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An explainable longitudinal multi-modal fusion model for predicting neoadjuvant therapy response in breast cancer treatment

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# An explainable longitudinal multi-modal fusion model for predicting neoadjuvant therapy response in breast cancer treatment

We developed a Multi-modal Response Prediction (MRP) system for instant prediction in breast cancer treatment with Neoadjuvant Therapy (NAT). Using longitudinal multidisciplinary data from 3,352 breast cancer patients, MRP mimics physician assessments, aided by a cross-modal learning module for facilitating the MRP practicality in multi-center hospitals. Validated across centers and reader studies, MRP exhibited comparable robustness and generalizability to breast radiologists, significantly outperforming humans in pCR prediction in the Pre-NAT phase. Clinical utility assessment shows MRP's potential: reducing treatment toxicity by 35.8% in Pre-NAT and preventing surgeries by 16.7% in Post-NAT, without mispredictions. Our work enhances AI applications in personalized treatment decisions.

## Neoadjuvant Therapy Pathway
<img src="https://github.com/yawwG/MRP/blob/main/figures/clinical_pathway.png"/>

## Model system
<img src="https://github.com/yawwG/MRP/blob/main/figures/structure.png"/>

## Environment Setup
Start by [installing PyTorch 1.8.1](https://pytorch.org/get-started/locally/) with the right CUDA version, then clone this repository and install the dependencies.

```bash
$ conda install pytorch==1.8.1 torchvision==0.9.1 torchaudio==0.8.1 cudatoolkit=11.1 -c pytorch
$ pip install git@github.com:yawwG/MRP.git
$ conda env create -f environment.yml
```

## Code Description
This codebase has been developed with python version 3.7, PyTorch version 1.8.1, CUDA 11.1 and pytorch-lightning 1.5.9. 
Example configurations for MG-based and MRI-based classification can be found in the `./configs`. 
All training and testing are done using the `run.py` script. For more documentation, please run:

```bash 
python run.py --help
```

The preprocessing steps for each dataset (MRI and Mammogram) can be found in `datasets/image_dataset.py`
The dataset using is specified in config.yaml by key("dataset").

### Train and Test
Training the MRI and Mammogram based model for pCR prediction with the following command: 
```bash 
python run.py -c imrrhpc.yaml --train --test
python run.py -c imgrhpc.yaml --train --test
```

### Multimodal contribution analysis
To ensure that the model's interpretability and predictable performance, we explicitly demonstrate the contribution of multi-modalities in the model's training. 
```bash 
python ./utils/contribution.py
```

### Decision curve analysis analysis
We explored in two specific clinical scenarios: personalizing Pre-/Mid-NAT management of non-pCR patients to avoid toxic therapy and optimizing Post-NAT management of pCR patients to reduce unnecessary surgeries. 
```bash 
python ./utils/dca_analysis.py
```

## Contact details
If you have any questions please contact us.

Email: ritse.mann@radboudumc.nl (Ritse Mann); taotanjs@gmail.com (Tao Tan); y.gao@nki.nl (Yuan Gao)

Links: [Netherlands Cancer Institute](https://www.nki.nl/), [Radboud University Medical Center](https://www.radboudumc.nl/en/patient-care), [Maastricht University](https://www.maastrichtuniversity.nl/nl), [St Joseph’s Healthcare Hamilton](https://www.stjoes.ca/) and [The University of Hong Kong](https://www.hku.hk/) 
<div align="center">
<img src="https://github.com/yawwG/Visualize-what-you-learn/blob/main/figures/NKI.png" width="166.98" height="87.12"/><img src="https://github.com/yawwG/Visualize-what-you-learn/blob/main/figures/RadboudUMC.png" width="231" height="87.12"/><img src="https://github.com/yawwG/Visualize-what-you-learn/blob/main/figures/Maastricht.png" width="237.6" height="87.12"/>  <img src="https://github.com/yawwG/Visualize-what-you-learn/blob/main/figures/hku.png" width="104" height="104"/> 
 </div>
 <div align="center">
<img src="https://github.com/yawwG/MRP/blob/main/figures/st_joseph's.png" width="140" height="87.12"/>
 </div>

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