Early detection of genotoxic hepatocarcinogens in rats using γH2AX and Ki-67: prediction by machine learning.

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.

A case of chronic gastric anisakiasis coexisting with early gastric cancer.

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.

Adenosquamous carcinoma coexisting with intraductal papillary mucinous neoplasm of the pancreas: a case report.

BACKGROUND: Adenosquamous carcinoma of the pancreas is a rare variant, with a worse prognosis than pancreatic ductal adenocarcinoma; moreover, it has characteristic clinical and histopathological features. Studies have mentioned the differentiation of intraductal papillary mucinous neoplasms into mucinous/tubular adenocarcinomas; however, their transdifferentiation into adenosquamous carcinoma remains unclear. CASE PRESENTATION: An 80-year-old Japanese woman was referred to our hospital for further examination of multiple pancreatic cysts. Enhanced computed tomography after close follow-up for 6 years revealed a new nodule with poor enhancement on the pancreatic body. Distal pancreatectomy and splenectomy were performed. Histopathological examination revealed an adenosquamous carcinoma with coexisting intraductal papillary mucinous neoplasms; moreover, the intraductal papillary mucinous neoplasms lacked continuity with the adenosquamous carcinoma. Immunohistochemical analysis revealed squamous cell carcinoma and differentiation from adenocarcinoma to squamous cell carcinoma. Gene mutation analysis revealed KRASG12D and KRASG12R mutations in adenosquamous carcinoma components and intraductal papillary mucinous neoplasm lesions, respectively, with none showing the mutation of GNAS codon 201. The final histopathological diagnosis was adenosquamous carcinoma with coexisting intraductal papillary mucinous neoplasms of the pancreas. CONCLUSIONS: This is the rare case of adenosquamous carcinoma with coexisting intraductal papillary mucinous neoplasms of the pancreas. To investigate the underlying transdifferentiation pathway of intraductal papillary mucinous neoplasms into this rare subtype of pancreatic cancer, we explored gene mutation differences as a clinicopathological parameter.

Acute genitourinary toxicity of pencil beam scanning proton therapy for localized prostate cancer: utility of the transition zone index and average urinary flow rate in predicting acute urinary retention.

BACKGROUND: The purpose of this study was to evaluate the incidence of acute genitourinary toxicities in patients undergoing pencil beam scanning proton therapy for prostate cancer and investigate predictive factors associated with acute urinary retention. METHODS: A total of 227 patients treated between 2018 and 2021 were divided into the normo-fractionated proton therapy group (n = 107) and the moderately hypo-fractionated proton therapy group (n = 120), with prescribed doses of 76-78 Gy relative biological effectiveness in 38-39 fractions and 60-63 Gy relative biological effectiveness in 20-21 fractions, respectively. Uroflowmetry parameters and the transition zone index were prospectively evaluated. RESULTS: Forty-five patients (42%) in the normo-fractionated proton therapy and 33 (28%) in the moderately hypo-fractionated proton therapy developed acute grade 2 genitourinary toxicities (P = 0.02). The most common acute genitourinary toxicity was acute urinary retention. Thirty-nine patients (36%) treated with normo-fractionated proton therapy and 27 (23%) treated with moderately hypo-fractionated proton therapy developed grade 2 acute urinary retention (P = 0.02). No patients developed grade ≥ 3 toxicity. Univariate analysis showed the transition zone index, prostate volume, international prostate symptom score, voided volume, maximum flow rate and average flow rate were associated with grade 2 acute urinary retention. Multivariate analysis in both groups revealed the transition zone index (P = 0.025 and 0.029) and average flow rate (P = 0.039 and 0.044) were predictors of grade 2 acute urinary retention. CONCLUSIONS: The incidence of acute genitourinary toxicities was lower in the moderately hypo-fractionated proton therapy compared with the normo-fractionated proton therapy. Lower pretreatment average flow rate and a higher transition zone index were useful predictors of grade 2 acute urinary retention.

Automated Classification of Idiopathic Pulmonary Fibrosis in Pathological Images Using Convolutional Neural Network and Generative Adversarial Networks.

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.

γH2AX, a DNA Double-Strand Break Marker, Correlates with PD-L1 Expression in Smoking-Related Lung Adenocarcinoma.

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.

