Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel (VUB), Pleinlaan 2, Brussels, B-1050, Belgium.
Radiology Department, Universitair Ziekenhuis (UZ) Brussels, Laarbeeklaan 101, Brussels, 1090, Belgium.
BMC Cancer. 2022 Feb 11;22(1):162. doi: 10.1186/s12885-021-09133-4.
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.
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.
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%.
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.
在乳房 X 光检查中检测到可疑微钙化是恶性乳腺肿瘤的最早迹象之一。在许多情况下，放射科医生很难根据二维乳房成像方式上的微钙化外观来评估其特征。本研究的目的是：（a）分析通过高分辨率 3D 扫描仪扫描乳房组织提取的乳腺微钙化的形状和纹理特征与恶性肿瘤之间的关系；（b）评估微钙化诊断良性/恶性患者的潜力。
使用微 CT 扫描仪以 9μm 的分辨率对 94 名女性患者进行了可疑微钙化的乳房 X 光检查。对 3504 个提取的微钙化进行了几种预处理技术。提取了大量的放射组学特征，试图捕捉良性和恶性病变中发生的微钙化之间的差异。使用机器学习算法来诊断：（a）单个微钙化；（b）样本。对于样本，评估了几种将单个微钙化结果组合到样本结果的方法。
我们可以以 77.32%的准确率、61.15%的灵敏度和 89.76%的特异性来分类单个微钙化。在样本水平的诊断中，我们达到了 84.04%的准确率、86.27%的灵敏度和 81.39%的特异性。
通过在常规乳房成像方式目前不可能达到的细节水平研究微钙化的特征，我们的分类结果表明乳腺微钙化与恶性肿瘤之间存在很强的关联。与纯形状特征相比，在变换域中提取的微钙化纹理特征对良性/恶性个体微钙化和样本的分类具有更高的区分能力。
