Graph Representation Learning of Longitudinal Medical Imaging Trajectories for Treatment Response Prediction
arXiv:2607.04912v1 Announce Type: cross Abstract: In patients with breast cancer, pathological complete response (pCR) has been established as a clinically meaningful surrogate marker for long-term outcomes. While commonly treated with neoadjuvant chemotherapy (NACT), effective treatment decision-making remains challenging, as therapeutic response can vary substantially across patients, calling for predictive models capable of accurately estimating individualized treatment response. To address this, we propose an imaging-based 3D spatio-temporal framework for treatment response prediction that
The rapid advancements in graph neural networks and 3D imaging analytics are converging, enabling more sophisticated predictive models for complex medical scenarios.
This development can significantly improve personalized cancer treatment decisions, leading to better patient outcomes and more efficient healthcare resource allocation.
Treatment response prediction in oncology moves closer to being data-driven and individualized, potentially reducing trial-and-error approaches in neoadjuvant chemotherapy.
- · Oncology patients
- · Medical AI companies
- · Healthcare providers
- · Pharmaceutical R&D
- · Traditional diagnostic methods
- · Inefficient drug protocols
Improved efficacy of neoadjuvant chemotherapy due to better patient stratification.
Increased demand for advanced medical imaging devices and AI interpretation platforms.
Reduced healthcare costs over time by minimizing ineffective treatments and their associated side effects.
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Read at arXiv cs.AI