Granular cell tumor, NOS - Female, 47 - Tissue image [5060730006982381] - Open Research Data - Bridge of Knowledge

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Granular cell tumor, NOS - Female, 47 - Tissue image [5060730006982381]

Description

This is the histopathological image of CONNECTIVE, SUBCUTANEOUS AND OTHER SOFT TISSUES tissue sample obtained in Medical University Gdańsk and deposited in ZMDL-GUMED. The sample image was taken using: Pannoramic 250 3DHistech slide scanner (20x magnification) and saved to DICOM format.

The detailed information about the patient, sample, and diagnosis are as follows:

Patient:

Age: 47

Clinical description: 1cm subcutaneus nodule of back.

Gender: Female

Diagnosis:

Classification: ICD-O_3.2

Classification code: GRANULAR CELL TUMORS AND ALVEOLAR SOFT PART SARCOMAS

Diagnosis: Granular cell tumor, NOS

Result of the histopathological examination: Granular cell tumor: subepidermal tumor with ill-defined borders composed of cells with abundant granular and eosinophilic granular cytoplasm. Incomplete excision. Ki-67 2% S100+, CD68+, CKAE1/AE3-, VIM+.

Sample:

Material: FFPE

Collecting method: Excision of lesions

Topography: CONNECTIVE, SUBCUTANEOUS AND OTHER SOFT TISSUES

Organ: CONNECTIVE, SUBCUTANEOUS AND OTHER SOFT TISSUES

Tissue: Connective, subcutaneous and other soft tissues of back

Type of staining: positive/IHC

Staining: Not applicable

Antibody: CK PAN (AE1/AE3)

Technology:

Equipment: Pannoramic 250 3DHistech

Lens: 20x

Organization:

Source: Medical University Gdańsk

Dataset file

07a0ae5b-a274-402f-890e-76c269859621
7.6 GB, S3 ETag , downloads: 20
The file hash is calculated from the formula
hexmd5(md5(part1)+md5(part2)+...)-{parts_count} where a single part of the file is 512 MB in size.

Example script for calculation:
https://github.com/antespi/s3md5

File details

License:
Creative Commons: by-nc-sa 4.0 open in new tab
CC BY-NC-SA
Non-commercial - Share-alike
Raw data:
Data contained in dataset was not processed.
Software:
CaseViewer 2.3

Details

Year of publication:
2021
Verification date:
2021-03-20
Creation date:
2021
Dataset language:
English
Fields of science:
  • medical sciences (Medical and Health Sciences )
DOI:
DOI ID 10.34808/kcwk-7j32 open in new tab
Ethical papers:
NKBBN/421-306/2020
Verified by:
Medical University of Gdańsk

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