327P - Monitoring over time of pathological complete response to neoadjuvant chemotherapy in breast cancer patients through an ensemble vision transformers-based model

Background

Morphological and vascular peculiarities of breast cancer can change during neoadjuvant chemotherapy (NAC). Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) acquired pre- and mid-treatment quantitatively capture information about tumour heterogeneity as potential earlier indicators of pathological complete response (pCR) to NAC in breast cancer. This study aimed to develop an ensemble deep learning-based model, exploiting a Vision Transformer (ViT) architecture, which merges features automatically extracted from five segmented slices of both pre- and mid-treatment exams containing the maximum tumour area, to predict and monitor pCR to NAC.

Methods

Imaging data analysed in this study referred to a cohort of 86 breast cancer patients, randomly split into training and test cohorts at a ratio of 8:2, who underwent NAC and for which information regarding the pCR achievement was available (37.2% of patients achieved pCR). As far as we know, our research is the first proposal using ViTs on DCE-MRI exams to monitor pCR over time during NAC.

Results

The performances of the proposed model were assessed using standard evaluation metrics and promising results were achieved: AUC value of 91.4%, accuracy value of 82.4%, a specificity value of 80.0%, a sensitivity value of 85.7%, precision value of 75.0%, F-score value of 80.0%, G-mean value of 82.8%.

Conclusions

Finally, the heterogeneity changes in DCE-MRI at pre- and mid-treatment could affect the accuracy of pCR prediction to NAC.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

Raffaella Massafra.

Funding

Ministry of Health.

Disclosure

All authors have declared no conflicts of interest.