Search

Cytology, a type of pathological examination, involves sampling cells from the human body and observing the morphology of the nucleus, cytoplasm, and cell arrangement. In developing classification AI technologies to support cytology, it is essential to collect and utilize a diverse range of images without bias. However, this is often challenging in practice because of the epidemiologic bias of cancer types and cellular characteristics. The main aim of this study was to develop a method to generate cytological diagnostic images from image findings using text-to-image technology in order to generate diverse images. In the proposed method, we collected Papanicolaou-stained specimens derived from the lung cells of 135 lung cancer patients, from which we extracted 472 patch images. Descriptions of the corresponding findings for these patch images were compiled to create a data set. This dataset was then utilized to finetune the Stable Diffusion (SD) v1 and v2 models. The cell images generated by this method closely resemble real images, and both cytotechnologists and cytopathologists provided positive subjective evaluations. Furthermore, SDv2 demonstrated shapes and contours of nuclei and cytoplasm that were more similar to real images compared to SDv1, showing superior performance in quantitative evaluation metrics. When the generated images were utilized in the classification tasks for cytological images, there was an improvement in classification performance. These results indicate that the proposed method may be effective for generating high-quality cytological images, which enables the image classification model to learn diverse features, thereby improving classification performance.

Urine cytology, which is based on the examination of cellular images obtained from urine, is widely used for the diagnosis of bladder cancer. However, the diagnosis is sometimes difficult in highly heterogeneous carcinomas exhibiting weak cellular atypia. In this study, we propose a new deep learning method that utilizes image information from another organ for the automated classification of urinary cells. We first extracted 3137 images from 291 lung cytology specimens obtained from lung biopsies and trained a classification process for benign and malignant cells using VGG-16, a convolutional neural network (CNN). Subsequently, 1380 images were extracted from 123 urine cytology specimens and used to fine-tune the CNN that was pre-trained with lung cells. To confirm the effectiveness of the proposed method, we introduced three different CNN training methods and compared their classification performances. The evaluation results showed that the classification accuracy of the fine-tuned CNN based on the proposed method was 98.8% regarding sensitivity and 98.2% for specificity of malignant cells, which were higher than those of the CNN trained with only lung cells or only urinary cells. The evaluation results showed that urinary cells could be automatically classified with a high accuracy rate. These results suggest the possibility of building a versatile deep-learning model using cells from different organs.

Extramedullary plasmacytoma (EMP) is a rare disease consisting of the presence of monoclonal plasma cells in tissues other than the bone. Most EMPs are located in the head and neck region. We present an extremely rare case of an EMP originating from the biliary tract in a 76-year-old male. This is the fifth report of a primary EMP arising from the biliary tract. He was diagnosed with jaundice, and he was referred for an additional examination. Abdominal ultrasonography revealed a tumor in the gallbladder and bile ducts, and a bile duct biopsy was performed via endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA). The pathological and immunohistochemical examination revealed that the tumor was a plasmacytoma originating in the biliary tract. Although endoscopic biliary drainage was performed, the bile duct infection was not well controlled due to obstructive jaundice caused by the tumor. Furthermore, the bleeding from the tumor during chemotherapy was uncontrolled. Pancreaticoduodenectomy and cholecystectomy were performed to control the infection and bleeding. Although chemotherapy was continued after surgery, the tumor of the intrahepatic bile duct enlarged. He died seven months after the diagnosis because of the treatment-resistant tumor.

Interstitial pneumonia of uncertain cause is referred to as idiopathic interstitial pneumonia (IIP). Among the various types of IIPs, the prognosis of cases of idiopathic pulmonary fibrosis (IPF) is extremely poor, and accurate differentiation between IPF and non-IPF pneumonia is critical. In this study, we consider deep learning (DL) methods owing to their excellent image classification capabilities. Although DL models require large quantities of training data, collecting a large number of pathological specimens is difficult for rare diseases. In this study, we propose an end-to-end scheme to automatically classify IIPs using a convolutional neural network (CNN) model. To compensate for the lack of data on rare diseases, we introduce a two-step training method to generate pathological images of IIPs using a generative adversarial network (GAN). Tissue specimens from 24 patients with IIPs were scanned using a whole slide scanner, and the resulting images were divided into patch images with a size of 224 × 224 pixels. A progressive growth GAN (PGGAN) model was trained using 23,142 IPF images and 7817 non-IPF images to generate 10,000 images for each of the two categories. The images generated by the PGGAN were used along with real images to train the CNN model. An evaluation of the images generated by the PGGAN showed that cells and their locations were well-expressed. We also obtained the best classification performance with a detection sensitivity of 97.2% and a specificity of 69.4% for IPF using DenseNet. The classification performance was also improved by using PGGAN-generated images. These results indicate that the proposed method may be considered effective for the diagnosis of IPF.

Since the discovery of Helicobacter pylori (H. pylori), many efforts have been made to establish animal models for the investigation of the pathological features and molecular mechanisms of gastric carcinogenesis. Among the animal models, Mongolian gerbils and mice are particularly useful for the analysis of H. pylori-associated inflammatory reactions and gastric cancer development. Inhibitors of oxidative stress, cyclooxygenase-2 (COX-2) and nuclear factor-κB, exert preventive effects on chronic gastritis and the development of adenocarcinomas in H. pylori-infected gerbils. Genetically-modified mouse models, including transgenic and knockout mice, have also revealed the importance of p53, COX-2/prostaglandin, Wnt/β-catenin, proinflammatory cytokines, gastrin and type III mucin in the molecular mechanisms of gastric carcinogenesis. Microarray technology is available for comprehensive gene analysis in the gastric mucosa of mouse models, and epigenetics, such as DNA methylation, could be an alternative approach to correlate the observations in animal models with the etiology in humans.