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Simulating Post-Neoadjuvant Chemotherapy Breast Cancer MRI via Diffusion Model with Prompt Tuning

arXiv 2509.24185 ↗First posted 29 Sep 2025cs.CVcited by 0

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Abstract (arXiv)

Neoadjuvant chemotherapy (NAC) is a common therapy option before the main surgery for breast cancer. Response to NAC is monitored using follow-up dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Accurate prediction of NAC response helps with treatment planning. Here, we adopt maximum intensity projection images from DCE-MRI to generate post-treatment images (i.e., 3 or 12 weeks after NAC) from pre-treatment images leveraging the emerging diffusion model. We introduce prompt tuning to account for the known clinical factors affecting response to NAC. Our model performed better than other generative models in image quality metrics. Our model was better at generating images that reflected changes in tumor size according to pCR compared to other models. Ablation study confirmed the design choices of our method. Our study has the potential to help with precision medicine.

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References · 14

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  3. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  4. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,177
  5. 2020Denoising Diffusion Implicit Modelscited by 9,781
  6. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,255
  7. 2023Adding Conditional Control to Text-to-Image Diffusion Modelscited by 5,503
  8. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,527
  9. 2022Visual Prompt Tuningcited by 1,681
  10. 2021ResViT: Residual vision transformers for multi-modal medical image synthesiscited by 177
  11. 2021Breaking the Dilemma of Medical Image-to-image Translationcited by 82
  12. 2023Adaptive Latent Diffusion Model for 3D Medical Image to Image Translation: Multi-modal Magnetic Resonance Imaging Studycited by 64
2 more
  1. 2024RadiomicsFill-Mammo: Synthetic Mammogram Mass Manipulation with Radiomics Featurescited by 5
  2. 2024Radiomics-guided Multimodal Self-attention Network for Predicting Pathological Complete Response in Breast MRIcited by 3

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  1. 2025Comparing Conditional Diffusion Models for Synthesizing Contrast-Enhanced Breast MRI from Pre-Contrast Images
  2. 2025Pre to Post-Treatment Glioblastoma MRI Prediction using a Latent Diffusion Model
  3. 2024Towards Learning Contrast Kinetics with Multi-Condition Latent Diffusion Models
  4. 2026Pre- to Post-Contrast Synthesis of Breast DCE-MRI using Latent Bridge Matching
  5. 2026Rectified flow-based prediction of post-treatment brain MRI from pre-radiotherapy priors for patients with glioma
  6. 2025Mechanistic Learning with Guided Diffusion Models to Predict Spatio-Temporal Brain Tumor Growth
  7. 2026DiffKD-DCIS: Predicting Upgrade of Ductal Carcinoma In Situ with Diffusion Augmentation and Knowledge Distillation
  8. 2026MAMA-FLUX.2: Image-to-Image Synthesis of Post-Contrast Breast DCE-MRI for the MAMA-SYNTH Challenge