Predicting response to neoadjuvant therapy in breast cancer using longitudinal DCE-MRI deep learning integrated with tumor microenvironment data

ObjectiveThis study developed and validated a multimodal fusion model to enable the early and accurate prediction of pathological complete response (pCR) to neoadjuvant therapy (NAT) in breast cancer. The model integrates deep learning (DL) features derived from longitudinal dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) acquired early during treatment, peripheral blood inflammatory (PBI) indices, and baseline levels of tumor-infiltrating lymphocytes (TILs).MethodsA total of 262 breast cancer patients receiving NAT were retrospectively enrolled and divided into a training cohort (n=183) and a validation cohort (n=79) based on the time of surgery.

Deep learning models (Pre-NAT DL and Post-2nd-NAT DL) were constructed using features extracted from pre-treatment (baseline) and post-second-cycle DCE-MRI images, respectively. An immune-inflammation model was built using baseline TILs and dynamically changing PBI indices.

A clinical model was developed based on baseline clinicopathological characteristics. Finally, a combined model was constructed by integrating features from all the aforementioned modalities.