iXL: Inferring eXpLainable algorithmic approaches to identifying cancer stages, cancer grades and regions of interest automatically from medical images

Suryadipto Sarkar, A S Aravinthakshan, Teresa Wu, Alvin C. Silva

About

This is a repository that contains information on how to reproduce results corresponding to the cutaneous T cell lymphoma (CTCL) case study reported in Spatial cell graph analysis reveals skin tissue organization characteristic for cutaneous T cell lymphoma.

Summary of methods

Part-1: Gradient-driven stochastic random walk identifies urologic cancer stages from CT and MRI images

Python packages:

• Network-GradR-Walk (access here)
• Spatial-GradR-Walk (access here)

Background:

• Access here

Part-2: Heterogeneity-based approach

Python packages:

• Network-Heterogeneity

• Spatial-Heterogeneity

Background:

• SHouT(entropy, egophily, homophily) (access here)

• Leibovici entropy, Altieri entropy

• Spatial entropy (Moran's I, Geary's C)

Part-3: Classifying cancer ROIs by combining imaging-based heterogeneity in tumor microenvironments, in combination with molecular data (for example, gene expression data)

• Can this help explain underlying molecular mechanisms (eg. gene and/ or protein expressions), and how they manifest in scans?

• Very loosely related to this work (ECCB2024 poster presentation):

Data

Overview

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Description

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Availability

Data will be made available under reasonable request to the corresponding author, Suryadipto Sarkar (more contact details below).

Installation

Install conda environment as follows (there also exists a requirements.txt)

conda create --name imaging_heterogeneity_study
conda activate imaging_heterogeneity_study
pip install scipy==1.10.1 numpy==1.23.5 squidpy==1.3.0 pandas==1.5.3 scikit-learn==1.2.2

Note: Additionally, modules math and statistics were used, however no installation is required as they are provided with Python by default.

Robustness testing

Pending

Scalability testing

Pending

Reproducing figures

Pending

Citing the work

MLA

Will be made available upon publication.

APA

Will be made available upon publication.

BibTex

Will be made available upon publication.

Contact

✉  suryadipto.sarkar@fau.de
✉  ssarka34@asu.edu
✉  ssarkarmanipal@gmail.com

Impressum

Suryadipto Sarkar ("Surya"), MS

PhD Candidate
Biomedical Network Science Lab
Department of Artificial Intelligence in Biomedical Engineering (AIBE)
Friedrich-Alexander University Erlangen-Nürnberg (FAU)
Werner von Siemens Strasse
91052 Erlangen

MS in CEN from Arizona State University, AZ, USA.
B.Tech in ECE from MIT Manipal, KA, India.