櫻井 映子

Direct DNA double-strand breaks result in phosphorylation of H2AX, a variant of the histone H2 protein. Phosphorylated H2AX (γH2AX) may be a potential indicator in the evaluation of genotoxicity and hepatocarcinogenicity. In this study, γH2AX and Ki-67 were detected in the short-term responses (24 h after chemical administration) to classify genotoxic hepatocarcinogens (GHs) from non-GH chemicals. One hundred and thirty-five 6-week-old Crl: CD(SD) (SPF) male rats were treated with 22 chemicals including 11 GH and 11 non-GH, sacrificed 24 h later, and immunostained with γH2AX and Ki-67. Positivity rates of these markers were measured in the 3 liver ZONEs 1-3; portal, lobular, and central venous regions. These values were input into 3 machine learning models-Naïve Bayes, Random Forest, and k-Nearest Neighbor to classify GH and non-GH using a 10-fold cross-validation method. All 11 and 10 out of 11 GH caused significant increase in γH2AX and Ki-67 levels, respectively (P < .05). Of the 3 machine learning models, Random Forest performed the best. GH were identified with 95.0% sensitivity (76/80 GH-treated rats), 90.9% specificity (50/55 non-GH-treated rats), and 90.0% overall correct response rate using γH2AX staining, and 96.2% sensitivity (77/80), 81.8% specificity (45/55), and 90.4% overall correct response rate using Ki-67 labeling. Random Forest model using γH2AX and Ki-67 could independently predict GH in the early stage with high accuracy.

BACKGROUND: Anisakiasis is a parasitic disease caused by the consumption of raw or undercooked fish that is infected with Anisakis third-stage larvae. In countries, such as Japan, Italy, and Spain, where people have a custom of eating raw or marinated fish, anisakiasis is a common infection. Although anisakiasis has been reported in the gastrointestinal tract in several countries, reports of anisakiasis accompanied by cancer are rare. CASE PRESENTATION: We present the rare case of a 40-year-old male patient with anisakiasis coexisting with mucosal gastric cancer. Submucosal gastric cancer was suspected on gastric endoscopy and endoscopic ultrasonography. After laparoscopic distal gastrectomy, granulomatous inflammation with Anisakis larvae in the submucosa was pathologically revealed beneath mucosal tubular adenocarcinoma. Histological and immunohistochemical investigation showed cancer cells as intestinal absorptive-type cells that did not produce mucin. CONCLUSION: Anisakis larvae could have invaded the cancer cells selectively because of the lack of mucin in the cancerous epithelium. Anisakiasis coexisting with cancer is considered reasonable rather than coincidental. In cancer with anisakiasis, preoperative diagnosis may be difficult because anisakiasis leads to morphological changes in the cancer.

BACKGROUND: Lung cancer frequently occurs in lungs with background idiopathic interstitial pneumonias (IIPs). Limited resection is often selected to treat lung cancer in patients with IIPs in whom respiratory function is already compromised. However, accurate surgical margins are essential for curative resection; underestimating these margins is a risk for residual lung cancer after surgery. We aimed to investigate the findings of lung fields adjacent to cancer segments affect the estimation of tumor size on computed tomography compared with the pathological specimen. METHODS: This analytical observational study retrospectively investigated 896 patients with lung cancer operated on at Fujita Health University from January 2015 to June 2020. The definition of underestimation was a ≥10 mm difference between the radiological and pathological maximum sizes of the tumor. RESULTS: The lung tumors were in 15 honeycomb, 30 reticulated, 207 emphysematous, and 628 normal lungs. The ratio of underestimation in honeycomb lungs was 33.3% compared to 7.4% without honeycombing (P=0.004). Multivariate analysis showed that honeycombing was a significant risk factor for tumor size underestimation. A Bland-Altman plot represented wide 95% limits of agreement, -40.8 to 70.2 mm, between the pathological and radiological maximum tumor sizes in honeycomb lungs.

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.

In recent years, the choice of immune checkpoint inhibitors (ICIs) as a treatment based on high expression of programmed death-ligand 1 (PD-L1) in lung cancers has been increasing in prevalence. The high expression of PD-L1 could be a predictor of ICI efficacy as well as high tumor mutation burden (TMB), which is determined using next-generation sequencing (NGS). However, a great deal of effort is required to perform NGS to determine TMB. The present study focused on γH2AX, a double-strand DNA break marker, and the suspected positive relation between TMB and γH2AX was investigated. We assessed the possibility of γH2AX being an alternative marker of TMB or PD-L1. One hundred formalin-fixed, paraffin-embedded specimens of lung cancer were examined. All of the patients in the study received thoracic surgery, having been diagnosed with lung adenocarcinoma or squamous cell carcinoma. The expressions of γH2AX and PD-L1 (clone: SP142) were evaluated immunohistochemically. Other immunohistochemical indicators, p53 and Ki-67, were also used to estimate the relationships of γH2AX. Positive relationships between γH2AX and PD-L1 were proven, especially in lung adenocarcinoma. Tobacco consumption was associated with higher expression of γH2AX, PD-L1, Ki-67, and p53. In conclusion, the immunoexpression of γH2AX could be a predictor for the adaptation of ICIs as well of as PD-L1 and TMB.

