1. Deep learning predicts the 1-year prognosis of pancreatic cancer patients using positive peritoneal washing cytology
- Author
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Aya Noguchi, Yasushi Numata, Takanori Sugawara, Hiroshu Miura, Kaori Konno, Yuzu Adachi, Ruri Yamaguchi, Masaharu Ishida, Takashi Kokumai, Daisuke Douchi, Takayuki Miura, Kyohei Ariake, Shun Nakayama, Shimpei Maeda, Hideo Ohtsuka, Masamichi Mizuma, Kei Nakagawa, Hiromu Morikawa, Jun Akatsuka, Ichiro Maeda, Michiaki Unno, Yoichiro Yamamoto, and Toru Furukawa
- Subjects
Medicine ,Science - Abstract
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.
- Published
- 2024
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