ArXiv Preprint
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
(CDI$^s$) 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 CDI$^s$ 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 CDI$^s$ in the cancer domain to further improve prediction
performance.
Chi-en Amy Tai, Nedim Hodzic, Nic Flanagan, Hayden Gunraj, Alexander Wong
2022-11-10
