Extract and evaluate radiomics for liver cancer tumors from DICOM segmentation masks. Using SimpleITK, PyRadiomics and PyDicom.

This is a complete guide on how to do Pyradiomics based feature extraction and then, build a model to calculate the grade of glioma.

Open source of Pyradiomics extension

Predict survival time from PET scans

Ontology-guided Radiomics Analysis Workflow (O-RAW)

This repository contains a deep learning-based cancer type prediction system using a trained convolutional neural network (CNN). The model is deployed using Streamlit, allowing users to upload medical images and receive predictions with a probability distribution displayed in a pie chart.

Lung Cancer Classification with CT Scans [Labs of AI and DS Course Project]

Lung Cancer Detection using CT Scans.

This script reads DICOM files in a source directory or in a list of source directories and searches for the patients in the given patients' list creates a DICOM DataBase in the destination directory, copies the files, and creates a DicomDataBase.csv file and a summary.txt file.

Workflow for Optimal Radiomics Classification, WORC toolbox.

Quantitative Analysis of Chromatin Organization in Neuroendocrine Lung Cancer using PyRadiomics and StarDist. This project leverages advanced image processing techniques to segment cell nuclei and extract detailed radiomic features from small-cell and large-cell neuroendocrine lung cancers.

I extracted the tumor features according to the data annotation of all four stages of 3D MRI data. And I deployed Graph Neural Networks based on important features such as First Order Statistics, Shape-based (3D), Shape-based (2D), GLCM, GLRLM, GLSZN, NGTDM, and GLDM.

Repository for CoRa, a CLI which can extract radiomics from COVID-19 CT scans.

IBSI-compliant radiomics feature extraction pipeline for head and neck cancer CT imaging. Automated quantitative biomarker analysis from DICOM RT structure sets. Extracts 100+ shape, first-order, and texture features for tumor characterization and outcome prediction. Built with PyRadiomics, SimpleITK, and rt-utils.