Abnormal cytological findings - Female, 32 - Cell image [5170730007432161] - Open Research Data - Bridge of Knowledge

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Abnormal cytological findings - Female, 32 - Cell image [5170730007432161]

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: 32

Clinical description: Prophylaxis. Vaginal discharge

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: NILM (Negative for Intraepithelial Lesion or Malignancy). Papsmear contains multiple superficial epithelial cells (partly overlapping), multiple groups of glandular cervical and transformation zone cells, multiple granulocytes. Single Candida alb. colonies.

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

7d0d9aa5-56b4-4ccf-a609-8bdc20fdb0e6
21.1 GB, S3 ETag , downloads: 3
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:
2020-08-18
Creation date:
2020
Dataset language:
English
Fields of science:
  • medical sciences (Medical and Health Sciences )
DOI:
DOI ID 10.34808/r493-k771 open in new tab
Ethical papers:
NKBBN/421-306/2020
Verified by:
Medical University of Gdańsk

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