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Diagnostics
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Lung Cancer and Lung Injury: Diagnosis, Differential Diagnosis, and Management
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Lung Cancer and Lung Injury: Diagnosis, Differential Diagnosis, and Management

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Pathology and Molecular Diagnostics".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 8761

Special Issue Editor

Prof. Dr. Kimihiro Shimizu

E-Mail Website
Guest Editor
School of Medicine, Shinshu Universitydisabled, Matsumoto, Japan
Interests: lung cancer; thoracic surgery; segmentectomy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The diagnosis of lung cancer has been divided into subsections. Correctly diagnosing a cancer subtype is critical to selecting appropriate treatment, because aggressiveness, responsiveness to therapy, and prognosis vary significantly among subtypes. High-grade lung neuroendocrine tumor (HGNET), which is classified into small-cell lung cancer (SCLC) and large-cell neuroendocrine carcinoma (LCNEC), and non-small cell lung cancer (NSCLC) may differ in indications for surgery and drug regimens. However, sometimes it is difficult to distinguish between poorly differentiated NSCLC and HGNET. Herein, we summarize the published evidence and discuss key issues related to differential diagnosis between HGNET and NSCLC by immunostaining, and we also demonstrate the usefulness of Stasthmin-1, which is a cytosolic phosphoprotein that mediates cellular division and proliferation by regulating microtubule dynamics.

Prof. Dr. Kimihiro Shimizu
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Stathmin-1
  • high-grade neuroendocrine tumor

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Published Papers (3 papers)

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Research

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13 pages, 3783 KiB  
Article
Automated Classification of Idiopathic Pulmonary Fibrosis in Pathological Images Using Convolutional Neural Network and Generative Adversarial Networks
by Atsushi Teramoto, Tetsuya Tsukamoto, Ayano Michiba, Yuka Kiriyama, Eiko Sakurai, Kazuyoshi Imaizumi, Kuniaki Saito and Hiroshi Fujita
Diagnostics 2022, 12(12), 3195; https://doi.org/10.3390/diagnostics12123195 - 16 Dec 2022
Cited by 3 | Viewed by 1773
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Lung Cancer and Lung Injury: Diagnosis, Differential Diagnosis, and Management)
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13 pages, 1449 KiB  
Article
Survival Rates of Patients with Non-Small Cell Lung Cancer Depending on Lymph Node Metastasis: A Focus on Saliva
by Lyudmila V. Bel’skaya, Elena A. Sarf and Victor K. Kosenok
Diagnostics 2021, 11(5), 912; https://doi.org/10.3390/diagnostics11050912 - 20 May 2021
Cited by 6 | Viewed by 2260
Abstract
The aim of this study was to compare overall survival (OS) rates at different pN stages of NSCLC depending on tumor characteristics and to assess the applicability of saliva biochemical markers as prognostic signs. The study included 239 patients with NSCLC (pN0 [...] Read more.
The aim of this study was to compare overall survival (OS) rates at different pN stages of NSCLC depending on tumor characteristics and to assess the applicability of saliva biochemical markers as prognostic signs. The study included 239 patients with NSCLC (pN0-120, pN1-51, pN2-68). Saliva was analyzed for 34 biochemical indicators before the start of treatment. For pN0, the tumor size does not have a prognostic effect, but the histological type should be taken into account. For pN1 and pN2, long-term results are significantly worse in squamous cell cancer with a large tumor size. A larger volume of surgical treatment reduces the differences between OS. The statistically significant factors of an unfavorable prognosis at pN0 are the lactate dehydrogenase activity <1294 U/L and the level of diene conjugates >3.97 c.u. (HR = 3.48, 95% CI 1.21–9.85, p = 0.01541); at pN1, the content of imidazole compounds >0.296 mmol/L (HR = 6.75, 95% CI 1.28–34.57, p = 0.00822); at pN2 levels of protein <0.583 g/L and Schiff bases >0.602 c.u., as well as protein >0.583 g/L and Schiff bases <0.602 c.u. (HR = 2.07, 95% CI 1.47–8.93, p = 0.04351). Using salivary biochemical indicators, it is possible to carry out stratification into prognostic groups depending on the lymph node metastasis. Full article
(This article belongs to the Special Issue Lung Cancer and Lung Injury: Diagnosis, Differential Diagnosis, and Management)
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Review

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13 pages, 1436 KiB  
Review
A Novel Strategy for the Diagnosis of Pulmonary High-Grade Neuroendocrine Tumor
by Kentaro Miura, Kimihiro Shimizu, Shogo Ide, Shuji Mishima, Shunichiro Matsuoka, Tetsu Takeda, Takashi Eguchi, Kazutoshi Hamanaka and Takeshi Uehara
Diagnostics 2021, 11(11), 1945; https://doi.org/10.3390/diagnostics11111945 - 20 Oct 2021
Cited by 6 | Viewed by 3606
Abstract
Correctly diagnosing a histologic type of lung cancer is important for selecting the appropriate treatment because the aggressiveness, chemotherapy regimen, surgical approach, and prognosis vary significantly among histologic types. Pulmonary NETs, which are characterized by neuroendocrine morphologies, represent approximately 20% of all lung [...] Read more.
Correctly diagnosing a histologic type of lung cancer is important for selecting the appropriate treatment because the aggressiveness, chemotherapy regimen, surgical approach, and prognosis vary significantly among histologic types. Pulmonary NETs, which are characterized by neuroendocrine morphologies, represent approximately 20% of all lung cancers. In particular, high-grade neuroendocrine tumors (small cell lung cancer and large cell neuroendocrine tumor) are highly proliferative cancers that have a poorer prognosis than other non-small cell lung cancers. The combination of hematoxylin and eosin staining, Ki-67, and immunostaining of classic neuroendocrine markers, such as chromogranin A, CD56, and synaptophysin, are normally used to diagnose high-grade neuroendocrine tumors; however, they are frequently heterogeneous. This article reviews the diagnostic methods of lung cancer diagnosis focused on immunostaining. In particular, we describe the usefulness of immunostaining by Stathmin-1, which is a cytosolic phosphoprotein and a key regulator of cell division due to its microtubule depolymerization in a phosphorylation-dependent manner, for the diagnosis of high-grade neuroendocrine tumors. Full article
(This article belongs to the Special Issue Lung Cancer and Lung Injury: Diagnosis, Differential Diagnosis, and Management)
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