Clinical utility of dual-energy contrast-enhanced spectral mammography for breast microcalcifications without associated mass: a preliminary analysis.

OBJECTIVE: To assess the utility of dual-energy contrast-enhanced spectral mammography (DE-CESM) for evaluation of suspicious malignant microcalcifications. METHODS: Two hundred and fifty-six DE-CESMs were reviewed from 2012-2013, 59 cases fulfilled the following criteria and were enrolled for analysis: (1) suspicious malignant microcalcifications (BI-RADS 4) on mammogram, (2) no related mass, (3) with pathological diagnoses. The microcalcification morphology and associated enhancement were reviewed to analyse the accuracy of the diagnosis and cancer size measurements versus the results of pathology. RESULTS: Of the 59 microcalcifications, 22 were diagnosed as cancers, 19 were atypical lesions and 18 were benign lesions. Twenty (76.9 %) cancers, three (11.55 %) atypia and three (11.55 %) benign lesions revealed enhancement. The true-positive rate of intermediate- and high-concern microcalcifications was significantly higher than that of low-concern lesions (93.75 % vs. 50 %). Overall, the diagnostic sensitivity of enhancement was 90.9 %, with 83.78 % specificity, 76.92 % positive predictive value, 93.94 % negative predictive value and 86.4 % accuracy. Performance was good (AUC = 0.87) according to a ROC curve and cancer size correlation with a mean difference of 0.05 cm on a Bland-Altman plot. CONCLUSIONS: DE-CESM provides additional enhancement information for diagnosing breast microcalcifications and measuring cancer sizes with high correlation to surgicohistology. KEY POINTS: • DE-CESM provides additional enhancement information for diagnosing suspicious breast microcalcifications. • The enhanced cancer size closely correlates to microscopy by Bland-Altman plot. • DE-CESM could be considered for evaluation of suspicious malignant microcalcifications.

Interpretable Radiomic Signature for Breast Microcalcification Detection and Classification.

Breast microcalcifications are observed in 80% of mammograms, and a notable proportion can lead to invasive tumors. However, diagnosing microcalcifications is a highly complicated and error-prone process due to their diverse sizes, shapes, and subtle variations. In this study, we propose a radiomic signature that effectively differentiates between healthy tissue, benign microcalcifications, and malignant microcalcifications. Radiomic features were extracted from a proprietary dataset, composed of 380 healthy tissue, 136 benign, and 242 malignant microcalcifications ROIs. Subsequently, two distinct signatures were selected to differentiate between healthy tissue and microcalcifications (detection task) and between benign and malignant microcalcifications (classification task). Machine learning models, namely Support Vector Machine, Random Forest, and XGBoost, were employed as classifiers. The shared signature selected for both tasks was then used to train a multi-class model capable of simultaneously classifying healthy, benign, and malignant ROIs. A significant overlap was discovered between the detection and classification signatures. The performance of the models was highly promising, with XGBoost exhibiting an AUC-ROC of 0.830, 0.856, and 0.876 for healthy, benign, and malignant microcalcifications classification, respectively. The intrinsic interpretability of radiomic features, and the use of the Mean Score Decrease method for model introspection, enabled models' clinical validation. In fact, the most important features, namely GLCM Contrast, FO Minimum and FO Entropy, were compared and found important in other studies on breast cancer.

A single-institution retrospective evaluation of noninvasive localization for non-palpable breast microcalcification.

TECHNIQUE: From January 1, 2018, to December 31, 2021, we localized the breast microcalcification of 40 patients before the surgical excision. We measured the distance between the nipple and the center of the calcification on the CC view and the ML view, respectively. The operation proceeded around the intersection between two lines, slightly larger than the diameter of the microcalcification. We also analyze the pathological findings. RESULTS: All 40 patients successfully detected calcification by mammograms preoperatively using the method mentioned above. 38 patients have the microcalcification removal within the one-time operation, while the other two underwent an extended lumpectomy. 20 of 40 calcifications (50 %) were malignant and 12(30 %) were precancerous lesions. In the group of women older than 45 years old, the percentages of malignant and atypical hyperplasias are 56.25 % (18/32) and 31.25 % (10/32) respectively. CONCLUSION: Our non-invasive method of preoperative localization is safe and cost-effective. Furthermore, initial observations suggest that there may be a link between age and malignant microcalcification.

The impact of (simulated) resolution on breast cancer diagnosis based on high-resolution 3D micro-CT microcalcification images.

