The importance of early detection of calcifications associated with breast cancer in screening

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.

Assessing the malignancy of suspicious breast microcalcifications: the role of contrast enhanced mammography

PURPOSE

To assess the role of contrast-enhanced mammography (CEM) in predicting the malignancy of breast calcifications.

MATERIAL AND METHODS

We retrospectively evaluated patients with suspicious calcifications (BIRADS 4) who underwent CEM and stereotactic vacuum-assisted biopsy (VAB) at our institution. We assessed the sensitivity (SE), specificity (SP), positive predictive value (PPV) and negative predictive value (NPV) of CEM in predicting malignancy of microcalcifications with a 95% confidence interval; we performed an overall analysis and a subgroup analysis stratified into group A-low risk (BIRADS 4a) and group B-medium/high risk (BIRADS 4b-4c). We then evaluated the correlation between enhancement and tumour proliferation index (Ki-67) for all malignant lesions.

RESULTS

Data from 182 patients with 184 lesions were collected. Overall the SE of CEM in predicting the malignancy of microcalcifications was 0.70, SP was 0.85, the PPV was 0.82, the NPV was 0.76 and AUC was 0.78. SE in group A was 0.89, SP was 0.89, PPV was 0.57, NPV was 0.98 and AUC was 0.75. SE in group B was 0.68, SP was 0.80, PPV was 0.87, NPV was 0.57 and AUC was 0.75. Among malignant microcalcifications that showed enhancement (N = 52), 61.5% had Ki-67 ≥ 20% and 38.5% had low Ki-67 values. Among the lesions that did not show enhancement (N = 22), 90.9% had Ki-67 < 20% and 9.1% showed high Ki-67 values 20%.

CONCLUSIONS

The absence of enhancement can be used as an indicative parameter for the absence of disease in cases of low-suspicious microcalcifications, but not in intermediate-high suspicious ones for which biopsy remains mandatory and can be used to distinguish indolent lesions from more aggressive neoplasms, with consequent reduction of overdiagnosis and overtreatment.

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.

Impact of super-resolution and image acquisition on the detection of calcifications in digital breast tomosynthesis

OBJECTIVES

A virtual clinical trial (VCT) method is proposed to determine the limit of calcification detection in tomosynthesis.

METHODS

Breast anatomy, focal findings, image acquisition, and interpretation (n = 14 readers) were simulated using screening data (n = 660 patients). Calcifications (0.2-0.4 mm3) were inserted into virtual breast phantoms. Digital breast tomosynthesis (DBT) acquisitions were simulated assuming various acquisition geometries: source motion (continuous and step-and-shoot), detector element size (140 and 70 µm), and reconstructed voxel size (35-140 µm). VCT results were estimated using multiple-reader multiple-case analyses and d' statistics. Signal-to-noise (SNR) analyses were also performed using BR3D phantoms.

RESULTS

Source motion and reconstructed voxel size demonstrated significant changes in the performance of imaging systems. Acquisition geometries that use 70 µm reconstruction voxel size and step-and-shoot motion significantly improved calcification detection. Comparing 70 with 100 µm reconstructed voxel size for step-and-shoot, the ΔAUC was 0.0558 (0.0647) and d' ratio was 1.27 (1.29) for 140 µm (70 µm) detector element size. Comparing step-and-shoot with a continuous motion for a 70 µm reconstructed voxel size, the ΔAUC was 0.0863 (0.0434) and the d' ratio was 1.40 (1.19) for 140 µm (70 µm) detector element. Small detector element sizes (e.g., 70 µm) did not significantly improve detection. The SNR results with the BR3D phantom show that calcification detection is dependent upon reconstructed voxel size and detector element size, supporting VCT results with comparable agreement (ratios: d' = 1.16 ± 0.11, SNR = 1.34 ± 0.13).

CONCLUSION

DBT acquisition geometries that use super-resolution (smaller reconstructed voxels than the detector element size) combined with step-and-shoot motion have the potential to improve the detection of calcifications.

CLINICAL RELEVANCE

Calcifications may not always be discernable in tomosynthesis because of differences in acquisition and reconstruction methods. VCTs can identify strategies to optimize acquisition and reconstruction parameters for calcification detection in tomosynthesis, most notably through super-resolution in the reconstruction.

KEY POINTS

• Super-resolution improves calcification detection and SNR in tomosynthesis; specifically, with the use of smaller reconstruction voxels. • Calcification detection using step-and-shoot motion is superior to that using continuous tube motion. • A detector element size of 70 µm does not provide better detection than 140 µm for small calcifications at the threshold of detectability.

