) is a known uncertainty. The aim of this prospective study was to compare the diagnostic performance of endoscopic ultrasound morphology, cytology and cyst fluid carcinoembryonic antigen (EUS-FNA-CEA) with cross-sectional imaging in resected PCLs. Methods The cross-sectional imaging and EUS-FNA-CEA results were collected in an academic tertiary referral centre using histology of the surgical specimen as the diagnostic standard. Results Of 289 patients undergoing evaluation for PCL with cross-sectional imaging and EUS-FNA between February 2007 and March 2017, 58 underwent surgical resection providing a final diagnosis of the PCLs: 45 mucinous, 5 serous, 1 pseudocyst, 2 endocrine, 2 solid pseudopapillary neoplasms and 3 other. EUS-FNA-CEA was more accurate than cross-sectional imaging in diagnosing mucinous PCLs (95% vs. 83%, p = 0.04). Ninety-two percent of the PCLs with high-grade dysplasia or adenocarcinoma were smaller than 3 cm in diameter. The sensitivity of EUS-FNA-CEA and cross-sectional imaging for detecting PCLs with high-grade dysplasia or adenocarcinoma were 33% and 5% (p = 0.03), respectively. However, there was no difference in accuracy between the modalities (62% vs. 66%, p = 0.79). The sensitivity for detecting pancreatic adenocarcinomas only was 64% for EUS-FNA-CEA and 9% for cross-sectional imaging (p = 0.03). Overall, EUS-FNA-CEA provided a correct diagnosis in more patients with PCLs than cross-sectional imaging (72% vs. 50%, p = 0.01). Conclusions EUS-FNA-CEA is accurate and should be considered a complementary test in the diagnosis of PCLs. However, the detection of PCLs with high-grade dysplasia or adenocarcinoma needs to be improved. Cyst size does not seem to be a reliable predictor of high-grade dysplasia or adenocarcinoma.

URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3ea720bdd68226bb2009d49eaf889a15
http://europepmc.org/articles/PMC8599246