%0 Journal Article
%T Deep learning predicts the 1-year prognosis of pancreatic cancer patients using positive peritoneal washing cytology.
%A Noguchi A
%A Numata Y
%A Sugawara T
%A Miura H
%A Konno K
%A Adachi Y
%A Yamaguchi R
%A Ishida M
%A Kokumai T
%A Douchi D
%A Miura T
%A Ariake K
%A Nakayama S
%A Maeda S
%A Ohtsuka H
%A Mizuma M
%A Nakagawa K
%A Morikawa H
%A Akatsuka J
%A Maeda I
%A Unno M
%A Yamamoto Y
%A Furukawa T
%J Sci Rep
%V 14
%N 1
%D 2024 08 2
%M 39095474
%F 4.996
%R 10.1038/s41598-024-67757-5
%X Peritoneal washing cytology (CY) in patients with pancreatic cancer is mainly used for staging; however, it may also be used to evaluate the intraperitoneal status to predict a more accurate prognosis. Here, we investigated the potential of deep learning of CY specimen images for predicting the 1-year prognosis of pancreatic cancer in CY-positive patients. CY specimens from 88 patients with prognostic information were retrospectively analyzed. CY specimens scanned by the whole slide imaging device were segmented and subjected to deep learning with a Vision Transformer (ViT) and a Convolutional Neural Network (CNN). The results indicated that ViT and CNN predicted the 1-year prognosis from scanned images with accuracies of 0.8056 and 0.8009 in the area under the curve of the receiver operating characteristic curves, respectively. Patients predicted to survive 1 year or more by ViT showed significantly longer survivals by Kaplan-Meier analyses. The cell nuclei found to have a negative prognostic impact by ViT appeared to be neutrophils. Our results indicate that AI-mediated analysis of CY specimens can successfully predict the 1-year prognosis of patients with pancreatic cancer positive for CY. Intraperitoneal neutrophils may be a novel prognostic marker and therapeutic target for CY-positive patients with pancreatic cancer.