Value of mammographic microcalcifications and MRI-enhanced lesions in the evaluation of residual disease after neoadjuvant therapy for breast cancer

Background

Microcalcifications persist even if a patient with breast cancer achieves pathologic complete response (pCR) as confirmed by surgery after neoadjuvant treatment (NAT). In practice, surgeons tend to remove all the microcalcifications. This study aimed to explore the correlation between changes in the extent of microcalcification after NAT and pathological tumor response and compare the accuracy of mammography (MG) and magnetic resonance imaging (MRI) in predicting the size of residual tumors.

Methods

This was a retrospective study which included a consecutive series of patients in Guangdong Provincial People's Hospital. Between January 2010 and January 2020, 127 patients with breast cancer and Breast Imaging Reporting and Data System (BI-RADS) 4-5 microcalcifications were included in this study. The maximum diameter of the microcalcifications on MG and lesion enhancement on MRI pre- and post-NAT were measured. The correlations between the changes in residual microcalcifications on MG and pCR were analyzed. Intraclass correlation coefficients (ICCs) were computed between the extent of the residual microcalcifications, residual enhancement, and residual tumor size.

Results

There were no statistically significant differences in the changes in microcalcifications after NAT according to the RECIST criteria on MRI (P=0.09) and Miller-Payne grade (P=0.14). MRI showed a higher agreement than did residual microcalcifications on MG in predicting residual tumor size (ICC: 0.771 vs. 0.097).

Conclusions

MRI is more accurate for evaluating residual tumor size in breast cancer. In our study, the extent of microcalcifications on MG after NAT had nearly no correlation with the pathological size of the residual tumor. Therefore, residual tumors with microcalcifications may not necessarily be a contraindication to breast-conserving surgery.

Detectability of category 3 or higher microcalcifications on digital mammograms: a comparative study between 5-MP color and monochrome liquid crystal display monitors

This study explored the detectability of category 3 or higher microcalcifications using 5-MP color and monochrome monitors. Contrast detail mammography phantom with polymethyl methacrylate (PMMA) images were observed in color and monochrome by five radiographers, and the image quality figures (IQF) were calculated based on the gold disc locations identified. Five radiographers and two radiologists observed 200 mammograms from 100 patients (including 36 with microcalcifications) and rated the microcalcifications. The results were analyzed using area under the curve (AUC) and jackknife resampling. A paired t test was used for statistical analysis (p < 0.05). The mean IQF of color and monochrome monitors were 10.73 and 10.49 (30 mm PMMA, p = 0.653) and 8.47 and 8.74 (50 mm PMMA, p = 0.774), respectively. The mean AUC of color and monochrome monitors were 0.917 and 0.936 (p = 0.335), respectively, with windowing and magnification. The detectability of microcalcifications was not significantly different between the monitors.

Addressing Misinformation About the Canadian Breast Screening Guidelines

Screening mammography has been shown to reduce breast cancer mortality by 41% in screened women ages 40-69 years. There is misinformation about breast screening and the Canadian breast screening guidelines. This can decrease confidence in screening mammography and can lead to suboptimal recommendations. We review some of this misinformation to help radiologists and referring physicians navigate the varied international and provincial guidelines. We address the ages to start and stop breast screening. We explore how these recommendations may vary for specific populations such as patients who are at increased risk, transgender patients and minorities. We identify who would benefit from supplemental screening and review the available supplemental screening modalities including ultrasound, MRI, contrast-enhanced mammography and others. We describe emerging technologies including the potential use of artificial intelligence for breast screening. We provide background on why screening policies vary across the country between provinces and territories. This review is intended to help radiologists and referring physicians understand and navigate the varied international and provincial recommendations and guidelines and make the best recommendations for their patients.

Calcinosis universalis and breast cancer: A distinctive association

Calcinosis universalis (CU) is characterised by diffuse deposition of insoluble calcium salt in the skin, subcutaneous tissue or organs. Calcium deposits in the breast may be associated with an increased risk for developing breast cancer. We present a case of a 65-year-old woman diagnosed with CU secondary to undifferentiated connective tissue disease. She developed progressive calcification of her skin, which did not improve with oral medications aimed at reducing the calcification. Investigations to look for possible causes of calcification were all unremarkable. During follow-up, calcification was also found in both her breasts. Initial mammography was reported as fibroadenoma. However, 3 years later, she returned with metastatic breast cancer which presented with a massive pleural effusion of the right lung. Calcinosis universalis should now be considered as a risk factor for breast cancer.

