Abstract
Pancreatic ductal adenocarcinoma (PDAC) is a leading cause of cancer-related death worldwide. The accuracy of a PDAC diagnosis based on endoscopic ultrasonography-guided fine-needle aspiration cytology can be strengthened by performing a rapid on-site evaluation (ROSE). However, ROSE can only be performed in a limited number of facilities, due to a relative lack of available resources or cytologists with sufficient training. Therefore, we developed the Mathematical Technology for Cytopathology (MTC) algorithm, which does not require teaching data or large-scale computing. We applied the MTC algorithm to support the cytological diagnosis of pancreatic cancer tissues, by converting medical images into structured data, which rendered them suitable for artificial intelligence (AI) analysis. Using this approach, we successfully clarified ambiguous cell boundaries by solving a reaction–diffusion system and quantitating the cell nucleus status. A diffusion coefficient (D) of 150 showed the highest accuracy (i.e., 74%), based on a univariate analysis. A multivariate analysis was performed using 120 combinations of evaluation indices, and the highest accuracies for each D value studied (50, 100, and 150) were all ≥70%. Thus, our findings indicate that MTC can help distinguish between adenocarcinoma and benign pancreatic tissues, and imply its potential for facilitating rapid progress in clinical diagnostic applications.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