Comparison of Fine-Tuned Deep Convolutional Neural Networks for the Automated Classification of Lung Cancer Cytology Images with Integration of Additional Classifiers.

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.

Two cases of pancreatic colloid carcinoma with different pathogenesis: case report and review of the literature.

The two patterns of pathogenesis for pancreatic colloid carcinoma are reported; (1) progression from ordinary ductal adenocarcinoma, a subtype of invasive pancreatic ductal carcinoma, and (2) progression from papillary adenocarcinoma derived from intraductal papillary mucinous neoplasm (IPMN) or mucinous cystic neoplasm (MCN). Whether these two conditions are the same disease remains controversial. Case Report 1. An 81-year-old woman was evaluated for an increased carbohydrate antigen 19-9 (CA19-9) value (130 U/mL) detected at 4-year follow-up after distal pancreatectomy for IPMN. Based on the image findings, a local recurrence of IPMN was diagnosed, and the patient underwent a remnant total pancreatectomy. Histopathologic findings showed marked mucus production from the tumor, also noteworthy because mucous nodule formation occurs in more than 80% of tumor. Fibrosis around the mucous cavity was noted, and a low papillary lesion was found in part of the cyst wall, which was contiguous to a flat, basal area; its nucleus was enlarged and heterogeneous in size, which is considered to be a component of intraductal papillary mucinous (IPMC). Therefore, the patient was diagnosed with pancreatic colloid carcinoma derived from IPMN. Case report 2 a 71-year-old man was evaluated for jaundice. Based on the image findings, a diagnosis of pancreatic head cancer was made, and a substomach preserving pancreaticoduodenectomy was performed. Histologically, marked mucus production and floating cuboidal masses of atypical cells without mucinous nodules were seen. Mucinous nodule formation is observed in more than 80% of tumor, but there was no IPMN component, which led to the diagnosis of pancreatic colloid carcinoma. In conclusion, there might be two types of colloid carcinoma of the pancreas, and further study is needed to determine whether these diseases are truly the same or not.

Weakly supervised learning for classification of lung cytological images using attention-based multiple instance learning.

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.

Development of Pathological Diagnosis Support System Using Micro-computed Tomography.

In pathological diagnosis, the cutting position of pathological materials is subjectively determined by pathologists. This leads to a low cutting accuracy, which in turn may lead to incorrect diagnoses. In this study, we developed a system that supports the determination of the cutting position by visualizing and analyzing the internal structure of pathological material using micro-computed tomography (CT) before cutting. This system consists of a dedicated micro-CT and cutting support software. The micro-CT system has a fixture for fixing the target, enabling the scanning of easily deformable pathological materials. In the cutting support software, a function that interactively selects the extraction plane while displaying the volume rendering image and outputs a pseudo-histological image was implemented. Our results confirmed that the pseudo-histological image showed the fine structure inside the organ and that the latter image was highly consistent with the pathological image.

Mutual stain conversion between Giemsa and Papanicolaou in cytological images using cycle generative adversarial network.

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.

Implantation of rectosigmoid cancer in a preexisting anal fissure.

Colorectal cancer (CRC) rarely spreads by implantation. We report a case of implantation of rectosigmoid cancer in an anal fissure. A 70-year-old woman with a 15-year medical history of anal fissure was referred to our hospital with anal pain of 3-month duration. Colonoscopy revealed a rectosigmoid tumor and a 10-mm submucosal tumor at the anal verge. Biopsy of the rectosigmoid and anal tumors revealed that both were moderately differentiated adenocarcinomas, and abdominoperineal resection (APR) was performed. The anal adenocarcinoma was surrounded by squamous cell epithelium and mainly proliferated in the submucosal and muscular layers. The patient was diagnosed as having rectosigmoid cancer with implantation of cancer in a preexisting anal fissure. The patient remains well 43 months post-surgery with no sign of recurrence. Implantation of CRC in anal fissure is a rare occurrence. Nevertheless, performing adequate anal examination of patients with CRC before surgery and during follow-up is necessary. Further, it is important to perform preoperative large bowel examination of patients with benign anal diseases to prevent implantation of CRC.

Deep learning approach to classification of lung cytological images: Two-step training using actual and synthesized images by progressive growing of generative adversarial networks.