Microcalcification Discrimination in Mammography Using Deep Convolutional Neural Network: Towards Rapid and Early Breast Cancer Diagnosis

Breast cancer is among the most common types of cancer in women and under the cases of misdiagnosed, or delayed in treatment, the mortality risk is high. The existence of breast microcalcifications is common in breast cancer patients and they are an effective indicator for early sign of breast cancer. However, microcalcifications are often missed and wrongly classified during screening due to their small sizes and indirect scattering in mammogram images. Motivated by this issue, this project proposes an adaptive transfer learning deep convolutional neural network in segmenting breast mammogram images with calcifications cases for early breast cancer diagnosis and intervention. Mammogram images of breast microcalcifications are utilized to train several deep neural network models and their performance is compared. Image filtering of the region of interest images was conducted to remove possible artifacts and noises to enhance the quality of the images before the training. Different hyperparameters such as epoch, batch size, etc were tuned to obtain the best possible result. In addition, the performance of the proposed fine-tuned hyperparameter of ResNet50 is compared with another state-of-the-art machine learning network such as ResNet34, VGG16, and AlexNet. Confusion matrices were utilized for comparison. The result from this study shows that the proposed ResNet50 achieves the highest accuracy with a value of 97.58%, followed by ResNet34 of 97.35%, VGG16 96.97%, and finally AlexNet of 83.06%.

Comprehensive quantitative malignant risk prediction of pure grouped amorphous calcifications: clinico-mammographic nomogram

BACKGROUND

Pure grouped amorphous calcifications are classified as Breast Imaging Reporting and Data System (BI-RADS) category 4B suspicious calcifications and recommended for biopsy. However, the biopsies often reveal benign findings, especially in screening mammograms (92.4-97.2%).

METHODS

Mammograms of 699 pure grouped amorphous calcifications with final pathological results were analyzed in this retrospective study. The maximum span (MS) of the group of calcifications and the MS of the parallel/vertical direction of the mammary duct (MPS/MVS) were measured, and the MPS to MVS ratio was calculated. Based on the MS and ratio, 2 prediction nomograms with other clinic-mammographic features were developed. The discrimination performance of the models was assessed and compared by the area under the receiver operating characteristic curve (AUC). Scatterplots were created to determine the cutoff values with fewer misdiagnoses of malignant calcifications and fewer false positives.

RESULTS

Ultimately, 2 prediction models were successfully developed based on the 4 risk factors of age, purpose of the mammogram, whether multiple or single calcifications, and the MS [odds ratio (OR) =1.06, P=0.02]/ratio (OR =6.05, P<0.001). Both models had good discrimination. The ratio model performed better than the MS model in the training cohort (AUC of 0.875 and 0.834, respectively, P=0.003) and validation cohort (AUC 0.908 and 0.867, respectively, P=0.047). For the group with probably benign calcifications (as detected by the ratio nomogram), the malignancy rates were 2.7% [95% confidence interval (CI): 1.00% to 6.53%] and 1.19% (95% CI: 0.06% to 7.37%) in the training and validation cohorts, respectively, and 44.12% and 47.70% of benign biopsies were detected in the training and validation cohorts, respectively.

CONCLUSIONS

The clinico-mammographic quantitative malignancy risk prediction nomogram showed favorable discrimination and calibration performance. The ratio model showed better diagnostic efficiency than the MS model, and identified >40% of benign biopsies.

Breast Density and Risk of Interval Cancers: The Effect of Annual Versus Biennial Screening Mammography Policies in Canada

Regular screening mammography reduces breast cancer mortality. However, in women with dense breasts, the performance of screening mammography is reduced, which is reflected in higher interval cancer rates (ICR). In Canada, population-based screening mammography programs generally screen women biennially; however, some provinces and territories offer annual mammography for women with dense breast tissue routinely and/or on recommendation of the radiologist. This study compared the ICRs in those breast screening programs with a policy of annual vs. those with biennial screening for women with dense breasts. Among 148,575 women with dense breasts screened between 2008 to 2010, there were 288 invasive interval breast cancers; screening programs with policies offering annual screening for women with dense breasts had fewer interval cancers 63/70,814 (ICR 0.89/1000, 95% CI: 0.67-1.11) compared with those with policies of usual biennial screening 225/77,761 (ICR 1.45 /1000 (annualized), 95% CI: 1.19-1.72) i.e. 63% higher (p = 0.0016). In screening programs where radiologists' screening recommendations were able to be analyzed, a total of 76,103 women were screened, with 87 interval cancers; the ICR was lower for recommended annual (65/69,650, ICR 0.93/1000, 95% CI: 0.71, 1.16) versus recommended biennial screening (22/6,453, ICR 1.70/1000 (annualized), 95%CI: 0.70, 2.71)(p = 0.0605). Screening program policies of annual as compared with biennial screening in women with dense breasts had the greatest impact on reducing interval cancer rates. We review our results in the context of current dense breast notification in Canada.

