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Article ; Online: Automated Classification of Urinary Cells

Atsushi Teramoto / Ayano Michiba / Yuka Kiriyama / Eiko Sakurai / Ryoichi Shiroki / Tetsuya Tsukamoto

Applied Sciences, Vol 13, Iss 1763, p

Using Convolutional Neural Network Pre-trained on Lung Cells

2023 Volume 1763

Abstract: 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. ...

Abstract	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.
Keywords	urinary cell ; classification ; deep learning ; convolutional neural network ; Technology ; T ; Engineering (General). Civil engineering (General) ; TA1-2040 ; Biology (General) ; QH301-705.5 ; Physics ; QC1-999 ; Chemistry ; QD1-999
Subject code	006
Language	English
Publishing date	2023-01-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: γH2AX, a DNA Double-Strand Break Marker, Correlates with PD-L1 Expression in Smoking-Related Lung Adenocarcinoma

Eiko Sakurai / Hisato Ishizawa / Yuka Kiriyama / Ayano Michiba / Yasushi Hoshikawa / Tetsuya Tsukamoto

International Journal of Molecular Sciences, Vol 23, Iss 6679, p

2022 Volume 6679

Abstract: 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 ... ...

Abstract	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.
Keywords	lung cancer ; adenocarcinoma ; squamous cell carcinoma ; immune checkpoint inhibitors ; programmed death-ligand 1 ; DNA damage response ; Biology (General) ; QH301-705.5 ; Chemistry ; QD1-999
Subject code	610
Language	English
Publishing date	2022-06-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A case of chronic gastric anisakiasis coexisting with early gastric cancer

Eiko Sakurai / Masaaki Okubo / Yutaka Tsutsumi / Tomoyuki Shibata / Tomomitsu Tahara / Yuka Kiriyama / Ayano Michiba / Naoki Ohmiya / Tetsuya Tsukamoto

Fujita Medical Journal, Vol 9, Iss 2, Pp 163-

2023 Volume 169

Abstract: 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 ... ...

Abstract	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.
Keywords	nematode ; anisakiasis ; stomach ; gastric cancer ; Medicine (General) ; R5-920
Subject code	610
Language	English
Publishing date	2023-05-01T00:00:00Z
Publisher	Fujita Medical Society
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Deep learning approach to classification of lung cytological images

Atsushi Teramoto / Tetsuya Tsukamoto / Ayumi Yamada / Yuka Kiriyama / Kazuyoshi Imaizumi / Kuniaki Saito / Hiroshi Fujita

PLoS ONE, Vol 15, Iss 3, p e

Two-step training using actual and synthesized images by progressive growing of generative adversarial networks.

2020 Volume 0229951

Abstract: 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 ... ...

Abstract	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.
Keywords	Medicine ; R ; Science ; Q
Subject code	006
Language	English
Publishing date	2020-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Automated Classification of Lung Cancer Types from Cytological Images Using Deep Convolutional Neural Networks

Atsushi Teramoto / Tetsuya Tsukamoto / Yuka Kiriyama / Hiroshi Fujita

BioMed Research International, Vol

2017 Volume 2017

Abstract: 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 ... ...

Abstract	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.
Keywords	Medicine ; R
Subject code	571
Language	English
Publishing date	2017-01-01T00:00:00Z
Publisher	Hindawi Limited
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Mutual stain conversion between Giemsa and Papanicolaou in cytological images using cycle generative adversarial network

Atsushi Teramoto / Ayumi Yamada / Tetsuya Tsukamoto / Yuka Kiriyama / Eiko Sakurai / Kazuya Shiogama / Ayano Michiba / Kazuyoshi Imaizumi / Kuniaki Saito / Hiroshi Fujita

Heliyon, Vol 7, Iss 2, Pp e06331- (2021)

2021

Abstract: 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 ...

Abstract	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.
Keywords	Giemsa stain ; Papanicolaou stain ; Translation ; Cycle-consistent generative adversarial network ; Deep learning ; Science (General) ; Q1-390 ; Social sciences (General) ; H1-99
Subject code	571
Language	English
Publishing date	2021-02-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Weakly supervised learning for classification of lung cytological images using attention-based multiple instance learning

Atsushi Teramoto / Yuka Kiriyama / Tetsuya Tsukamoto / Eiko Sakurai / Ayano Michiba / Kazuyoshi Imaizumi / Kuniaki Saito / Hiroshi Fujita

Scientific Reports, Vol 11, Iss 1, Pp 1-

2021 Volume 9

Abstract: Abstract 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 ... ...

Abstract	Abstract 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.
Keywords	Medicine ; R ; Science ; Q
Subject code	006
Language	English
Publishing date	2021-10-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Prevention of Gastric Cancer

Tetsuya Tsukamoto / Mitsuru Nakagawa / Yuka Kiriyama / Takeshi Toyoda / Xueyuan Cao

International Journal of Molecular Sciences, Vol 18, Iss 8, p

Eradication of Helicobacter Pylori and Beyond

2017 Volume 1699

Abstract: 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. ... ...

