1. Deep learning predicts the 1-year prognosis of pancreatic cancer patients using positive peritoneal washing cytology.
- Author
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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, and Maeda, Ichiro
- Subjects
CONVOLUTIONAL neural networks ,RECEIVER operating characteristic curves ,TRANSFORMER models ,CANCER prognosis ,CELL nuclei - 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. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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