This is the first study to examine whether prognosis can be predicted using AI-mediated analysis of CY specimens from patients with pancreatic cancer. In this study, deep learning using ViT and CNN was able to predict the 1-year prognosis from CY+ specimens of patients with pancreatic cancer, with an accuracy of more than AUC 0.8. In addition, the prediction accuracy was higher for the ViT than for the CNN. Segmented nuclei were found among the cell nuclei with high predicted probabilities of ViT, suggesting a poor prognosis. These cells were most likely neutrophils. The round cell nuclei had low predicted probabilities, suggesting a good prognosis, which were macrophages based on negative immunocytochemical staining for calretinin.AI techniques are being used in a variety of fields, and in pathology they are being applied to diagnose benign and malignant conditions and tumor subtypes, evaluate pathological features and biomarkers, and predict prognosis13. In the cytological field, a deep learning system was able to classify malignant cells and predict tumor origins from cytology images of pleural and ascitic fluids, achieving an AUC of 0.95 and better14. Previous studies on prognosis prediction by deep learning using histopathology specimens have reported that survival prediction was successful for gastric cancer, liver cancer, and glioma with C-indexes of 0.657, 0.78, and 0.754, respectively, and for soft-tissue sarcoma with AUC accuracy of 0.9115,16,17,18. For pancreatic cancer, which was the subject of this study, CT images were used to predict prognosis with an accuracy of AUC 0.72319. All of these previous studies used CNN-based machine learning. The accuracy of survival prediction in this study was demonstrated in 0.8 of AUC, which seems to be sufficiently high. Furthermore, our team has also shown that combining clinical information with machine learning of histopathology specimen images improves prediction accuracy of patients with pancreatic cancer undergoing surgery and adjuvant therapy20. Training AI by integrating clinical and molecular data, such as genomic and RNA sequencing information, with imaging information, such as that obtained in this study, could provide a more accurate assessment of therapeutic options. Recently, attention mechanisms have gained prominence in deep learning. A model consisting solely of an attention mechanism was first developed in the field of machine translation21. Subsequently, ViT classified images with higher accuracy than CNN which has been widely used for prognosis prediction based on image data22. In this study, the AUC for predicting 1-year prognosis was only slightly higher for ViT than for CNN. A Kaplan–Meier survival analysis of patients using ViT predictions in this study showed that the good prognosis group had significantly longer survival than the poor prognosis group. However, there was no significant difference in survival curves between the groups classified by CNN. We used data for cell nuclei, which allowed us to over 50,000 image data for training. The large amount of data may enable ViT to efficiently learn the cell nuclei that are more likely to influence on prognosis, and thereby improving classification accuracy23. In addition, although segmentation was performed for each cell, morphological annotation of each cell was not performed during the training phase. We previously reported a deep learning system without morphological annotation for histopathological specimens24. These approaches not only save the physician time regarding the need for annotation but also have the potential to uncover new findings.The cell nuclei in the poor prognosis group were most likely those of neutrophils. An elevated neutrophil-to-lymphocyte ratio in the peripheral blood and an increased number of intratumoral neutrophils have been reported as poor prognostic factors in patients with pancreatic cancer25,26, and meta-analyses of multiple cancer types have shown similar findings27,28. The present results suggest that intraperitoneal neutrophils are a poor prognostic factor in patients with pancreatic cancer. To the best of our knowledge, there have been no reports examining the relationship between intraperitoneal neutrophils and prognosis in patients with cancer. Findings of intraperitoneal neutrophils have been reported in mouse models. Intraperitoneal neutrophils increased in a mouse model of peritoneal dissemination of ovarian cancer29 and intraperitoneal neutrophil extracellular traps (NETs) promoted intraperitoneal dissemination in mice transplanted with human gastric cancer cells30. In recent years, the relationship between tumor cells and immune cells has attracted much attention, and various findings have been reported for neutrophils. Neutrophils have been shown to promote tumor cell proliferation. Elastase produced by neutrophils degrades insulin receptor substrate-1 when taken up by tumor cells, resulting in enhanced interaction between phosphatidylinositol 3-kinase (PI3K) and platelet-derived growth factor receptor. The subsequent activation of the PI3K pathway promotes tumor growth31. In addition, neutrophils