Arylphosphonate-Tethered Porphyrins: Fluorescence Silencing Speaks a Metal Language in Living Enterocytes*

We report the application of a highly versatile and engineerable novel sensor platform to monitor biologically significant and toxic metal ions in live human Caco-2 enterocytes. The extended conjugation between the fluorescent porphyrin core and metal ions through aromatic phenylphosphonic acid tethers generates a unique turn off and turn on fluorescence and, in addition, shifts in absorption and emission spectra for zinc, cobalt, cadmium and mercury. The reported fluorescent probes p-H8 TPPA and m-H8 TPPA can monitor a wide range of metal ion concentrations via fluorescence titration and also via fluorescence decay curves. Cu- and Zn-induced turn off fluorescence can be differentially reversed by the addition of common chelators. Both p-H8 TPPA and m-H8 TPPA readily pass the mammalian cellular membrane due to their amphipathic character as confirmed by confocal microscopic imaging of living enterocytes.

Classification of Mammographic Breast Microcalcifications Using a Deep Convolutional Neural Network: A BI-RADS-Based Approach

MATERIALS AND METHODS

Over 56,000 images of 268 mammograms from 94 patients were labeled to 3 classes according to the BI-RADS standard: "no microcalcifications" (BI-RADS 1), "probably benign microcalcifications" (BI-RADS 2/3), and "suspicious microcalcifications" (BI-RADS 4/5). Using the preprocessed images, a dCNN was trained and validated, generating 3 types of models: BI-RADS 4 cohort, BI-RADS 5 cohort, and BI-RADS 4 + 5 cohort. For the final validation of the trained dCNN models, a test data set consisting of 141 images of 51 mammograms from 26 patients labeled according to the corresponding BI-RADS classification from the radiological reports was applied. The performances of the dCNN models were evaluated, classifying each of the mammograms and computing the accuracy in comparison to the classification from the radiological reports. For visualization, probability maps of the classification were generated.

RESULTS

The accuracy on the validation set after 130 epochs was 99.5% for the BI-RADS 4 cohort, 99.6% for the BI-RADS 5 cohort, and 98.1% for the BI-RADS 4 + 5 cohort. Confusion matrices of the "real-world" test data set for the 3 cohorts were generated where the radiological reports served as ground truth. The resulting accuracy was 39.0% for the BI-RADS 4 cohort, 80.9% for BI-RADS 5 cohort, and 76.6% for BI-RADS 4 + 5 cohort. The probability maps exhibited excellent image quality with correct classification of microcalcification distribution.

CONCLUSIONS

The dCNNs can be trained to successfully classify microcalcifications on mammograms according to the BI-RADS classification system in order to act as a standardized quality control tool providing the expertise of a team of radiologists.

Dying To Be Men: Masculinity and Early Cancer Detection Among Nigerian Men

Cancer incidence and mortality in Nigeria are increasing at an alarming rate, especially among Nigerian men. Despite the numerous public health campaigns and education on the importance of early cancer detection in Nigeria, there exist high rate of fatal/advanced stage cancer diagnoses among Nigerian men, even among affluent Nigerian men. However, there is limited information on patterns of cancer screening and psychosocial predictors of early cancer detection behaviors among Nigerian men. In this cross-sectional study, we examined demographic and psychosocial factors influencing early cancer detection behaviors among Nigerian men. Participants (N = 143; Mage = 44.73) responded to survey assessing: masculinity, attachment styles, current and future cancer detection behaviors, and sociodemographic characteristics. We found that among the participants studied, education, masculinity and anxious attachment were significantly associated with current cancer detection behaviors. Additionally, education and anxious attachment were significantly associated with future cancer detection behaviors. Our finding is best served for clinicians and public health professionals, especially those in the field of oncology in Sub-Saharan Africa. Also, the study may be used as a groundwork for future research and health intervention programs targeting men in Sub-Saharan Africa.

