Multicenter study provides radiomic and biological insights into neoadjuvant chemotherapy response and prognosis in luminal breast cancer
Luminal breast cancer shows limited sensitivity to neoadjuvant chemotherapy (NAC) and substantial risk of late recurrence among non-pCR patients. Accurate tools to predict both NAC response and long-term prognosis are urgently needed. We retrospectively analyzed 850 patients from three cohorts (FUSCC, YNCC,tures were used for pCR prediction with XGBoost, whereas pre-, post-, and delta (Δ) features informed prognostic modeling with Cox-XGBoost. Performance was evaluated by AUC and C-index with internal and external validation. Independent value of the radiomics-derived RadScore was tested by I-SPY2). A subregion-aware, multitemporal radiomics model was built from DCE- and DWI-MRI, complemented by conventional MRI descriptors (e.g., breast edema, shrinkage pattern) and clinicopathologic variables. Pre-NAC feamultivariable regression. Radiogenomic analyses explored biological underpinnings. For response prediction, 253 patients from FUSCC (pCR 38/253) and 222 from YNCC (pCR 34/222) were included. Seven radiomics features were retained, mainly from high-perfusion subregions (4/7). The combined model achieved the best performance across cohorts, surpassing radiomics, traditional MRI, and clinical models, with AUCs of 0.83 [0.78-0.88], 0.78 [0.73-0.83], 0.63 [0.57-0.69] and 0.61 [0.55-0.67] in the validation cohort. RadScore derived from the radiomics remained an independent predictor of pCR after adjusting for clinical and MRI variables (OR = 2.06 [1.28–2.15]; P = 0.001). For prognosis, 318 non-pCR patients with ≥ 5 years follow-up were analyzed (FUSCC n = 160, 44 events; YNCC n = 158, 48 events). Nine radiomics features were retained, dominated by delta (Δ) features (5/9) and moderate-perfusion subregions (4/9). The combined model showed the highest prognostic discrimination outperforming radiomics, traditional MRI and clinical models in the validation cohort (0.84 [0.73-0.90] vs 0.81 [0.61, 0.84], 0.60 [0.73-0.90], 0.58 [0.47, 0.69]). Prognostic RadScore remained independentlnedy associated with recurrence (HR 4.78, 95% CI 2.54–5.89; P = 0.001), along with post-NAC Ki-67, diffuse edema, and non-concentric shrinkage. Radiogenomic validation confirmed that high-perfusion features driving response were enriched in drug-metabolism, PI3K-Akt, and estrogen signaling pathways, whereas moderate-perfusion delta (Δ) features driving prognosis aligned with hypoxia and immune-evasion programs associated with recurrence. Subregion-aware, multitemporal radiomics accurately and interpretabily predicts NAC response and long-term prognosis in luminal breast cancer, supporting individualized treatment selection and risk stratification.
Authors
- Yansong Bai
- Shiyun Sun
- Yingnan Bai (ORCID: https://orcid.org/0000-0002-8637-2301)
- Jinlong Zheng
- Jiayin Zhou
- Yingying Ding
- Tingting Jiang
- Yu Xie
- Yajia Gu
- Zhuolin Li
- Chao You
Institutions
- Shanghai Medical College of Fudan University (CN)
- Kunming Medical University (CN)
- Fudan University Shanghai Cancer Center (CN)
- Shanghai Center for Brain Science and Brain-Inspired Technology (CN)
Publication Details
- Journal
- Cancer Imaging
- Published
- 2026-02-02
- DOI
- https://doi.org/10.1186/s40644-026-00994-1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00