Background: The detection of suspicious microcalcifications on mammography represents one of the earliest signs of a malignant breast tumor. Assessing microcalcifications’ characteristics based on their appearance on 2D breast imaging modalities is in many cases challenging for radiologists. The aims of this study were to: (a) analyse the association of shape and texture properties of breast microcalcifications (extracted by scanning breast tissue with a high resolution 3D scanner) with malignancy, (b) evaluate microcalcifications’ potential to diagnose benign/malignant patients. Methods: Biopsy samples of 94 female patients with suspicious microcalcifications detected during a mammography, were scanned using a micro-CT scanner at a resolution of 9 μm. Several preprocessing techniques were applied on 3504 extracted microcalcifications. A high amount of radiomic features were extracted in an attempt to capture differences among microcalcifications occurring in benign and malignant lesions. Machine learning algorithms were used to diagnose: (a) individual microcalcifications, (b) samples. For the samples, several methodologies to combine individual microcalcification results into sample results were evaluated. Results: We could classify individual microcalcifications with 77.32% accuracy, 61.15% sensitivity and 89.76% specificity. At the sample level diagnosis, we achieved an accuracy of 84.04%, sensitivity of 86.27% and specificity of 81.39%. Conclusions: By studying microcalcifications’ characteristics at a level of details beyond what is currently possible by using conventional breast imaging modalities, our classification results demonstrated a strong association between breast microcalcifications and malignancies. Microcalcification’s texture features extracted in transform domains, have higher discriminating power to classify benign/malignant individual microcalcifications and samples compared to pure shape-features.

Micro-computed tomography (micro-CT) is a non-destructive imaging technique that offers highly detailed, 3D visualizations of a target specimen. In the context of breast cancer, micro-CT has emerged as a promising tool for analyzing microcalcifications (MCs), tiny calcium deposits that can indicate at an early stage the presence of cancer. This review aimed to explore the current applications of micro-CT in analyzing breast MCs (ex vivo, animal models, and phantoms) and to identify potential avenues in scientific research. We followed PRISMA guidelines for scoping reviews, yielding 18 studies that met our criteria. The studies varied in their purposes: feasibility and optimization of micro-CT for breast cancer imaging and MC analysis/diagnosis, comparison with other imaging modalities, development of micro-CT scanners and processing algorithms, enhancement of MC detection through contrast agents, etc. In conclusion, micro-CT offers superior image quality and detailed visualization of breast tissue (especially tumor masses and MCs), surpassing traditional methods like mammography and approaching the level of detail of histology. It holds great potential to enhance our understanding of MC characteristics and breast pathologies when used as a supplementary tool. Further research will solidify its role in clinical practice and potentially expand its applications in breast cancer studies.

Three-dimensional imaging technologies are increasingly used in breast reconstructive and plastic surgery due to their potential for efficient and accurate preoperative assessment and planning. This study systematically evaluates the accuracy and consistency of six commercially available 3D scanning applications (apps)—Structure Sensor, 3D Scanner App, Heges, Polycam, SureScan, and Kiri—in reconstructing the female torso. To avoid variability introduced by human subjects, a silicone breast mannequin model was scanned, with fiducial markers placed at known anatomical landmarks. Manual distance measurements were obtained using calipers by two independent evaluators and compared to digital measurements extracted from 3D reconstructions in Blender software. Each scan was repeated six times per application to ensure reliability. SureScan demonstrated the lowest mean error (2.9 mm), followed by Structure Sensor (3.0 mm), Heges (3.6 mm), 3D Scanner App (4.4 mm), Kiri (5.0 mm), and Polycam (21.4 mm), which showed the highest error and variability. Even the app using an external depth sensor (Structure Sensor) showed no statistically significant accuracy advantage over those using only the iPad’s built-in camera (except for Polycam), underscoring that software is the primary driver of performance, not hardware (alone). This work provides practical insights for selecting mobile 3D scanning tools in clinical workflows and highlights key limitations, such as scaling errors and alignment artifacts. Future work should include patient-based validation and explore deep learning to enhance reconstruction quality. Ultimately, this study lays the foundation for more accessible and cost-effective 3D imaging in surgical practice, showing that smartphone-based tools can produce clinically useful scans.

Microcalcifications (MCs) are small calcium deposits which are often present in breast tissue. Although they may be an early indicator of breast cancer, they are often present in healthy women. Judging on the malignancy of MCs based on their appearance on mammograms is a challenging task for radiologists as mammography projects a 3D object onto a 2D plane, as a consequence, superposition of tissue may alter their appearance. Although Computer Aided Detection and Diagnosis (CAD) systems have been developed to assist radiologists in detecting and characterizing MCs, they mainly focus on analyzing MCs' spatial configuration rather than their individual characteristics. In this study, we propose a CAD system for the characterization of individual MCs, whose shape is visualized in 3D by scanning breast tissue with micro-CT, a high resolution 3D imaging modality. By calculating a large amount of radiomic features we are able to classify MCs as benign or malignant with 75.88% accuracy, 62.13% sensitivity and 86.39% specificity, outperforming the state of the art and proving that there is a difference regarding the shape and the texture of individual MCs. Although this methodology is not applicable in vivo, its potential for use in intra operative imaging is high, as it can reduce the waiting time between tissue extraction and result of the anatomopathological investigation, facilitating and accelerating the procedure.