Cytology is the first pathological examination performed in the diagnosis of lung cancer. In our previous study, we introduced a deep convolutional neural network (DCNN) to automatically classify cytological images as images with benign or malignant features and achieved an accuracy of 81.0%. To further improve the DCNN's performance, it is necessary to train the network using more images. However, it is difficult to acquire cell images which contain a various cytological features with the use of many manual operations with a microscope. Therefore, in this study, we aim to improve the classification accuracy of a DCNN with the use of actual and synthesized cytological images with a generative adversarial network (GAN). Based on the proposed method, patch images were obtained from a microscopy image. Accordingly, these generated many additional similar images using a GAN. In this study, we introduce progressive growing of GANs (PGGAN), which enables the generation of high-resolution images. The use of these images allowed us to pretrain a DCNN. The DCNN was then fine-tuned using actual patch images. To confirm the effectiveness of the proposed method, we first evaluated the quality of the images which were generated by PGGAN and by a conventional deep convolutional GAN. We then evaluated the classification performance of benign and malignant cells, and confirmed that the generated images had characteristics similar to those of the actual images. Accordingly, we determined that the overall classification accuracy of lung cells was 85.3% which was improved by approximately 4.3% compared to a previously conducted study without pretraining using GAN-generated images. Based on these results, we confirmed that our proposed method will be effective for the classification of cytological images in cases at which only limited data are acquired.

Short-term detection of gastric genotoxicity using the DNA double-strand break marker γ-H2AX.

DNA damage caused by Helicobacter pylori infection and chronic inflammation or exposure to genotoxic agents is considered an important risk factor of gastric carcinogenesis. In this study, we have evaluated a short-term technique to detect DNA damage response to various chemical carcinogens; it involves visualization of Ser 139-phosphorylated histone H2AX (γ-H2AX) foci by immunohistochemistry and expression analysis of other genes by quantitative RT-PCR. Six-week-old male rats were intragastrically administered N-methyl-N-nitrosourea (MNU), 3,2'-dimethyl-4-aminobiphenyl (DMAB), dimethylnitrosamine (DMN), and 1,2- dimethylhydrazine (DMH) for 5 days/week for 4 weeks, using corn oil as a vehicle. Animals were sacrificed at day 28, and their stomachs were excised. γ-H2AX foci formation, indicating DNA double-strand breaks, was observed in the proliferative zone of both fundic and pyloric glands. The number of positive cells per gland was significantly high in pyloric glands in the MNU group and in fundic glands in the MNU and DMAB groups. A significant increase in p21waf1 mRNA level was observed in the DMN group compared with the control, which was in contrast to the decreasing tendency of the h2afx mRNA level in the MNU and DMN groups. Apoptotic cells positive for γ-H2AX pan or peripheral nuclear staining were observed on the surface layer of the fundic mucosa in the MNU group. The fundic pepsinogen a5 (pga5) mRNA level showed a significant decrease, indicating gland damage. The pyloric pepsinogen c mRNA level showed no change. In conclusion, γ-H2AX in combination with other gene expression analyses could be a useful biomarker in a short-term experiment on gastric chemical genotoxicity.

Eradication of Helicobacter pylori Induces Immediate Regressive Changes in Early Gastric Adenocarcinomas.

OBJECTIVE: Helicobacter pylori eradication is expected to prevent gastric cancer. However, morphological alterations after eradication often hinder accurate diagnosis. Therefore, we evaluated endoscopic and histological changes in gastric tumors after eradication of H. pylori in a time-dependent manner. METHODS: We classified 144 cases of endoscopic submucosal dissection (ESD) of early gastric cancer into the following categories: (i) patients positive for H. pylori with no eradication history, (ii) patients positive for H. pylori who underwent ESD 2 months after eradication, (iii) patients negative for H. pylori with an eradication history of at least 6 months before ESD, and (iv) patients negative for H. pylori with an unknown history. We compared endoscopic and histological factors between the groups. RESULTS: The characteristics of cancers positive for H. pylori were exploding shape, superficial high-grade atypical epithelium, and a surface proliferating zone. H. pylori eradication induced a series of endoscopic and histological changes, including shape -depression, appearance of surface regenerative and lower-grade atypical epithelium, and a downward shift of the proliferative zone within a period as short as 2 months. CONCLUSION: H. pylori eradication rapidly causes cancer regression and leads to tumor shrinkage, diminished atypism, and shortened proliferative zone, resulting in drastic morphological changes.