Reduction of scatter in breast CT yields improved microcalcification visibility

The inadequate visibility of microcalcifications-small calcium deposits that cue radiologists to early stages of cancer-is a major limitation in current designs of dedicated breast computed tomography (bCT). This limitation has previously been attributed to the constituent components, spatial resolution, and utilized dose. Scattered radiation has been considered an occurrence with low-frequency impacts that can be compensated for in post-processing. We hypothesized, however, that the acquisition of scattered radiation has a far more detrimental impact on clinically relevant features than has previously been understood. Critically, acquisition of scatter leads to the reduced visibility of microcalcifications. This hypothesis was investigated and supported via mathematical derivations and simulation studies. We conducted a series of comparative studies in which four bCT systems were simulated under iso-dose and iso-resolution conditions, characterizing the dependencies of microcalcification contrast on accumulated scatter. Included among the simulated systems is a novel bCT design-narrow beam bCT (NB-bCT)-that captures nearly zero scatter. We find that current bCT systems suffer from significant levels of scatter. As validated in theory, depending on the system and size of microcalcifications, between 25% and over 70% of contrast resolution is lost due to scatter. The results in NB-bCT, however, provide evidence that by removing scatter build-up in projections, the contrast of microcalcifications in a bCT image is preserved, regardless of their size or location in the breast.

Fluorescent Arylphosphonic Acids: Synergic Interactions between Bone and the Fluorescent Core

Herein, we report the third generation of fluorescent probes (arylphosphonic acids) to target calcifications, particularly hydroxyapatite (HAP). In this study, we use highly conjugated porphyrin-based arylphosphonic acids and their diesters, namely 5,10,15,20-tetrakis[m-(diethoxyphosphoryl)phenyl]porphyrin (m-H8 TPPA-OEt8 ) and 5,10,15,20-tetrakis [m-phenylphosphonic acid]porphyrin (m-H8 TPPA), in comparison with their positional isomers 5,10,15,20-tetrakis[p-(diisopropoxyphosphoryl)phenyl]porphyrin (p-H8 TPPA-iPr8 ) and 5,10,15,20-tetrakis [p-phenylphosphonic acid]porphyrin (p-H8 TPPA), which have phosphonic acid units bonded to sp2 carbon atoms of the fluorescent core. The conjugation of the fluorescent core is thus extended to the (HAP) through sp2 -bonded -PO3 H2 units, which generates increased fluorescence upon HAP binding. The resulting fluorescent probes are highly sensitive towards the HAP in rat bone sections. The designed probes are readily taken up by cells. Due to the lower reported toxicity of (p-H8 TPPA), these probes could find applications in monitoring bone resorption or adsorption, or imaging vascular or soft tissue calcifications for breast cancer diagnosis etc.

Missed Breast Cancer: Effects of Subconscious Bias and Lesion Characteristics

Medical errors are a substantial cause of morbidity and mortality and the third leading cause of death in the United States. Errors resulting in missed breast cancer are the most common reason for medical malpractice lawsuits against all physicians. Missed breast cancers are breast malignancies that are detectable at retrospective review of a previously obtained mammogram that was prospectively reported as showing negative, benign, or probably benign findings. Investigators in prior studies have found that up to 35% of both interval cancers and screen-detected cancers could be classified as missed. As such, in conjunction with having awareness of the most common misleading appearances of breast cancer, it is important to understand the cognitive processes and unconscious biases that can impact image interpretation, thereby helping to decrease the number of missed breast cancers. The various cognitive processes that lead to unconscious bias in breast imaging, such as satisfaction of search, inattention blindness, hindsight, anchoring, premature closing, and satisfaction of reporting, are outlined in this pictorial review of missed breast cancers. In addition, strategies for reducing the rates of these missed cancers are highlighted. The most commonly missed and misinterpreted lesions, including stable lesions, benign-appearing masses, one-view findings, developing asymmetries, subtle calcifications, and architectural distortion, also are reviewed. This information will help illustrate why and how breast cancers are missed and aid in the development of appropriate minimization strategies in breast imaging. ^©RSNA, 2020.