also promote tumor metastasis. Previous studies have shown that NETs formed by neutrophils capture circulating tumor cells and provide a foothold for metastasis32, and that the interaction of β2 integrin on neutrophils with intercellular adhesion molecule 1 on tumor cells promotes tumor cell anchorage to the vascular endothelium and invasion into the tissue33. Moreover, neutrophils are an important source of matrix metallopeptidase 9, which releases vascular endothelial growth factor from the extracellular matrix and is thought to promote angiogenesis by malignant tumors34,35. Further to the above, neutrophils have been shown to induce apoptosis of CD8 T cells in the presence of tumor necrosis factor α and nitric oxide, which may favor tumor cells by suppressing cellular immunity36. In contrast, neutrophils exert antitumor effects by causing tumor cell death37, inhibiting neovascularization38, and promoting T cell proliferation39. Thus, neutrophils have a dual function with respect to tumor cells, and factors affecting their functional polarization have been investigated40. In our study results, intraperitoneal neutrophils were associated with poor prognosis. Intraperitoneal neutrophils in pancreatic cancer patients with CY+ may exert a variety of functions that promote the tumorigenic effects described above, thereby worsening their prognosis. Regarding patient characteristics, the only significant difference between the good and poor prognostic groups was CRP level, indicating that a high CRP level was associated with poor prognosis. CRP is a systemic inflammatory marker; therefore, this result may indicate an association between systemic inflammation and patient prognosis. However, the difference in mean CRP levels between the 2 groups was small, and a prospective study is needed to clarify whether this difference is clinically significant. Furthermore, the relationship between intraperitoneal neutrophils and systemic inflammation was obscure in this study, although some cases revealed the presence of intraperitoneal neutrophils even in the absence of significant inflammation proved by hematological examination.These intraperitoneal neutrophils could be therapeutic targets and are currently under investigation. The removal of intraperitoneal neutrophils in a mouse model of peritoneal dissemination of ovarian cancer caused an increase in regulatory T cells and a decrease in CD8+ T cells, suggesting that they may promote tumor growth41. In contrast, it has been reported that type 1 interferon induces the antitumor function of neutrophils and that neutrophil elastase selectively kills cancer cells42,43. These reports suggest that enhancing the antitumor effects of neutrophils by modulating their functions with inflammatory cytokines, rather than eliminating them, could be a therapeutic strategy.Cells with low predicted probabilities of ViT were determined to be macrophages, which are broadly classified as M1 macrophages with inflammatory effects or M2 macrophages with anti-inflammatory effects; however, the polarization state of macrophages is plastic, and their activation state is composed of a spectrum44. The macrophages identified in this study may have M1-like polarity, which may contribute to an improved prognosis. The interaction between tumor and immune cells has been studied mainly in tumor tissues, but much remains unknown regarding the immune environment in the peritoneal cavity. Further clarification of these issues may lead to a better understanding of the mechanisms underlying the intraperitoneal invasion and peritoneal dissemination of cancer cells, which would help control these processes.The limitations of this study include its retrospective design, small number of cases, heterogeneous patient background, single-center design and chronological differences in analyzed cohorts. The results should be validated in future studies involving a large number of patients. Chronological differences in obtaining the CY specimens in training dataset cohort (2011–2018), test dataset cohort (2019–2021), and validation dataset cohort (2022) may cause unexpected biases in qualities of materials and trivial differences in clinical treatment and medications. Revalidation of the results of our study using contemporaneous CY specimens may be desirable. Furthermore, future stratification of patients according to treatment methods and other factors may enable the use of CY specimens to predict treatment efficacy.Using deep learning, we were able to predict the 1-year prognosis of patients with pancreatic cancer from CY+ specimens. Our results may help to optimize the treatment of pancreatic cancer patients with CY+ by stratifying their prognosis. Neutrophil exudation into the peritoneal cavity may promote tumor progression and influence prognosis. Thus, intraperitoneal neutrophils may be a new therapeutic target in patients with CY+ pancreatic cancer. Peritoneal washing fluid is a clinical specimen of interest not only for determining the stage of cancer but also for obtaining new clinical information, which may lead to the development of new treatment methods.
