Cancer-Net BCa-S: Breast Cancer Grade Prediction using Volumetric Deep Radiomic Features from Synthetic Correlated Diffusion Imaging

TL;DR

This paper introduces Cancer-Net BCa-S, a deep learning model that predicts breast cancer severity from synthetic diffusion MRI, potentially reducing the need for invasive biopsies and improving treatment planning.

Contribution

It presents a novel volumetric deep radiomics approach using synthetic correlated diffusion imaging for accurate breast cancer grading, outperforming traditional methods.

Findings

Achieves higher accuracy in SBR grade prediction than standard imaging modalities.

Demonstrates the effectiveness of synthetic diffusion MRI for non-invasive cancer grading.

Provides an open-source tool for the medical imaging community.

Abstract

The prevalence of breast cancer continues to grow, affecting about 300,000 females in the United States in 2023. However, there are different levels of severity of breast cancer requiring different treatment strategies, and hence, grading breast cancer has become a vital component of breast cancer diagnosis and treatment planning. Specifically, the gold-standard Scarff-Bloom-Richardson (SBR) grade has been shown to consistently indicate a patient's response to chemotherapy. Unfortunately, the current method to determine the SBR grade requires removal of some cancer cells from the patient which can lead to stress and discomfort along with costly expenses. In this paper, we study the efficacy of deep learning for breast cancer grading based on synthetic correlated diffusion (CDI$^s$) imaging, a new magnetic resonance imaging (MRI) modality and found that it achieves better performance on SBR grade prediction compared to those learnt using gold-standard imaging modalities. Hence, we introduce Cancer-Net BCa-S, a volumetric deep radiomics approach for predicting SBR grade based on volumetric CDI$^s$ data. Given the promising results, this proposed method to identify the severity of the cancer would allow for better treatment decisions without the need for a biopsy. Cancer-Net BCa-S has been made publicly available as part of a global open-source initiative for advancing machine learning for cancer care.