Touseef Ahmad Qureshi1, Sehrish Javed1, Tabasom Sarmadi2, Stephen Jacob Pandol3, Debiao Li1. 1. Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA. 2. Regis University, Colorado, USA. 3. Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Division of Gastroenterology, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Abstract
OBJECTIVE: To emphasize the importance of pancreatic imaging and the application of artificial intelligence (AI) for enhanced risk prediction of pancreatic ductal adenocarcinoma (PDAC). BACKGROUND: Detecting PDAC at the early stage is challenging as the disease either remains asymptomatic or presents nonspecific symptoms. Risk prediction of PDAC is an efficient strategy as subsequent targeted screening can assist in diagnosing cancer at the early stage even before the symptoms appear. However, the lack of specific clinical and epidemiological predictors of PDAC makes prediction a highly challenging task. Detecting precursor changes in the pancreas can potentially assist in the risk prediction of PDAC as the precancerous pancreas evolves through biological adaptations-presented as morphological and textural changes on abdominal imaging. However, such microlevel "clues" usually remain unnoticed or unappreciated, partly due to the unavailability of tools to detect and interpret such complex measurements, making the risk prediction of PDAC an unresolved problem. METHODS: This review study highlights the limitations of the current risk prediction models of PDAC and the importance of abdominal imaging for predicting PDAC. A suggestive narrative is made as to how recent AI tools can assist in extracting precise measurements of biomarkers, detecting early signs and precancerous abnormalities, quantifying tissue characteristics, and revealing complex features potentially indicative of future incidence of pancreatic cancer (PC) using abdominal imaging. With the help of peer examples of other cancers, a case is built about the application of AI in utilizing image features of the pancreas to enhance risk prediction of PDAC. Furthermore, the challenges of AI applications including insufficient data for model training, risk of data privacy violation, inconsistent data labeling, and limited computational resources, and their potential solutions are also discussed. CONCLUSIONS: The recent advancement in the domain of AI is a potential opportunity to utilize automated tools for the identification of imaging-based indicators of PDAC and perform enhanced risk prediction of cancer. With this awareness and motivation, better management of PDAC has expected.
OBJECTIVE: To emphasize the importance of pancreatic imaging and the application of artificial intelligence (AI) for enhanced risk prediction of pancreatic ductal adenocarcinoma (PDAC). BACKGROUND: Detecting PDAC at the early stage is challenging as the disease either remains asymptomatic or presents nonspecific symptoms. Risk prediction of PDAC is an efficient strategy as subsequent targeted screening can assist in diagnosing cancer at the early stage even before the symptoms appear. However, the lack of specific clinical and epidemiological predictors of PDAC makes prediction a highly challenging task. Detecting precursor changes in the pancreas can potentially assist in the risk prediction of PDAC as the precancerous pancreas evolves through biological adaptations-presented as morphological and textural changes on abdominal imaging. However, such microlevel "clues" usually remain unnoticed or unappreciated, partly due to the unavailability of tools to detect and interpret such complex measurements, making the risk prediction of PDAC an unresolved problem. METHODS: This review study highlights the limitations of the current risk prediction models of PDAC and the importance of abdominal imaging for predicting PDAC. A suggestive narrative is made as to how recent AI tools can assist in extracting precise measurements of biomarkers, detecting early signs and precancerous abnormalities, quantifying tissue characteristics, and revealing complex features potentially indicative of future incidence of pancreatic cancer (PC) using abdominal imaging. With the help of peer examples of other cancers, a case is built about the application of AI in utilizing image features of the pancreas to enhance risk prediction of PDAC. Furthermore, the challenges of AI applications including insufficient data for model training, risk of data privacy violation, inconsistent data labeling, and limited computational resources, and their potential solutions are also discussed. CONCLUSIONS: The recent advancement in the domain of AI is a potential opportunity to utilize automated tools for the identification of imaging-based indicators of PDAC and perform enhanced risk prediction of cancer. With this awareness and motivation, better management of PDAC has expected.
Entities:
Keywords:
Risk prediction; abdominal imaging; pancreatic cancer (PC)
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