{Reference Type}: Journal Article
{Title}: Deep learning predicts the 1-year prognosis of pancreatic cancer patients using positive peritoneal washing cytology.
{Author}: Noguchi A;Numata Y;Sugawara T;Miura H;Konno K;Adachi Y;Yamaguchi R;Ishida M;Kokumai T;Douchi D;Miura T;Ariake K;Nakayama S;Maeda S;Ohtsuka H;Mizuma M;Nakagawa K;Morikawa H;Akatsuka J;Maeda I;Unno M;Yamamoto Y;Furukawa T;
{Journal}: Sci Rep
{Volume}: 14
{Issue}: 1
{Year}: 2024 08 2
{Factor}: 4.996
{DOI}: 10.1038/s41598-024-67757-5
{Abstract}: 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.