{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T17:31:25Z","timestamp":1786469485104,"version":"build-2736575974"},"reference-count":30,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T00:00:00Z","timestamp":1786406400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Swiss Cancer League","award":["KFS-5524-02-2022"],"award-info":[{"award-number":["KFS-5524-02-2022"]}]},{"award":["KFS-5524-02-2022"],"award-info":[{"award-number":["KFS-5524-02-2022"]}],"id":[{"id":"https:\/\/ror.org\/01pd7my79","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancers"],"abstract":"<jats:p>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\u00b0 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\u00b0\/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.<\/jats:p>","DOI":"10.3390\/cancers18162585","type":"journal-article","created":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T16:30:32Z","timestamp":1786465832000},"page":"2585","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Zero-Shot Domain Adaptation of 2D Mammography Lesion Detection Model for 3D Breast CT Malignancy Detection"],"prefix":"10.3390","volume":"18","author":[{"given":"Andrew P.","family":"Leynes","sequence":"first","affiliation":[{"name":"Diagnostic and Interventional Radiology, University Hospital Zurich, University Zurich, 8091 Zurich, Switzerland"},{"name":"b-rayZ, 8952 Schlieren, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8667-3884","authenticated-orcid":false,"given":"Jonathan Andreas","family":"Saenger","sequence":"additional","affiliation":[{"name":"Diagnostic and Interventional Radiology, University Hospital Zurich, University Zurich, 8091 Zurich, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anna","family":"Weber","sequence":"additional","affiliation":[{"name":"Institute of Diagnostic and Interventional Radiology, GZO Regional Health Center, 8620 Wetzikon, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3295-6619","authenticated-orcid":false,"given":"Thomas","family":"Frauenfelder","sequence":"additional","affiliation":[{"name":"Diagnostic and Interventional Radiology, University Hospital Zurich, University Zurich, 8091 Zurich, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andreas","family":"Boss","sequence":"additional","affiliation":[{"name":"Diagnostic and Interventional Radiology, University Hospital Zurich, University Zurich, 8091 Zurich, Switzerland"},{"name":"Institute of Diagnostic and Interventional Radiology, GZO Regional Health Center, 8620 Wetzikon, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,11]]},"reference":[{"key":"ref_1","first-page":"394","article-title":"Global Cancer Statistics 2018: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries","volume":"68","author":"Bray","year":"2018","journal-title":"CA Cancer J. 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