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Lumi-Guide: An Artificial-Intelligence-Driven Multimodal Framework for Optimizing Personalized Neoadjuvant Therapy

Yanting Liang1,2, Xiaobo Chen1,2,3, Penghao Lai1,2

  • 1Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou 510080, China.

Research (Washington, D.C.)
|June 1, 2026
PubMed
Summary

Predicting pathological complete response (pCR) in luminal breast cancer is difficult. The Lumi-Guide system, integrating MRI, clinical, and genomic data, accurately predicts treatment response, enabling personalized neoadjuvant therapy selection.

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Area of Science:

  • Oncology
  • Medical Imaging
  • Genomics

Background:

  • Accurate prediction of pathological complete response (pCR) to neoadjuvant therapy in luminal breast cancer is crucial for personalized treatment but remains challenging.
  • Current methods often fail to fully integrate diverse patient data, limiting treatment precision.

Purpose of the Study:

  • To develop and validate the Lumi-Guide system, a multimodal framework for predicting pCR in luminal breast cancer.
  • To integrate deep learning-based MRI analysis with clinical and genomic data for personalized neoadjuvant treatment selection.

Main Methods:

  • A multicenter study involving 1,097 patients with luminal breast cancer from 6 international datasets.
  • Development of a Swin Transformer-based MRI model (Lumi-I) and integration with clinical factors (Lumi-CI).
  • Radiogenomic analysis and development of a genomic model (Lumi-G) using 22 RNA biomarkers, integrated into a multimodal model (Lumi-CIG).

Main Results:

  • The Lumi-CI model demonstrated robust predictive performance (AUCs of 0.810, 0.819, 0.864) across validation and external test sets.
  • Radiogenomic analysis revealed distinct biological subtypes associated with Lumi-I scores, correlating with immune activity and estrogen signaling.
  • The Lumi-Guide 2-step triage system, prioritizing Lumi-CI and selectively using Lumi-CIG, optimized resource allocation and improved pCR rates in predicted responders.

Conclusions:

  • The Lumi-Guide system offers a clinically practical and biologically interpretable framework for personalized neoadjuvant therapy in luminal breast cancer.
  • Integration of multimodal data (imaging, clinical, genomic) significantly enhances the prediction of treatment response.
  • This framework facilitates scalable and precise treatment selection, improving actual pCR rates for patients across different treatment strategies.