Multimodal Prediction of Five-Year Breast Cancer Recurrence in Women Who Receive Neoadjuvant Chemotherapy

Simona Rabinovici-Cohen (Corresponding Author), Xosé M. Fernández, Beatriz Grandal Rejo, Efrat Hexter, Oliver Hijano Cubelos, Juha Pajula, Harri Pölönen, Fabien Reyal, Michal Rosen-Zvi

Research output: Contribution to journalArticleScientificpeer-review

4 Citations (Scopus)


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.

Original languageEnglish
Article number3848
Issue number16
Publication statusPublished - Aug 2022
MoE publication typeA1 Journal article-refereed


  • breast cancer recurrence
  • deep learning
  • image processing
  • machine learning
  • magnetic resonance imaging (MRI)
  • neoadjuvant chemotherapy
  • radiomics


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