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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.
摘要：
胰腺癌患者的腹膜冲洗细胞学(CY)主要用于分期;它也可用于评估腹膜内状态以预测更准确的预后.这里,我们研究了CY标本图像的深度学习对预测CY阳性患者胰腺癌1年预后的潜力.回顾性分析88例具有预后信息的CY标本。对整个载玻片成像设备扫描的CY标本进行分割，并使用视觉变换器（ViT）和卷积神经网络（CNN）进行深度学习。结果表明，ViT和CNN从扫描图像中预测1年预后，在受试者工作特征曲线的曲线下区域的精度分别为0.8056和0.8009。分别。通过Kaplan-Meier分析，通过ViT预测存活1年或更长时间的患者显示出明显更长的存活时间。被ViT发现具有负面预后影响的细胞核似乎是嗜中性粒细胞。我们的结果表明，AI介导的CY标本分析可以成功预测CY阳性的胰腺癌患者的1年预后。腹膜内中性粒细胞可能是CY阳性胰腺癌患者的一种新的预后标志物和治疗靶点。
