Update 2023-04-12: We added the pretrained Cancer-Net BCa-S-A model (breast cancer grade prediction) in the models folder.
Update 2022-11-26: We released the Cancer-Net BCa dataset on Kaggle.
Update 2022-11-07: We added the pretrained Cancer-Net BCa-A model (neoadjuvant pCR prediction) in the models folder.
Note: The Cancer-Net BCa models provided here are intended to be used as reference models that can be built upon and enhanced as new data becomes available. They are currently at a research stage and not yet intended as production-ready models (not meant for direct clinical diagnosis), and we are working continuously to improve them as new data becomes available. Please do not use Cancer-Net BCa for self-diagnosis and seek help from your local health authorities.
Cancer-Net BCa is part of the Cancer-Net initiatives, a parallel initiative to the COVID-Net initiative.
Example DWI images with CDIs overlaid for sample patients with breast cancer.
Breast cancer is the second most common type of cancer in women in Canada and the United States, representing over 25% of all new female cancer cases. Neoadjuvant chemotherapy treatment has recently risen in usage as it may result in a patient having a pathologic complete response (pCR), and it can shrink inoperable breast cancer tumors prior to surgery so that the tumor becomes operable, but it is difficult to predict a patient’s pathologic response to neoadjuvant chemotherapy. In this paper, we investigate the efficacy of leveraging learnt volumetric deep features from a newly introduced magnetic resonance imaging (MRI) modality called synthetic correlated diffusion imaging (CDIs) for the purpose of pCR prediction. More specifically, we leverage a volumetric convolutional neural network to learn volumetric deep radiomic features from a pre-treatment cohort and construct a predictor based on the learnt features using the post-treatment response. As the first study to explore the utility of CDIs within a deep learning perspective for clinical decision support, we evaluated the proposed approach using the ACRIN-6698 study against those learnt using gold-standard imaging modalities, and found that the proposed approach can provide enhanced pCR prediction performance and thus may be a useful tool to aid oncologists in improving recommendation of treatment of patients. Subsequently, this approach to leverage volumetric deep radiomic features (which we name Cancer-Net BCa) can be further extended to other applications of CDIs in the cancer domain to further improve prediction performance.
For a detailed description of the methodology behind Cancer-Net BCa and a full description of the dataset used, please click here.
If you are a researcher or healthcare worker and you would like access to the GSInquire tool to use to interpret Cancer-Net BCa results on your data or existing data, please reach out to a28wong@uwaterloo.ca or alex@darwinai.ca
Our desire is to encourage broad adoption and contribution to this project. Accordingly this project has been licensed under the GNU Affero General Public License 3.0. Please see license file for terms.
If there are any technical questions after the README, FAQ, and past/current issues have been read, please post an issue or contact:
- Main ACRIN-6698 Archive: https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=50135447
- Cancer-Net BCa models (Cancer pCR prediction for breast cancer): https://github.com/catai9/Cancer-Net-BCa/tree/main/models
- Cancer-Net SCa models (Cancer detection for skin cancer): https://github.com/jamesrenhoulee/CancerNet-SCa/blob/main/docs/models.md
- Cancer-Net BCa dataset: https://www.kaggle.com/datasets/amytai/cancernet-bca
- DarwinAI Corp., Canada and Vision and Image Processing Research Group, University of Waterloo, Canada
- Alexander Wong
- Vision and Image Processing Research Group, University of Waterloo, Canada
- Amy Tai
- Hayden Gunraj
Cancer-Net BCa, an open access benchmark dataset of volumetric synthetic correlated diffusion imaging (CDIs) data acquisitions of breast cancer patients. Cancer-Net BCa is a part of the Cancer-Net global open-source, open-access initiative dedicated to accelerating advancement in machine learning to aid clinicians in the global fight against cancer.
The volumetric CDIs data acquisitions in the Cancer-Net BCa dataset were generated from a pre-treatment (T0) patient cohort of 253 patient cases from the American College of Radiology Imaging Network (ACRIN) 6698 study. The patients in this pre-treatment cohort had not received any neoadjuvant chemotherapy at the time of imaging. The Scarff-Bloom-Richardson (SBR) grade and post-treatment breast cancer pathologic complete response (pCR) to neoadjuvant chemotherapy are provided as output labels for training and evaluating purposes.
This dataset is being used to train and validate our Cancer-Net BCa models for pCR prediction from CDIs data acquisitions.
A demo of model inference with the Cancer-Net BCa-A model on a provided demo CDI^s image can be found in demo.ipynb.
Cancer-Net BCa (pCR prediction)
@inproceedings{cancer-net-bca,
title = {Cancer-Net BCa: Breast Cancer Pathologic Complete Response Prediction using Volumetric Deep Radiomic Features from Synthetic Correlated Diffusion Imaging},
author = {Tai, Chi-en Amy and Hodzic, Nedim and Flanagan, Nic and Gunraj, Hayden and Wong, Alexander},
booktitle = {Conference and Workshop on Neural Information Processing Systems (NeurIPS)},
series = {Medical Imaging Meets NeurIPS Workshop (MED-NeurIPS)},
year = {2022},
url = {https://arxiv.org/abs/2211.05308}
}
Cancer-Net BCa-S (grade prediction)
@inproceedings{cancer-net-bca-s,
title = {Cancer-Net BCa-S: Breast Cancer Grade Prediction using Volumetric Deep Radiomic Features from Synthetic Correlated Diffusion Imaging},
author = {Tai, Chi-en Amy and Gunraj, Hayden and Wong, Alexander},
booktitle = {Conference on Computer Vision and Pattern Recognition (CVPR)},
series = {Women in Computer Vision (WiCV)},
year = {2023},
url = {https://arxiv.org/abs/2304.05899}
}
Cancer-Net BCa Dataset
@inproceedings{cancer-net-bca-dataset,
title = {A Multi-Institutional Open-Source Benchmark Dataset for Breast Cancer Clinical Decision Support using Synthetic Correlated Diffusion Imaging Data},
author = {Tai, Chi-en Amy and Gunraj, Hayden and Wong, Alexander},
booktitle = {Conference on Computer Vision and Pattern Recognition (CVPR)},
series = {Women in Computer Vision (WiCV)},
year = {2023},
url = {https://arxiv.org/abs/2304.05623}
}