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  • MDPI AG  (3)
  • Michiba, Ayano  (3)
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  • MDPI AG  (3)
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  • 1
    Online Resource
    Online Resource
    Automated Classification of Urinary Cells: Using Convolutional Neural Network Pre-trained on Lung Cells
    MDPI AG ; 2023
    In:  Applied Sciences Vol. 13, No. 3 ( 2023-01-30), p. 1763-
    Details
    In: Applied Sciences, MDPI AG, Vol. 13, No. 3 ( 2023-01-30), p. 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. 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.
    Type of Medium: Online Resource
    ISSN: 2076-3417
    URL: Article
    DOI: 10.3390/app13031763
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2704225-X
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    Online Resource
  • 2
    Online Resource
    Online Resource
    γH2AX, a DNA Double-Strand Break Marker, Correlates with PD-L1 Expression in Smoking-Related Lung Adenocarcinoma
    MDPI AG ; 2022
    In:  International Journal of Molecular Sciences Vol. 23, No. 12 ( 2022-06-15), p. 6679-
    Details
    In: International Journal of Molecular Sciences, MDPI AG, Vol. 23, No. 12 ( 2022-06-15), p. 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 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.
    Type of Medium: Online Resource
    ISSN: 1422-0067
    URL: Article
    DOI: 10.3390/ijms23126679
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2019364-6
    SSG: 12
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  • 3
    Online Resource
    Online Resource
    Automated Classification of Idiopathic Pulmonary Fibrosis in Pathological Images Using Convolutional Neural Network and Generative Adversarial Networks
    MDPI AG ; 2022
    In:  Diagnostics Vol. 12, No. 12 ( 2022-12-16), p. 3195-
    Details
    In: Diagnostics, MDPI AG, Vol. 12, No. 12 ( 2022-12-16), p. 3195-
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 2075-4418
    URL: Article
    DOI: 10.3390/diagnostics12123195
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2662336-5
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