OBJECTIVE: It is essential to accurately diagnose and classify histological subtypes into adenocarcinoma (ADC), squamous cell carcinoma (SCC), and small cell lung carcinoma (SCLC) for the appropriate treatment of lung cancer patients. However, improving the accuracy and stability of diagnosis is challenging, especially for non-small cell carcinomas. The purpose of this study was to compare multiple deep convolutional neural network (DCNN) technique with subsequent additional classifiers in terms of accuracy and characteristics in each histology. METHODS: Lung cancer cytological images were classified into ADC, SCC, and SCLC with four fine-tuned DCNN models consisting of AlexNet, GoogLeNet (Inception V3), VGG16 and ResNet50 pretrained by natural images in ImageNet database. For more precise classification, the figures of 3 histological probabilities were further applied to subsequent machine learning classifiers using Naïve Bayes (NB), Support vector machine (SVM), Random forest (RF), and Neural network (NN). RESULTS: The classification accuracies of the AlexNet, GoogLeNet, VGG16 and ResNet50 were 74.0%, 66.8%, 76.8% and 74.0%, respectively. Well differentiated typical morphologies were tended to be correctly judged by all four architectures. However, poorly differentiated non-small cell carcinomas lacking typical structures were inclined to be misrecognized in some DCNNs. Regarding the histological types, ADC were best judged by AlexNet and SCC by VGG16. Subsequent machine learning classifiers of NB, SVV, RF, and NN improved overall accuracies to 75.1%, 77.5%, 78.2%, and 78.9%, respectively. CONCLUSION: Fine-tuning DCNNs in combination with additional classifiers improved classification of cytological diagnosis of lung cancer, although classification bias could be indicated among DCNN architectures.

Secondary fungal infections are a critical problem that accompany immunosuppressive therapy for severe coronavirus disease 2019 (COVID-19). We report a fatal case of COVID-19 with disseminated mucormycosis diagnosed during autopsy. A 58-year-old man with diabetes was hospitalized for severe COVID-19 and treated with remdesivir, systemic steroids and tocilizumab. Following treatment, he was provided extracorporeal membrane oxygenation support. However, he died of multiple organ failure accompanied by pulmonary and kidney infarction, as revealed by computed tomography. Autopsy revealed that the infarction was caused by thromboangiitis due to mucormycosis in the brain, lungs, heart, liver and kidneys. Therefore, the diagnosis of disseminated mucormycosis was established. Disseminated mucormycosis is a rare complication of COVID-19. Although its early diagnosis is difficult, the disease progresses rapidly. Hence, we propose that immunosuppressive treatment for COVID-19 should be administered with caution considering the risk of developing severe opportunistic infections, such as mucormycosis.

<title>Abstract</title>In cytological examination, suspicious cells are evaluated regarding malignancy and cancer type. To assist this, we previously proposed an automated method based on supervised learning that classifies cells in lung cytological images as benign or malignant. However, it is often difficult to label all cells. In this study, we developed a weakly supervised method for the classification of benign and malignant lung cells in cytological images using attention-based deep multiple instance learning (AD MIL). Images of lung cytological specimens were divided into small patch images and stored in bags. Each bag was then labeled as benign or malignant, and classification was conducted using AD MIL. The distribution of attention weights was also calculated as a color map to confirm the presence of malignant cells in the image. AD MIL using the AlexNet-like convolutional neural network model showed the best classification performance, with an accuracy of 0.916, which was better than that of supervised learning. In addition, an attention map of the entire image based on the attention weight allowed AD MIL to focus on most malignant cells. Our weakly supervised method automatically classifies cytological images with acceptable accuracy based on supervised learning without complex annotations.

Objective: Papanicolaou and Giemsa stains used in cytology have different characteristics and complementary roles. In this study, we focused on cycle-consistent generative adversarial network (CycleGAN), which is an image translation technique using deep learning, and we conducted mutual stain conversion between Giemsa and Papanicolaou in cytological images using CycleGAN. Methods: A total of 191 Giemsa-stained images and 209 Papanicolaou-stained images were collected from 63 patients with lung cancer. From those images, 67 images from nine cases were used for testing and the remaining images were used for training. For data augmentation, the number of training images was increased by rotation and inversion, and the images were pipelined to CycleGAN to train the mutual conversion process involving Giemsa- and Papanicolaou-stained images. Three pathologists and three cytotechnologists performed visual evaluations of the authenticity of cell nuclei, cytoplasm, and cell layouts of the test images translated using CycleGAN. Results: As a result of converting Giemsa-stained images into Papanicolaou-stained images, the background red blood cell patterns present in Giemsa-stained images disappeared, and cell patterns that reproduced the shape and staining of the cell nuclei and cytoplasm peculiar to Papanicolaou staining were obtained. Regarding the reverse-translated results, nuclei became larger, and red blood cells that were not evident in Papanicolaou-stained images appeared. After visual evaluation, although actual images exhibited better results than converted images, the results were promising for various applications. Discussion: The stain translation technique investigated in this paper can complement specimens under conditions where only single stained specimens are available; it also has potential applications in the massive training of artificial intelligence systems for cell classification, and can also be used for training cytotechnologist and pathologists.