What question did this study set out to answer?

This research aims to predict the response of breast cancer patients to neoadjuvant therapy using a novel graph learning method.

February 22, 2026

Abstract PS3-06-14: Interpretable Graph Learning on Preoperative Biopsy Predicts Pathological Complete Response to Neoadjuvant Therapy in Breast Cancer

Key Result

CSV-Net predicted pathological complete response to neoadjuvant therapy in breast cancer with ROC-AUCs of 0.845 internal and 0.815 external, outperforming prior methods.

Key Points

This research aims to predict the response of breast cancer patients to neoadjuvant therapy using a novel graph learning method.
Developed a graph learning framework called CSV-Net integrating biopsy images with clinicopathological data.
Utilized spatial-variant graph convolution and graph pooling for enhanced interpretability.
Evaluated the model on a dataset of 950 breast cancer patients from five medical centers.
Achieved ROC-AUCs of 0.845 and 0.815 on internal and external validation sets, respectively.
Demonstrated strong performance across different molecular subtypes, including Luminal and TNBC.
Confirmed clinical utility through analysis of disease-free survival and transcriptomic profiling.

Structured PICO

Does CSV-Net accurately predict pathological complete response to neoadjuvant therapy in breast cancer patients?

Population

950 breast cancer patients receiving neoadjuvant therapy from 5 medical centers

Intervention

CSV-Net (clinicopathological-guided spatial-variant network) integrating whole-slide images from biopsy samples with clinicopathological variables

Comparator

State-of-the-art methods

Outcome

Pathological complete response (pCR)surrogate

A novel graph learning framework integrating whole-slide images and clinicopathological variables accurately predicts pathological complete response to neoadjuvant therapy in breast cancer patients.

Main Result

Absolute Event Rate: 0% vs 0%

Abstract

Abstract Predicting neoadjuvant therapy (NAT) response in breast cancer patients still remains challenging due to the high heterogeneity of tumors and modality differences in medical data. In this paper, we proposed a novel graph learning framework, a clinicopathological-guided spatial-variant network (CSV-Net), which integrates whole-slide images (WSIs) from biopsy samples with clinicopathological (CP) variables to predict pathological complete response (pCR) of patients receiving NAT. CSV-Net incorporates two novel components: (1) a spatial-variant graph convolution (SV-GraphConv) layer, which models the spatial-semantic heterogeneity inherent in whole-slide images (WSIs), and (2) a clinicopathological-guided graph pooling (CG-GraphPool) module that dynamically integrates clinicopathological (CP) variables with WSI features at a fine-grained level, enhancing both performance and interpretability. We evaluated CSV-Net on a retrospective dataset of 950 breast cancer patients from 5 medical centers. CSV-Net achieved ROC-AUCs of 0.845 (95% CI: 0.801-0.886) and 0.815 (95% CI: 0.755-0.873) in predicting pCR on the internal and external validation sets, respectively, outperforming state-of-the-art methods. Notably, subgroup analysis highlighted CSV-Net's robust performance in patients with different molecular subtypes (e.g. Luminal, HER2 overexpression and TNBC), underscoring its clinical utility. Furthermore, disease-free survival relevance and transcriptomic profiling experiments confirmed the model's prognostic value and biological interpretability, suggesting potential applications in personalized treatment planning and biomarker discovery. Citation Format: W. Hou, Z. Pu, Z. Xu, A. Wu, Z. Liu, K. Zhao, C. Duan, J. Guo, K. Chen, S. Qiu, Z. Du, X. Zhao, J. Bai, H. Zeng, G. Zhang. Interpretable Graph Learning on Preoperative Biopsy Predicts Pathological Complete Response to Neoadjuvant Therapy in Breast Cancer abstract. In: Proceedings of the San Antonio Breast Cancer Symposium 2025; 2025 Dec 9-12; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(4 Suppl):Abstract nr PS3-06-14.

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