Abnormal cytological findings - Female, 37 - Cell image [4130730006612781] - Open Research Data - Bridge of Knowledge

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Abnormal cytological findings - Female, 37 - Cell image [4130730006612781]

Description

This is the histopathological image of CERVIX UTERI cell 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: 37

Clinical description: Ectropion

Gender: Female

Diagnosis:

Classification: ICD-10_10-20

Classification code: Chapter XVIII - Symptoms, signs and abnormal clinical and laboratory findings, not elsewhere classified

Diagnosis: Abnormal cytological findings

Result of the histopathological examination: HGSIL (High grade squamous intraepithelial lesion). Papsmear contains multiple superficial epithelial cells, groups of cells with distorted N/C ratio, nuclei are polymorphic and hyperchromatic. Glandular cervical cells in groups or single, multiple granulocytes. Surepath.

Sample:

Material: SurePath

Collecting method: Exfoliative cytology

Topography: FEMALE GENITAL ORGANS

Organ: CERVIX UTERI

Tissue: Exocervix

Type of staining: positive/HC

Staining: Papanicolaou

Antibody: Not applicable

Technology:

Equipment: Pannoramic 250 3DHistech

Lens: 20x

Organization:

Source: Medical University Gdańsk

Dataset file

154c358d-532c-4e95-af32-ae6652e40280
22.1 GB, S3 ETag , downloads: 25
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:
2019-12-02
Creation date:
2019
Dataset language:
English
Fields of science:
  • medical sciences (Medical and Health Sciences )
DOI:
DOI ID 10.34808/aj5z-mc13 open in new tab
Ethical papers:
NKBBN/421-306/2020
Verified by:
Medical University of Gdańsk

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