Bibliographic Details
| Title: |
A Novel Strategy for the Diagnosis of Pulmonary High-Grade Neuroendocrine Tumor |
| Authors: |
Kentaro Miura, Kimihiro Shimizu, Shogo Ide, Shuji Mishima, Shunichiro Matsuoka, Tetsu Takeda, Takashi Eguchi, Kazutoshi Hamanaka, Takeshi Uehara |
| Source: |
Diagnostics, Vol 11, Iss 11, p 1945 (2021) |
| Publisher Information: |
MDPI AG, 2021. |
| Publication Year: |
2021 |
| Collection: |
LCC:Medicine (General) |
| Subject Terms: |
high-grade neuroendocrine tumor, small cell lung cancer, large cell neuroendocrine tumor, immunostaining, stathmin-1, Medicine (General), R5-920 |
| More Details: |
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. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2075-4418 |
| Relation: |
https://www.mdpi.com/2075-4418/11/11/1945; https://doaj.org/toc/2075-4418 |
| DOI: |
10.3390/diagnostics11111945 |
| Access URL: |
https://doaj.org/article/515011ba71ab49848fab7a364e63932e |
| Accession Number: |
edsdoj.515011ba71ab49848fab7a364e63932e |
| Database: |
Directory of Open Access Journals |
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