BACKGROUND: Breast microcalcifications (MCs) are considered to be a robust marker of breast cancer. A machine learning model can provide breast cancer diagnosis based on properties of individual MCs - if their characteristics are captured at high resolution and in 3D. PURPOSE: The main purpose of the study was to explore the impact of image resolution (8 µm, 16 µm, 32 µm, 64 µm) when diagnosing breast cancer using radiomics features extracted from individual high resolution 3D micro-CT MC images. METHODS: Breast MCs extracted from 86 female patients were analyzed at four different spatial resolutions: 8 µm (original resolution) and 16 µm, 32 µm, 64 µm (simulated image resolutions). Radiomic features were extracted at each image resolution in an attempt, to find a compact feature signature allowing to distinguish benign and malignant MCs. Machine learning algorithms were used for classifying individual MCs and samples (i.e., patients). For sample diagnosis, a custom-based thresholding approach was used to combine individual MC results into sample results. We conducted classification experiments when using (a) the same MCs visible in 8 µm, 16 µm, 32 µm, and 64 µm resolution; (b) the same MCs visible in 8 µm, 16 µm, and 32 µm resolution; (c) the same MCs visible in 8 µm and 16 µm resolution; (d) all MCs visible in 8 µm, 16 µm, 32 µm, and 64 µm resolution. Accuracy, sensitivity, specificity, AUC, and F1 score were computed for each experiment. RESULTS: The individual MC results yielded an accuracy of 77.27%, AUC of 83.83%, F1 score of 77.25%, sensitivity of 80.86%, and specificity of 72.2% at 8 µm resolution. For the individual MC classifications we report for the F1 scores: a 2.29% drop when using 16 µm instead of 8 µm, a 4.01% drop when using 32 µm instead of 8 µm, a 10.69% drop when using 64 µm instead of 8 µm. The sample results yielded an accuracy and F1 score of 81.4%, sensitivity of 80.43%, and specificity value of 82.5% at 8 µm. For the sample classifications we report for F1 score values: a 6.3% drop when using 16 µm instead of 8 µm, a 4.91% drop when using 32 µm instead of 8 µm, and a 6.3% drop when using 64 µm instead of 8 µm. CONCLUSIONS: The highest classification results are obtained at the highest resolution (8 µm). If breast MCs characteristics could be visualized/captured in 3D at a higher resolution compared to what is used nowadays in digital mammograms (approximately 70 µm), breast cancer diagnosis will be improved.

A shortcut weighted fusion pyramid network for microcalcification detection in breast mammograms.

BACKGROUND: High-precision detection for individual and clustered microcalcifications in mammograms is important for the early diagnosis of breast cancer. Large-scale differences between the two types and low-contrast images are major difficulties faced by radiologists when performing diagnoses. OBJECTIVE: Deep learning-based methods can provide end-to-end solutions for efficient detection. However, multicenter data bias, the low resolution of network inputs, and scale differences between microcalcifications lead to low detection rates. Aiming to overcome the aforementioned limitations, we propose a pyramid feature network for microcalcification detection in mammograms, MicroDMa, with adaptive image adjustment and shortcut connections. METHODS: First, mammograms from multiple centers are represented as histograms and cropped by adaptive image adjustment, which mitigates the impact of dataset bias. Second, the proposed shortcut connection pyramid network ensures that the feature map contains more information for multiscale objects, while a shortcut path that jumps over layers enhances the efficiency of feature propagation from bottom to top. Third, the weights of each feature map at different scales in the fusion are trainable; thus, the network can automatically learn the contributions of all feature maps in the fusion stage. RESULT: Experiments were conducted on our in-house dataset and the public dataset INbreast. When the average number of positives per image is one on the in-house dataset, the recall rates of MicroDMa are the 96.8% for individual microcalcification and 98.9% for clustered microcalcification, which are higher than 69.1% and 91.2% achieved by recent deep learning model. Free-response receiver operating characteristic curve of MicroDMa is also higher than other methods when models are performed on INbreast. CONCLUSION: MicroDMa network is better than other methods and it can effectively help radiologists detect and identify two types of microcalcifications in clinical applications.

A novel machine learning approach on texture analysis for automatic breast microcalcification diagnosis classification of mammogram images.

