ArticleBMC cancer2022
Improved automated early detection of breast cancer based on high resolution 3D micro-CT microcalcification images.
Article in BMC cancer, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 23 citations in OpenAlex.
- Radiomic Characterization of Breast Tissue from Breast CT Images Obtained with Synchrotron Beams.Tomography (Ann Arbor, Mich.) · 2026Article
- Micro-CT radiomics reveals the microcalcification-surrounding tissue interface as the diagnostic epicenter of breast microcalcifications.Breast cancer research : BCR · 2026Article
- Predicting fracture risk in Y-TZP zirconia implants using radiomics-based artificial intelligence.BMC oral health · 2026Article
- High-resolution microCT to assess breast microcalcification morphometry by histologic lesion subtype and radiologic classification.Breast cancer research and treatment · 2025Article
- Mechanisms of osteopontin-stabilized amorphous calcium phosphate calcification in benign and pre-malignant breast disease.Scientific reports · 2025Article
- Innovative Approaches to Early Detection of Cancer-Transforming Screening for Breast, Lung, and Hard-to-Screen Cancers.Cancers · 2025Review
- Enhancing Microcalcification Detection in Mammography with YOLO-v8 Performance and Clinical Implications.Diagnostics (Basel, Switzerland) · 2024Article
- Micro-CT Microcalcification Analysis: A Scoping Review of Current Applications and Future Potential in Breast Cancer Research.Tomography (Ann Arbor, Mich.) · 2024Article
- Bibliometric analysis of phosphoglycerate kinase 1 expression in breast cancer and its distinct upregulation in triple-negative breast cancer.World journal of clinical oncology · 2024Article
- Enhancing early breast cancer diagnosis through automated microcalcification detection using an optimized ensemble deep learning framework.PeerJ. Computer science · 2024Article
- The Value of Micro-CT in the Diagnosis of Lung Carcinoma: A Radio-Histopathological Perspective.Diagnostics (Basel, Switzerland) · 2023Review
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundThe 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.
methodsBiopsy 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.
resultsWe 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%.
conclusionsBy 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.
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