Metadata-Version: 2.1
Name: pd-dwi
Version: 1.1.4
Summary: Physiologically-Decomposed Diffusion-Weighted MRI machine-learning model for predicting response to neoadjuvant chemotherapy in invasive breast cancer
Home-page: https://tcml-bme.github.io/
Author: Maya Gilad
Author-email: ms.maya.gilad@gmail.com
Requires-Python: >=3.8.1,<3.9
Classifier: Development Status :: 3 - Alpha
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Provides-Extra: preprocessing
Requires-Dist: Cython (>=0.29.30,<0.30.0)
Requires-Dist: PyYAML (>=6.0,<7.0)
Requires-Dist: StrEnum (>=0.4.15,<0.5.0); python_version < "3.11"
Requires-Dist: click (>=8.1.7,<9.0.0)
Requires-Dist: jsonschema (>=4.6.0,<5.0.0)
Requires-Dist: numpy (>=1.22.0,<2.0.0)
Requires-Dist: pandas (>=1.4.3,<2.0.0)
Requires-Dist: pydantic (>=2.6.4,<3.0.0)
Requires-Dist: pydantic-yaml (>=1.2.1,<2.0.0)
Requires-Dist: pydicom (>=2.4.4,<3.0.0); extra == "preprocessing"
Requires-Dist: pyradiomics (>=3.0.1,<4.0.0)
Requires-Dist: scikit-learn (>=1.0.2,<2.0.0)
Requires-Dist: xgboost (>=1.6.1,<2.0.0)
Project-URL: Documentation, https://pd-dwi.readthedocs.io/en/stable/
Project-URL: Repository, https://github.com/TechnionComputationalMRILab/PD-DWI
Description-Content-Type: text/markdown

# PD-DWI

PD-DWI is a physiologically-decomposed Diffusion-Weighted MRI machine-learning model for predicting response to neoadjuvant chemotherapy in invasive breast cancer.

PD-DWI was developed by [TCML](https://tcml-bme.github.io/) group as part of [BMMR2 challenge](https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=89096426) using [ACRIN-6698](https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=50135447) dataset.

This work was accepted to MICCAI 2022 (to be held during Sept 18-22 in Singapore). 

**If you publish any work which uses this package, please cite the following publication:** 
```
M. Gilad, M. Freiman. PD-DWI: Predicting response to neoadjuvant chemotherapy in invasive breast cancer with Physiologically-Decomposed Diffusion-Weighted MRI machine-learning model. Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 to be held during Sept 18-22 in Singapore. 
```
A pre-print version of our paper is available at https://arxiv.org/abs/2206.05695

## Installation

PD-DWI model can be installed directly from Github:

```
pip install git+https://github.com/TechnionComputationalMRILab/PD-DWI.git
```

## Usage

PD-DWI can be used in a Python script or via command line.

To explore all CLI options and syntax requirements run `pd-dwi --help` in your terminal.

## Contact

Please contact us on ms.maya.gilad@gmail.com
