We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Deep learning predicts the 1-year prognosis of pancreatic cancer patients using positive peritoneal washing cytology.
- Authors
Noguchi, Aya; Numata, Yasushi; Sugawara, Takanori; Miura, Hiroshu; Konno, Kaori; Adachi, Yuzu; Yamaguchi, Ruri; Ishida, Masaharu; Kokumai, Takashi; Douchi, Daisuke; Miura, Takayuki; Ariake, Kyohei; Nakayama, Shun; Maeda, Shimpei; Ohtsuka, Hideo; Mizuma, Masamichi; Nakagawa, Kei; Morikawa, Hiromu; Akatsuka, Jun; Maeda, Ichiro
- 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.
- Subjects
CONVOLUTIONAL neural networks; RECEIVER operating characteristic curves; TRANSFORMER models; CANCER prognosis; CELL nuclei
- Publication
Scientific Reports, 2024, Vol 14, Issue 1, p1
- ISSN
2045-2322
- Publication type
Article
- DOI
10.1038/s41598-024-67757-5