Dedicated Breast PET-Based Deep Learning Radiomics for Prediction of Pathologic Complete Response to Neoadjuvant Chemotherapy in HER2-Positive Breast Cancer

Objectives: To exploratorily evaluate the potential of baseline dedicated breast PET (D-PET) for noninvasive prediction of pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in HER2-positive (HER2+) breast cancer, and to investigate a fusion strategy integrating conventional radiomics and deep learning features. Methods: We developed a multi-representation framework with radiomics based on data-driven high-/low-uptake metabolic subregions and deep learning trained on standardized 3D tumor volumes, and intratumoral heterogeneity (ITH) was quantified on the largest slice as an additional comparator. The outputs of these pathways were subsequently integrated through feature-level and decision-level fusion. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), and interpretability analyses were applied to identify image regions and features contributing to predictions. Results: In a HER2-positive breast cancer cohort (n = 147) with baseline D-PET, deep learning (3D ResNet, AUC = 0.79) and radiomics (logistic regression, AUC = 0.78) achieved comparable performance on the primary test set, whereas the ITH model showed limited value (AUC = 0.61). Fusion further improved discrimination on test set 1, with an AUC of 0.83 for decision-level fusion and 0.84 for feature-level fusion. On test set 2, decision-level fusion achieved the highest AUC (0.84), and feature-level fusion maintained stable performance (AUC = 0.80). Conclusions: In this exploratory study, baseline D-PET showed promising performance for noninvasive prediction of NAC response in HER2+ breast cancer. The fusion of deep learning and radiomics yielded improvements over single-representation models, highlighting the potential role of D-PET models as decision-support tools.