Purpose: Breast cancer screening relies primarily on two-dimensional (2D) digital mammography, which is limited by tissue superposition and compression-related patient discomfort. Dedicated spiral breast computed tomography (BCT) overcomes these limitations by providing isotropic, fully three-dimensional (3D) volumetric images without compression. However, translating artificial intelligence (AI) advancements to 3D BCT lesion detection is hindered by the scarcity of large-scale annotated 3D datasets and substantial computational constraints. The purpose of this study is to evaluate the feasibility of repurposing a pre-trained 2D mammography AI model for 3D BCT malignancy detection without dedicated model retraining. Methods: In this exploratory proof-of-concept study, the dataset comprised BCT examinations of female patients with malignant lesions. We developed a slab projection framework that systematically rotates the BCT volume and extracts overlapping tissue slabs to simulate mammography-like 2D images compatible with the input requirements of the pre-existing 2D mammography model. The 2D inferences were subsequently back-projected to their original 3D spatial coordinates and aggregated via a consensus voting scheme. Optimization and performance evaluation were performed on the same dataset to establish an internal baseline. Object-level detection performance was assessed using the F1 score, and segmentation quality was evaluated using the Dice coefficient. Results: Application of the proposed framework enabled 3D lesion localization in BCT volumes without requiring domain-specific training. An optimal configuration was found with a sparser angular sampling combined with medium slab thickness (45° angular spacing, 50-voxel thickness), which achieved an F1 score of 0.346, with 48.3% precision and 26.9% recall. Evaluation of segmentation quality corroborated these findings, yielding a maximum mean Dice coefficient of 0.336 for the 45°/30-voxel configuration. Conclusions: This exploratory proof-of-concept demonstrates the technical feasibility of adapting a pre-trained 2D mammography AI model to volumetric BCT without architectural modifications or retraining. The reported performance represents an internal same-dataset baseline under substantial domain shift and should not be interpreted as generalizable or clinically deployable performance. These findings provide a foundation for future domain-adapted models and independent external validation of AI-based tumour detection in breast CT.