TY - JOUR
T1 - Multimodal Prediction of Five-Year Breast Cancer Recurrence in Women Who Receive Neoadjuvant Chemotherapy
AU - Rabinovici-Cohen, Simona
AU - Fernández, Xosé M.
AU - Grandal Rejo, Beatriz
AU - Hexter, Efrat
AU - Hijano Cubelos, Oliver
AU - Pajula, Juha
AU - Pölönen, Harri
AU - Reyal, Fabien
AU - Rosen-Zvi, Michal
N1 - Funding Information:
Research reported in this publication was partially supported by the European Union’s Horizon 2020 research and innovation program under grant agreement No. 780495. The authors are solely responsible for the content of this paper. It does not represent the opinion of the European Union, and the European Union is not responsible for any use that might be made of data appearing therein.
Publisher Copyright:
© 2022 by the authors.
PY - 2022/8
Y1 - 2022/8
N2 - In current clinical practice, it is difficult to predict whether a patient receiving neoadjuvant chemotherapy (NAC) for breast cancer is likely to encounter recurrence after treatment and have the cancer recur locally in the breast or in other areas of the body. We explore the use of clinical history, immunohistochemical markers, and multiparametric magnetic resonance imaging (DCE, ADC, Dixon) to predict the risk of post-treatment recurrence within five years. We performed a retrospective study on a cohort of 1738 patients from Institut Curie and analyzed the data using classical machine learning, image processing, and deep learning. Our results demonstrate the ability to predict recurrence prior to NAC treatment initiation using each modality alone, and the possible improvement achieved by combining the modalities. When evaluated on holdout data, the multimodal model achieved an AUC of 0.75 (CI: 0.70, 0.80) and 0.57 specificity at 0.90 sensitivity. We then stratified the data based on known prognostic biomarkers. We found that our models can provide accurate recurrence predictions (AUC > 0.89) for specific groups of women under 50 years old with poor prognoses. A version of our method won second place at the BMMR2 Challenge, with a very small margin from being first, and was a standout from the other challenge entries.
AB - In current clinical practice, it is difficult to predict whether a patient receiving neoadjuvant chemotherapy (NAC) for breast cancer is likely to encounter recurrence after treatment and have the cancer recur locally in the breast or in other areas of the body. We explore the use of clinical history, immunohistochemical markers, and multiparametric magnetic resonance imaging (DCE, ADC, Dixon) to predict the risk of post-treatment recurrence within five years. We performed a retrospective study on a cohort of 1738 patients from Institut Curie and analyzed the data using classical machine learning, image processing, and deep learning. Our results demonstrate the ability to predict recurrence prior to NAC treatment initiation using each modality alone, and the possible improvement achieved by combining the modalities. When evaluated on holdout data, the multimodal model achieved an AUC of 0.75 (CI: 0.70, 0.80) and 0.57 specificity at 0.90 sensitivity. We then stratified the data based on known prognostic biomarkers. We found that our models can provide accurate recurrence predictions (AUC > 0.89) for specific groups of women under 50 years old with poor prognoses. A version of our method won second place at the BMMR2 Challenge, with a very small margin from being first, and was a standout from the other challenge entries.
KW - breast cancer recurrence
KW - deep learning
KW - image processing
KW - machine learning
KW - magnetic resonance imaging (MRI)
KW - neoadjuvant chemotherapy
KW - radiomics
UR - http://www.scopus.com/inward/record.url?scp=85137315592&partnerID=8YFLogxK
U2 - 10.3390/cancers14163848
DO - 10.3390/cancers14163848
M3 - Article
AN - SCOPUS:85137315592
SN - 2072-6694
VL - 14
JO - Cancers
JF - Cancers
IS - 16
M1 - 3848
ER -