PURPOSE: Screening programs use mammography as a diagnostic tool for the early detection of breast cancer. Mammogram enhancement is used to increase the local contrast of the mammogram so that the lesions are more visible in the advanced image. For accurate diagnosis in the early stage of breast cancer, the appearance of masses and microcalcification on the mammographic image are two important indicators. The objective of this study was to evaluate the feasibility of the automatic separation of images of breast tissue microcalcifications and also to evaluate its accuracy. METHODS: The research was carried out by using two techniques of image enhancement and highlighting of breast tissue microcalcifications for the desired areas by regional ROI based on fuzzy system and also Gabor filtering method. After determining the clusters of breast tissue microcalcifications, the clusters are classified using the decision tree classification algorithm. Then, for segmentation, samples suspected of microcalcification are highlighted and masked, and in the last stage, tissue characteristics are extracted. Subsequently, with the help of an artificial neural network (ANN), determining the benign and malignant types of segmented ROI clusters was accomplished. The proposed system is trained with a Digital Database for Screening Mammography (DDSM) developed by the University of South Florida, USA, and the simulations are performed under MATLAB software and the results are compared with previous work. RESULTS: The results of this training performed under this work show an accuracy of 93% and an improvement of sensitivity above 95%. CONCLUSION: The result indicates that the proposed approach can be applied to ensure breast cancer diagnosis.

Breast Microcalcifications as the Only Imaging Manifestation of Metastatic Serous Peritoneal Adenocarcinoma in the Breast.

We present a case of a 65 year old female with newly diagnosed primary peritoneal serous carcinoma who was found to have indeterminate segmental microcalcifications in the right upper outer quadrant with a mildly enlarged right axillary node on mammogram. There was no associated breast mass on ultrasound. Core biopsy of the right axillary lymph node and right upper outer quadrant breast microcalcifications confirmed the presence of breast metastases at both sites from primary peritoneal serous carcinoma. This case highlights the importance of histopathological correlation of any breast and axillary abnormalities in patient with primary extramammary malignancy. Imaging features of metastatic lesions to the breast are also reviewed.

A comparison of diagnostic performance of vacuum-assisted biopsy and core needle biopsy for breast microcalcification: a systematic review and meta-analysis.

BACKGROUND: Core needle biopsy (CNB) and vacuum-assisted biopsy (VAB) are both popularly used breast percutaneous biopsies. Both of them have become reliable alternatives to open surgical biopsy (OSB) for breast microcalcification (BM). AIMS: It is controversial that which biopsy method is more accurate and safer for BM. Hence, we conducted this meta-analysis to compare the diagnostic performance between CNB and VAB for BM, aiming to find out the better method. METHODS: Articles according with including and excluding criteria were collected from the databases, PubMed, Embase, and the Cochrane Library. Preset outcomes were abstracted and pooled to find out the potential advantages in CNB or VAB. RESULTS: Seven studies were identified and entered final meta-analysis from initially found 138 studies. The rate of ductal carcinoma in situ (DCIS) underestimation was significantly lower in VAB than CNB group [risk ratio (RR) = 1.83, 95% confidence interval (CI) 1.40 to 2.40, p < 0.001]. The microcalcification retrieval rate was significantly higher in VAB than CNB group (RR = 0.89, 95% CI 0.81 to 0.98, p = 0.02), while CNB owned a significantly lower complication rate than VAB (RR = 0.18, 95% CI 0.03 to 0.93, p = 0.04). The atypical ductal hyperplasia (ADH) underestimation rates were not compared for the limited number of studies reporting this outcome. CONCLUSIONS: Compared with CNB, VAB shows better diagnostic performance in DCIS underestimation rate and microcalcification retrieval rate. However, CNB shows a significantly lower complication rate. More studies are needed to verify these findings.

Visualization of Breast Microcalcifications on Digital Breast Tomosynthesis and 2-Dimensional Digital Mammography Using Specimens.

PURPOSE: The purpose of this study is to compare the visibility of microcalcifications of digital breast tomosynthesis (DBT) and full-field digital mammography (FFDM) using breast specimens. MATERIALS AND METHODS: Thirty-one specimens' DBT and FFDM were retrospectively reviewed by four readers. RESULTS: The image quality of microcalcifications of DBT was rated as superior or equivalent in 71.0% by reader 1, 67.8% by reader 2, 64.5% by reader 3, and 80.6% by reader 4. The Fleiss kappa statistic for agreement among readers was 0.31. CONCLUSIONS: We suggest that image quality of DBT appears to be comparable with or better than FFDM in terms of revealing microcalcifications.