Analysing spatial point patterns in digital pathology: immune cells in high-grade serous ovarian carcinomas
Clicks: 86
ID: 281598
2023
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Steady Performance
25.5
/100
86 views
36 readers
AI Quality Assessment
Not analyzed
Readership in this journal
SteadyRanked #199 of 803 articles by views in arXiv
Most read
Least read
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 803 in total.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
Multiplex immunofluorescence (mIF) imaging technology facilitates the study
of the tumour microenvironment in cancer patients. Due to the capabilities of
this emerging bioimaging technique, it is possible to statistically analyse,
for example, the co-varying location and functions of multiple different types
of immune cells. Complex spatial relationships between different immune cells
have been shown to correlate with patient outcomes and may reveal new pathways
for targeted immunotherapy treatments.
This tutorial reviews methods and procedures relating to spatial point
patterns for complex data analysis. We consider tissue cells as a realisation
of a spatial point process for each patient. We focus on proper functional
descriptors for each observation and techniques that allow us to obtain
information about inter-patient variation.
Ovarian cancer is the deadliest gynaecological malignancy and can resist
chemotherapy treatment effective in cancers. We use a dataset of high-grade
serous ovarian cancer samples from 51 patients. We examine the immune cell
composition (T cells, B cells, macrophages) within tumours and additional
information such as cell classification (tumour or stroma) and other patient
clinical characteristics. Our analyses, supported by reproducible software,
apply to other digital pathology datasets.
| Reference Key |
moraga2023analysing
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Jonatan A. González; Julia Wrobel; Simon Vandekar; Paula Moraga |
| Journal | arXiv |
| Year | 2023 |
| DOI |
DOI not found
|
| URL | |
| Keywords |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.