Abstract	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.
Keywords	Helicobacter pylori ; chronic atrophic gastritis ; intestinal metaplasia ; eradication ; chemoprevention ; Biology (General) ; QH301-705.5 ; Chemistry ; QD1-999
Subject code	610
Language	English
Publishing date	2017-08-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Automated classification of benign and malignant cells from lung cytological images using deep convolutional neural network

Atsushi Teramoto / Ayumi Yamada / Yuka Kiriyama / Tetsuya Tsukamoto / Ke Yan / Ling Zhang / Kazuyoshi Imaizumi / Kuniaki Saito / Hiroshi Fujita

Informatics in Medicine Unlocked, Vol 16, Iss , Pp - (2019)

2019

Abstract: Background: Lung cancer is a leading cause of death worldwide, and its early detection is usually performed with low-dose computed tomography. For lesions suspected of abnormality by CT examination, a cytological diagnosis of lung cells collected by ... ...

Abstract	Background: Lung cancer is a leading cause of death worldwide, and its early detection is usually performed with low-dose computed tomography. For lesions suspected of abnormality by CT examination, a cytological diagnosis of lung cells collected by biopsy is first performed. However, atypical cells are the major challenge in malignant lung cell classification for cytotechnologists and cytopathologists. In this study, we aimed to automatize the classification of malignant lung cells from microscopic images using a deep convolutional neural network (DCNN). Method: Cytological specimens were prepared with a liquid-based cytology system and stained using the Papanicolaou technique. Images were acquired with a digital still camera attached to a microscope with a 40× objective lens. The original microscopic images were first cropped to obtain image patches with resolution of 224 × 224 pixels. We obtained 306 benign and 315 malignant image patches. To avoid overfitting, 60,000 patch images were generated using data augmentation by applying rotation, flipping, filtering, and color adjustment. DCNN classification was conducted based on a fine-tuned VGG-16 model. We performed patch-based segmentation of malignant regions in the images and evaluated classification performance using threefold cross-validation. Results: The classification sensitivity and specificity were 89.3 and 83.3%, respectively, reaching a performance comparable to that of a cytopathologist. Using the gradient-weighted class activation mapping, we visualized the DCNN identification performance while the network searched for typical benign and malignant cells in images for classification. Conclusions: The proposed method can be useful for accurate and automatic classification of lung cells from pulmonary cytological images. Keywords: Pathology, Cytology, Deep learning, Deep convolutional neural network, Lung cancer, Malignancy analysis
Keywords	Computer applications to medicine. Medical informatics ; R858-859.7
Subject code	006
Language	English
Publishing date	2019-01-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A case of an inflammatory myofibroblastic tumor of the bladder in a young woman

Hokuto Akamatsu / Shimpei Akiyama / Ryoichi Kato / Hitoshi Ishise / Ryoichi Shiroki / Yuka Kiriyama / Makoto Kuroda / Hiroshi Toyama

Fujita Medical Journal, Vol 3, Iss 1, Pp 20-

2017 Volume 23

Abstract: We herein report a relatively rare case of an inflammatory myofibroblastic tumor (IMT) of the bladder in a young woman. In contrast to IMTs of other organs, IMTs of the lower urinary tract involve the mixture of a non-neoplastic lesion with a neoplastic ... ...

Abstract	We herein report a relatively rare case of an inflammatory myofibroblastic tumor (IMT) of the bladder in a young woman. In contrast to IMTs of other organs, IMTs of the lower urinary tract involve the mixture of a non-neoplastic lesion with a neoplastic lesion; therefore, the determination of whether other neoplastic lesions are present is important. In this case, magnetic resonance imaging confirmed a pedunculated lobulated tumor that protruded from the posterior left bladder wall into the lumen. The lobulated area exhibited a low-intensity signal on T1-weighted imaging (T1WI) and a high-intensity signal similar to that of urine on T2-weighted imaging (T2WI). Morphologically, the peduncle resembled a submucosal mass with faint low-intensity signaling on T1WI and faint high-intensity signaling on T2WI. These findings differ from those of a neoplastic lesion of the bladder mucous membrane. The presence of lesions from the muscularis propria to the submucosa and in areas with markedly high-intensity signals exhibiting a nodular fasciitis pattern on T2WI is considered useful for the differential diagnosis of IMT on imaging examination.
Keywords	inflammatory myofibroblastic tumor ; urinary bladder ; inflammatory pseudotumor ; magnetic resonance imaging ; submucosal tumor ; Medicine (General) ; R5-920
Language	English
Publishing date	2017-02-01T00:00:00Z
Publisher	Fujita Medical Society
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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