Invasive Ductal Carcinoma Arising in Mucinous Cystic Neoplasm of Pancreas: A Case Report.

BACKGROUND Mucinous cystic neoplasm (MCN) of the pancreas is a rare mucin-producing cystic neoplasm that has a characteristic histological feature referred to as ovarian-type stroma (OS) underlying the epithelium. Pancreatic ductal carcinoma arises from MCN as a precursor lesion, but data on progression pathways are limited. CASE REPORT A 40-year-old female was referred to our hospital for further investigation of a pancreatic cyst. Further examination showed a 7.0 cm multilocular cyst in the pancreatic tail and a solid mass in the thick septum of the cystic tumor. Distal pancreatectomy and splenectomy were performed. Histological examination revealed a moderately differentiated invasive ductal carcinoma (IDC) with a diameter of 0.5 cm in the thick septum of the cystic lesion and a cyst wall composed of epithelium with low-grade to severe dysplasia. The epithelium covered an OS. Pathological diagnosis was IDC arising in MCN of the pancreas. Immunohistochemical examination showed that MUC1 expression was negative in MCN but positive in IDC. KRAS mutation was observed in both MCN and IDC regions. CONCLUSIONS We present a rare case of moderately differentiated pancreatic IDC arising in MCN. To elucidate the underlying progression pathway, we explored the correlation between KRAS mutation and MUC expression as a clinicopathological parameter.

A Hypoglycemia-inducing Giant Borderline Phyllodes Tumor Secreting High-molecular-weight Insulin-Like Growth Factor II: Immunohistochemistry and a Western Blot Analysis.

A 50-year-old woman with a large right breast mass was emergently hospitalized for generalized weakness and fatigue. A histological examination of tumor biopsy specimens revealed a phyllodes tumor (PT). She suddenly lost consciousness due to severe hypoglycemia. Non-islet cell tumor hypoglycemia (NICTH) due to the PT was suspected. The tumor was emergently resected. A histological examination revealed a borderline PT. The patient recovered from the hypoglycemic episode. High-molecular-weight insulin-like growth factor II was detected in serum that had been collected preoperatively and in the tumor tissue, but not in serum that had been collected postoperatively. We herein present a case of a borderline PT with NICTH.

Automated Classification of Lung Cancer Types from Cytological Images Using Deep Convolutional Neural Networks.

Lung cancer is a leading cause of death worldwide. Currently, in differential diagnosis of lung cancer, accurate classification of cancer types (adenocarcinoma, squamous cell carcinoma, and small cell carcinoma) is required. However, improving the accuracy and stability of diagnosis is challenging. In this study, we developed an automated classification scheme for lung cancers presented in microscopic images using a deep convolutional neural network (DCNN), which is a major deep learning technique. The DCNN used for classification consists of three convolutional layers, three pooling layers, and two fully connected layers. In evaluation experiments conducted, the DCNN was trained using our original database with a graphics processing unit. Microscopic images were first cropped and resampled to obtain images with resolution of 256 × 256 pixels and, to prevent overfitting, collected images were augmented via rotation, flipping, and filtering. The probabilities of three types of cancers were estimated using the developed scheme and its classification accuracy was evaluated using threefold cross validation. In the results obtained, approximately 71% of the images were classified correctly, which is on par with the accuracy of cytotechnologists and pathologists. Thus, the developed scheme is useful for classification of lung cancers from microscopic images.

Prevention of Gastric Cancer: Eradication of Helicobacter Pylori and Beyond.

Although its prevalence is declining, gastric cancer remains a significant public health issue. The bacterium Helicobacter pylori is known to colonize the human stomach and induce chronic atrophic gastritis, intestinal metaplasia, and gastric cancer. Results using a Mongolian gerbil model revealed that H. pylori infection increased the incidence of carcinogen-induced adenocarcinoma, whereas curative treatment of H. pylori significantly lowered cancer incidence. Furthermore, some epidemiological studies have shown that eradication of H. pylori reduces the development of metachronous cancer in humans. However, other reports have warned that human cases of atrophic metaplastic gastritis are already at risk for gastric cancer development, even after eradication of these bacteria. In this article, we discuss the effectiveness of H. pylori eradication and the morphological changes that occur in gastric dysplasia/cancer lesions. We further assess the control of gastric cancer using various chemopreventive agents.