# Radiomics-guided Multimodal Self-attention Network for Predicting Pathological Complete Response in Breast MRI

arXiv: https://arxiv.org/abs/2406.02936 · HTML: https://mlsanity.com/paper/2406.02936/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 11 references that are in the map.
- First posted on arXiv 5 Jun 2024.

## Builds on (most cited first, 4 of 4)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)

## Built on by (most cited first, 3 of 3)

- [Domain Aware Multi-Task Pretraining of 3D Swin Transformer for T1-weighted Brain MRI](https://mlsanity.com/paper/2410.00410/) (2024)
- [Tumor Synthesis conditioned on Radiomics](https://mlsanity.com/paper/2509.24182/) (2025)
- [Simulating Post-Neoadjuvant Chemotherapy Breast Cancer MRI via Diffusion Model with Prompt Tuning](https://mlsanity.com/paper/2509.24185/) (2025)

## Abstract (arXiv)

Breast cancer is the most prevalent cancer among women and predicting pathologic complete response (pCR) after anti-cancer treatment is crucial for patient prognosis and treatment customization. Deep learning has shown promise in medical imaging diagnosis, particularly when utilizing multiple imaging modalities to enhance accuracy. This study presents a model that predicts pCR in breast cancer patients using dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) and apparent diffusion coefficient (ADC) maps. Radiomics features are established hand-crafted features of the tumor region and thus could be useful in medical image analysis. Our approach extracts features from both DCE MRI and ADC using an encoder with a self-attention mechanism, leveraging radiomics to guide feature extraction from tumor-related regions. Our experimental results demonstrate the superior performance of our model in predicting pCR compared to other baseline methods.