DNA methylation landscape of triple-negative ductal carcinoma in situ (DCIS) progressing to the invasive stage in canine breast cancer

Triple-negative breast cancer (TNBC) is a subtype of breast cancer unresponsive to traditional receptor-targeted treatments, leading to a disproportionate number of deaths. Invasive breast cancer is believed to evolve from non-invasive ductal carcinoma in situ (DCIS). Detection of triple-negative DCIS (TN-DCIS) is challenging, therefore strategies to study molecular events governing progression of pre-invasive TN-DCIS to invasive TNBC are needed. Here, we study a canine TN-DCIS progression and investigate the DNA methylation landscape of normal breast tissue, atypical ductal hyperplasia (ADH), DCIS and invasive breast cancer. We report hypo- and hypermethylation of genes within functional categories related to cancer such as transcriptional regulation, apoptosis, signal transduction, and cell migration. DNA methylation changes associated with cancer-related genes become more pronounced at invasive breast cancer stage. Importantly, we identify invasive-only and DCIS-specific DNA methylation alterations that could potentially determine which lesions progress to invasive cancer and which could remain as pre-invasive DCIS. Changes in DNA methylation during TN-DCIS progression in this canine model correspond with gene expression patterns in human breast tissues. This study provides evidence for utilizing methylation status of gene candidates to define late-stage (DCIS and invasive), invasive stage only or DCIS stage only of TN-DCIS progression.

Classification of Microcalcification Clusters in Digital Mammograms Using a Stack Generalization Based Classifier

This paper presents a machine learning based approach for the discrimination of malignant and benign microcalcification (MC) clusters in digital mammograms. A series of morphological operations was carried out to facilitate the feature extraction from segmented microcalcification. A combination of morphological, texture, and distribution features from individual MC components and MC clusters were extracted and a correlation-based feature selection technique was used. The clinical relevance of the selected features is discussed. The proposed method was evaluated using three different databases: Optimam Mammography Image Database (OMI-DB), Digital Database for Screening Mammography (DDSM), and Mammographic Image Analysis Society (MIAS) database. The best classification accuracy (95.00±0.57%) was achieved for OPTIMAM using a stack generalization classifier with 10-fold cross validation obtaining an Az value equal to 0.97±0.01.

Multiscale connected chain topological modelling for microcalcification classification

Computer-aided diagnosis (CAD) systems can be employed to help classify mammographic microcalcification clusters. In this paper, a novel method for the classification of the microcalcification clusters based on topology/connectivity has been introduced. The proposed method is distinct from existing techniques which concentrate on morphology and texture of microcalcifications and surrounding tissue. The proposed approach used multiscale morphological relationship of connectivity between microcalcifications where connected chains between nearest microcalcifications were generated at each scale. Subsequently, graph connectivity features at each scale were extracted to estimate the topological connectivity structure of microcalcification clusters for benign versus malignant classification. The proposed approach was evaluated using publicly available digitized datasets: MIAS and DDSM, in addition to the digital OPTIMAM dataset. The classification of features using KNN obtained a classification accuracy of 86.47±1.30%, 90.0±0.00%, 82.5±2.63%, 76.75±0.66% for the DDSM, MIAS-manual, MIAS-auto and OPTIMAM datasets respectively. The study showed that topological/connectivity modelling using a multiscale approach was appropriate for microcalcification cluster analysis and classification; topological connectivity and distribution can be linked to clinical understanding of microcalcification spatial distribution.

Microcalcification detection in full-field digital mammograms: A fully automated computer-aided system

BACKGROUND

Microcalcification clusters in mammograms can be considered as early signs of breast cancer. However, their detection is a very challenging task because of different factors: large variety of breast composition, highly textured breast anatomy, impalpable size of microcalcifications in some cases, as well as inherent low contrast of mammograms. Thus, the need to support the clinicians' work with an automatic tool.

METHODS

In this work a three-phases approach for clustered microcalcification detection is presented. Specifically, it is made up of a pre-processing step, aimed at highlighting potentially interesting breast structures, followed by a single microcalcification detection step, based on Hough transform, that is able to grasp the innate characteristic shape of the structures of interest. Finally, a cluster identification step to group microcalcifications is carried out by means of a clustering algorithm able to codify expert domain rules.

RESULTS

The detection performance of the proposed method has been evaluated on 364 mammograms of 182 patients obtaining a true positive ratio of 91.78% with 2.87 false positives per image.

CONCLUSIONS

Experimental results demonstrated that the proposed method is able to detect microcalcification clusters in digital mammograms showing performance comparable to different methodologies exploited in the state-of-art approaches, with the advantage that it does not require any training phase and a large set of data. The performance of the proposed approach remains high even for more difficult clinical cases of mammograms of young women having high-density breast tissue thus resulting in a reduced contrast between microcalcifications and surrounding dense tissues.