What question did this study set out to answer?

The aim is to develop an AI-driven model for personalized treatment strategies in HER2-positive breast cancer.

February 22, 2026

Abstract PS3-06-09: Her2-ladder: a spatially interpretable artificial intelligence-driven model for tailored dual-targeted therapy in HER2-positive breast cancer

XWX. WuFudan University Shanghai Cancer Center DMD. MaFudan University Shanghai Cancer Center HLH. LvFudan University Shanghai Cancer Center

Key Points

The aim is to develop an AI-driven model for personalized treatment strategies in HER2-positive breast cancer.
Developed HER2-LADDER framework integrating clinicopathological and spatial features
Trained and tested model using patients from the FASCINATE-N trial
Validated externally across multiple cohorts
Employed spatial profiling techniques to explore biological mechanisms
HER2-LADDER achieved an AUC of 0.986 in training and 0.939 in testing cohorts
Patients classified into Low-, Medium-, and High-score groups for treatment recommendations
Effective identification for de-escalation or standard therapy
Revealed biological determinants like HER2-enriched tumor cell patterns and immune interactions

Abstract

Abstract Background Neoadjuvant dual HER2 blockade with trastuzumab and pertuzumab plus chemotherapy represents the current standard of care for HER2-positive breast cancer. However, marked heterogeneity in patient response has intensified discussions on treatment de-escalation for low-risk individuals and alternative therapies for resistant cases. Presently, clinically robust precision oncology tools facilitating such individualized therapeutic strategies remain unavailable. Methods We developed HER2-LADDER (Layered AI-based Dual-targeteD anti-HER2 Recommendation), an AI-driven decision-support framework that integrates clinicopathological information with spatial features derived from routine hematoxylin and eosin (H 2025 Dec 9-12; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(4 Suppl):Abstract nr PS3-06-09.

AI에게 질문

Bookmark

View Full Paper

Cite This Study

Wu et al. (Tue,) studied this question.

synapsesocial.com/papers/699a9dae482488d673cd3c76 https://doi.org/https://doi.org/10.1158/1557-3265.sabcs25-ps3-06-09

AI에게 질문

Bookmark

View Full Paper